2011Siegal-GaskinsEtal11(EmergenceOfSwitchLikeBehaviorInALargeFamilyOfSimpleBiochemicalNetworks)[Pre

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Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks Dan Siegal-Gaskins 1,2 *, Maria Katherine Mejia-Guerra 2 , Gregory D. Smith 3 , Erich Grotewold 2 1 Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio, United States of America, 2 Department of Molecular Genetics and Plant Biotechnology Center, The Ohio State University, Columbus, Ohio, United States of America, 3 Department of Applied Science, The College of William and Mary, Williamsburg, Virginia, United States of America Abstract Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes). We find that there exist reaction rate constants leading to bistability in ,90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two- gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion. Citation: Siegal-Gaskins D, Mejia-Guerra MK, Smith GD, Grotewold E (2011) Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks. PLoS Comput Biol 7(5): e1002039. doi:10.1371/journal.pcbi.1002039 Editor: JO ¨ rg Stelling, ETH Zurich, Switzerland Received November 23, 2010; Accepted March 21, 2011; Published May 12, 2011 Copyright: ß 2011 Siegal-Gaskins et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by NRI Grant 2007-35318-17805 from the USDA CSREES, DOE Grant DE-FG02-07ER15881 and NSF grant DBI-0701405 to EG, NSF Grant DMS-0443843 to GDS, and an NIH T32 training grant from the Division of Human Cancer Genetics to DSG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction Bistability–the coexistence of two stable equilibria in a dynamical system–is responsible for the switch-like behavior seen in a wide variety of cell biological networks, such as those involved in signal transduction [1], cell fate specification [2–4], cell cycle regulation [5], apoptosis [6–8], and in regulating extracellular DNA uptake (competence development) [9]. Evidence for bistable networks has been found in experimental observations of the hysteretic (i.e., history dependent) response to stimuli that is commonly associated with bistability [10,11], for example in the Cdc2 activation circuit in Xenopus egg extracts [12,13] and in the lactose utilization network in E. coli [14]. Complementing experimental analyses, mathematical tools such as bifurcation theory can be used to determine if a particular network–written as a set of ordinary differential equations (ODEs) –is bistable [15]. However, because the dynamical behavior of a network is dependent on the values of the system parameters (e.g., reaction rates), and the number of parameters required for an accurate description of even simple systems is typically large and uncertain, new bistable circuit architectures tend to be identified only slowly and on a network-by-network basis. Chemical reaction network theory (CRNT), which gives conditions for the existence, multiplicity, and stability of steady states in systems of nonlinear ODEs derived from mass-action kinetics [16–18], offers a novel framework for the rapid identification of network topologies with the capacity for bistability (herein referred to as bistable networks). Importantly, CRNT is applicable without specific knowledge of the system parameters. This ability to study network characteristics in a parameter-free context is particularly beneficial in cell and developmental biology, given the high level of uncertainty in parameter values [19]. As a result, CRNT has found a number of biological applications [20– 23]. Still, considering its potential for large-scale analyses, the use of CRNT has been fairly limited. Here, we apply CRNT to reaction network models representing a broad class of small gene regulatory networks (GRNs): those consisting of two transcription factor (TF)-coding genes and their associated proteins. Our comprehensive parameter-free survey resulted in the identification of 36,771 bistable GRN architectures (out of a total of 40,680), including eleven without the TF cooperativity typically associated with switch-like circuits. Approx- imately 40% of the bistable systems were confirmed as such using existing computational tools, with the remainder identified PLoS Computational Biology | www.ploscompbiol.org 1 May 2011 | Volume 7 | Issue 5 | e1002039

description

Introduction PLoSComputationalBiology|www.ploscompbiol.org 1 May2011|Volume7 |Issue5 | e1002039 Abstract 1MathematicalBiosciencesInstitute,TheOhioStateUniversity,Columbus,Ohio,UnitedStatesofAmerica,2DepartmentofMolecularGeneticsandPlantBiotechnology Center,TheOhioStateUniversity,Columbus,Ohio,UnitedStatesofAmerica,3DepartmentofAppliedScience,TheCollegeofWilliamandMary,Williamsburg,Virginia, UnitedStatesofAmerica

Transcript of 2011Siegal-GaskinsEtal11(EmergenceOfSwitchLikeBehaviorInALargeFamilyOfSimpleBiochemicalNetworks)[Pre

Emergence of Switch-Like Behavior in a Large Family ofSimple Biochemical NetworksDan Siegal-Gaskins1,2*, Maria Katherine Mejia-Guerra2, Gregory D. Smith3, Erich Grotewold2

1Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio, United States of America, 2Department of Molecular Genetics and Plant Biotechnology

Center, The Ohio State University, Columbus, Ohio, United States of America, 3Department of Applied Science, The College of William and Mary, Williamsburg, Virginia,

United States of America

Abstract

Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions,including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibitbistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number ofcomponents. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silicosurvey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by twotranscription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes).We find that there exist reaction rate constants leading to bistability in ,90% of these GRN models, including severalcircuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority ofwhich could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two-gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistablenetworks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number ofpreviously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises withrelative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application ofCRNT to bistable network identification in real biological systems, we integrated publicly available protein-proteininteraction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNspredicted to behave in a bistable fashion.

Citation: Siegal-Gaskins D, Mejia-Guerra MK, Smith GD, Grotewold E (2011) Emergence of Switch-Like Behavior in a Large Family of Simple BiochemicalNetworks. PLoS Comput Biol 7(5): e1002039. doi:10.1371/journal.pcbi.1002039

Editor: JOrg Stelling, ETH Zurich, Switzerland

Received November 23, 2010; Accepted March 21, 2011; Published May 12, 2011

Copyright: ! 2011 Siegal-Gaskins et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by NRI Grant 2007-35318-17805 from the USDA CSREES, DOE Grant DE-FG02-07ER15881 and NSF grant DBI-0701405 to EG,NSF Grant DMS-0443843 to GDS, and an NIH T32 training grant from the Division of Human Cancer Genetics to DSG. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

Bistability–the coexistence of two stable equilibria in adynamical system–is responsible for the switch-like behavior seenin a wide variety of cell biological networks, such as those involvedin signal transduction [1], cell fate specification [2–4], cell cycleregulation [5], apoptosis [6–8], and in regulating extracellularDNA uptake (competence development) [9]. Evidence for bistablenetworks has been found in experimental observations of thehysteretic (i.e., history dependent) response to stimuli that iscommonly associated with bistability [10,11], for example in theCdc2 activation circuit in Xenopus egg extracts [12,13] and in thelactose utilization network in E. coli [14]. Complementingexperimental analyses, mathematical tools such as bifurcationtheory can be used to determine if a particular network–written asa set of ordinary differential equations (ODEs) –is bistable [15].However, because the dynamical behavior of a network isdependent on the values of the system parameters (e.g., reactionrates), and the number of parameters required for an accuratedescription of even simple systems is typically large and uncertain,new bistable circuit architectures tend to be identified only slowlyand on a network-by-network basis.

Chemical reaction network theory (CRNT), which givesconditions for the existence, multiplicity, and stability of steadystates in systems of nonlinear ODEs derived from mass-actionkinetics [16–18], offers a novel framework for the rapididentification of network topologies with the capacity for bistability(herein referred to as bistable networks). Importantly, CRNT isapplicable without specific knowledge of the system parameters.This ability to study network characteristics in a parameter-freecontext is particularly beneficial in cell and developmental biology,given the high level of uncertainty in parameter values [19]. As aresult, CRNT has found a number of biological applications [20–23]. Still, considering its potential for large-scale analyses, the useof CRNT has been fairly limited.Here, we apply CRNT to reaction network models representing

a broad class of small gene regulatory networks (GRNs): thoseconsisting of two transcription factor (TF)-coding genes and theirassociated proteins. Our comprehensive parameter-free surveyresulted in the identification of 36,771 bistable GRN architectures(out of a total of 40,680), including eleven without the TFcooperativity typically associated with switch-like circuits. Approx-imately 40% of the bistable systems were confirmed as such usingexisting computational tools, with the remainder identified

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through the novel concept of network ancestry, in which thepresence of a bistable subnetwork can under certain conditions beused to establish bistability in a larger network if the condition thatthe two network structures have an identical stoichiometricsubspace is met (see the following section on CRNT basics).Despite its large size, the entire two-gene bistable network familycan be understood as descended from a set of only eleven minimalbistable networks, that is, bistable networks that do not containwithin them a smaller bistable subnetwork and, as a consequence,are rendered monostable by the removal of one or more networkreactions. Using experimental protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomycescerevisiae, we demonstrate how a general theoretical survey of thiskind has unique predictive power to identify bistable modules inorganisms that have not been fully explored from a functionalgenomics perspective. Our results are further suggestive of a rolefor parameter-free modeling in simplifying the study of complexregulatory networks, understanding network evolution, anddesigning new synthetic biological circuits.

Results

Two-gene network constructionAs done previously [23], we assume classical chemical kinetics

and specify gene regulatory networks (GRNs) as sets of elementarybiochemical reactions. For a network consisting of N transcriptionfactor genes Xi and associated proteins Pi (i~1, . . . ,N), theessential reactions are basal protein production (Xi? XizPi) anddegradation (Pi ?1). Networks may also contain proteindimerization reactions (PizPj ' PiPj ), binding of both TFmonomers and dimers to the gene promoters (XizPj ' XiPj andXizPjPk ' XiPjPk), and protein production from a bound gene(XiPj ? XiPjzPi and XiPjPk ? XiPjPkzPi). For reactions ofthis last type, under our parameter-free framework, the rate ofprotein production from a bound gene is unspecified and thus maybe either higher or lower than the basal rate but cannot be zero.For simplicity, we assume that the promoter of each gene may onlybe bound by a single monomer or dimer species at any given time,

or they may remain unoccupied. We further assume that, whiledegradation is considered for monomeric TFs, all TF dimers arestable to proteolytic degradation; the validity of this assumptionand its implications are discussed below.A variety of networks may be constructed by combining these

reactions, subject to certain logical constraints (e.g., the presence ofa dimer-promoter binding reaction requires the inclusion of thedimer formation reaction) and with the requirement that everynetwork includes the necessary basal TF production anddegradation reactions. In the two-gene case (N =2), there are 4essential reactions and 23 additional reactions (Table 1) that maybe combined to form 40,680 different networks. The total numberof networks is smaller than might be expected (i.e., less than 223) asa result of reaction dependencies (Table 1) and networksymmetries; for example, the network consisting of reactions k, q,and w is functionally equivalent to the that with reactions i, l, andr, and as a result we do not include the latter and other symmetricnetworks like it in the total.It should be noted that within this set of two-gene networks

there are a small number for which there is no coupling betweenthe two genes. Given that there are twelve possible one-genenetworks for both X1/P1 and X2/P2 independently (see [23]), thetotal number of unique decoupled two-gene networks is 12(12+1)/2= 78, the number of distinct pairs of one-gene circuits. Thepresence of 78 decoupled two-gene networks was verified bysearching through the full list of 40,680 networks for those lackingthe basic coupling reactions b, c, j, n, and o (Table 1).

Chemical reaction network theory basicsGiven the centrality of CRNT to our analysis, we provide here a

primer on the relevant aspects of the theory and illustrate themwith the rudimentary two-gene network that consists of only theessential basal protein production and degradation reactions(Figure 1).At the heart of the theory is the concept of network complexes,

formally the chemical species or linear combinations of specieswhich occur on either side of a chemical equation. A reactionnetwork can be visualized as a directed graph where each of thesecomplexes appears only once at the heads and/or tails of reactionarrows. A collection of complexes connected by arrows is referredto as a linkage class. The complexes and linkage classes for ourrudimentary network are highlighted in Figure 1 in yellow andwith dashed lines, respectively.Every complex in the network can also be represented as a

vector in an appropriate vector space; in a network of N species,the complex vectors lie in RN . Reactions also have associatedvectors (termed reaction vectors), which are constructed by subtract-ing the reactant complex vectors from the product complexvectors. The size of the largest linearly independent set of reactionvectors is the rank of the network, and the set of all possible linearcombinations of reaction vectors (i.e., their span) is referred to asthe stoichiometric subspace of the network. This subspace plays animportant role in setting boundaries on the system behavior:although the species’ concentrations may evolve with time, theyare ultimately constrained within surfaces that are paralleltranslations of the stoichiometric subspace. Exactly which surface(or stoichiometric compatibility class) the concentrations are constrainedto is defined by the initial conditions.For a system with n complexes, l linkage classes, and rank s, the

network deficiency d is defined as d= n2l2s. A number of theoremsregarding the stability properties of networks are based on thedeficiency, including the deficiency zero and deficiency onetheorems [16,17].

Author Summary

Switch-like behavior is found across a wide range ofbiological systems, and as a result there is significantinterest in identifying the various ways in which biochem-ical reactions can be combined to yield a switch-likeresponse. In this work we use a set of mathematical toolsfrom chemical reaction network theory that provideinformation about the steady-states of a reaction networkirrespective of the values of network rate constants, toconduct a large computational study of a family of modelnetworks consisting of only two protein-coding genes. Wefind that a large majority of these networks (,90%) have(for some set of parameters) the mathematical propertyknown as bistability and can behave in a switch-likemanner. Interestingly, the capacity for switch-like behavioris often maintained as networks increase in size throughthe introduction of new reactions. We then demonstrateusing published yeast data how theoretical parameter-freesurveys such as this one can be used to discover possibleswitch-like circuits in real biological systems. Our resultshighlight the potential usefulness of parameter-freemodeling for the characterization of complex networksand to the study of network evolution, and are suggestiveof a role for it in the development of novel syntheticbiological switches.

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Advanced deficiency theory (ADT) [18] is required for networkswith a deficiency greater than one. The ADT algorithm, detailed

in [24] and implemented in the Chemical Reaction NetworkToolbox software package (http://www.chbmeng.ohio-state.edu/,feinberg/crntwin/), constructs and attempts to solve systems ofequalities and inequalities that are based on the network structure.If no solutions (which together with the equality and inequalitysystems are known as ‘signatures’ of the reaction network) can befound, then the network does not have the qualitative capacity tosupport multiple steady states. However, if signatures can befound, then the network can support multiple steady states, andthe Toolbox will produce example rate constants and associatedsteady states consistent with the mass-action ODE description ofthe network, as well as report the stability characteristics of thesteady states. It should be emphasized that ADT cannot guaranteebistability even if the network does support multiple steady states,as they may be unstable. Nevertheless, with its substantialanalytical power and ease of use, ADT has played a role in anumber of recent studies [23,25–28].

Preliminary bistable network identificationAll of the two-gene networks modeled were found by the

Chemical Reaction Network Toolbox (herein referred to as simplythe Toolbox) to have a deficiency of two or more, necessitating theuse of ADT in their analyses. Screening the Toolbox-generatedADT analysis reports, we determined that of the 40,680 networks

Table 1. Reactions combined to generate the 40,680 unique networks of two genes and two gene products.

Reaction label ri Reaction Dependencies Biochemical process

* X1 R X1+P1 – gene X1 basal protein production

* X2 R X2+P2 – gene X2 basal protein production

* P1 R 0/ – protein P1 degradation

* P2 R 0/ – protein P2 degradation

a X1+P1 ( X1 P1 – binding of P1 to the X1 promoter

b X1+P2 ( X1 P2 – binding of P2 to the X1 promoter

c X2+P1 ( X2 P1 – binding of P1 to the X2 promoter

d X2+P2 ( X2 P2 – binding of P2 to the X2 promoter

e X1 P1 R X1 P1+P1 a production of P1 from a P1-bound gene

f X1 P2 R X1 P2+P1 b production of P1 from a P2-bound gene

g X2 P1 R X2 P1+P2 c production of P2 from a P1-bound gene

h X2 P2 R X2 P2+P2 d production of P2 from a P2-bound gene

i P1+P1 ( P1 P1 – homodimerization of P1

j P1+P2 ( P1 P2 – heterodimerization of P1 and P2

k P2+P2 ( P2 P2 – homodimerization of P2

l X1+P1 P1 ( X1 P1 P1 i binding of P1 P1 dimer to the X1 promoter

m X1+P1 P2 ( X1 P1 P2 j binding of P1 P2 dimer to the X1 promoter

n X1+P2 P2 ( X1 P2 P2 k binding of P2 P2 dimer to the X1 promoter

o X2+P1 P1 ( X2 P1 P1 i binding of P1 P1 dimer to the X2 promoter

p X2+P1 P2 ( X2 P1 P2 j binding of P1 P2 dimer to the X2 promoter

q X2+P2 P2 ( X2 P2 P2 k binding of P2 P2 dimer to the X2 promoter

r X1 P1 P1 R X1 P1P1+P1 i, l production of P1 from a P1 P1-bound gene

s X1 P1 P2 R X1 P1P2+P1 j, m production of P1 from a P1 P2-bound gene

t X1 P2 P2 R X1 P2P2+P1 k, n production of P1 from a P2 P2-bound gene

u X2 P1 P1 R X2 P1P1+P2 i, o production of P2 from a P1 P1-bound gene

v X2 P1 P2 R X2 P1P2+P2 j, p production of P2 from a P1 P2-bound gene

w X2 P2 P2 R X2 P2P2+P2 k, q production of P2 from a P2 P2-bound gene

*These reactions occur in every network.doi:10.1371/journal.pcbi.1002039.t001

Figure 1. Rudimentary two-gene network consisting of onlybasal protein production and degradation. In the ‘CRNT picture’,complexes are highlighted in yellow and linkage classes are identifiedwith dashed lines. Labeling scheme is adopted from [77].doi:10.1371/journal.pcbi.1002039.g001

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surveyed, 18,352 (*45%) have the capacity for multiple steadystates, with 14,721 of these being confirmed as bistable withexample rate constants (see Materials and Methods for adescription of the screening procedure). Only 2,654 networks(*6.5%) cannot be bistable regardless of the parameter values.For the remaining 19,674 networks, ADT could neither establishnor rule out the capacity for multiple steady states, and as a resultwe refer to these as ‘unknown’ networks. It is noteworthy that thefraction of networks of a given size (that is, a given number ofreactions) that are unknown increases as the size increases; forexample, w90% of networks with more than 21 reactions, and allnetworks with more than 24 reactions, are unknown (Figure 2). Asexpected, the stabilities of the decoupled two-gene networks arethe same as the constituent one-gene systems previously studied[23].The two smallest bistable networks identified exhibit canonical

switch topologies (Figure 3). In the double negative feedbackcircuit shown in Figure 3A, we find that dimerization of only oneof the TFs is sufficient for bistability. The autoregulatory positivefeedback network shown in Figure 3B is an example of adecoupled two-gene network, with bistability in the concentrationof one TF only. We note that while CRNT does not take intoaccount the strength of the regulation in determining a network’scapacity for multiple steady states, the fact that an autoregulatorycircuit requires positive feedback in order to achieve bistability is

well-established (see, e.g., [29,30]). Bistability via positiveautoregulation has also been demonstrated experimentally withsynthetic gene circuits in both prokaryotes [31] and eukaryotes[32].

Identifying bistability through network ancestryThe bistable networks shown in Figure 3, each containing

seven reactions, can be ‘grown’ into new eight-reaction networksthrough the addition of reactions from Table 1: reactions a, b, d,g, i, j, q, or t to the circuit shown in Figure 3A, and reactions a, b,c, d, i, j, or n to the circuit shown in Figure 3B. In all cases, thenew larger networks were also confirmed by the Toolbox to bebistable. We may then ask: is bistability, once established in a‘parent’ network of N reactions, guaranteed in any ‘descendant’network of Nz1 reactions? ADT alone is not sufficient to answerthis question, since systems were less likely to be characterizableas they increased in size (Figure 2). However, CRNT doesprovide a basis for establishing bistability in networks whichcontain subnetworks known to be bistable: if following theaddition of a reaction the stoichiometric subspace of the

Figure 2. Fraction of networks which cannot have their stabilityestablished by advanced deficiency theory (ADT), as a functionof network size.doi:10.1371/journal.pcbi.1002039.g002

Figure 3. The smallest two-gene bistable networks found withADT. (A) A double negative feedback circuit, in which dimerization ofonly one of the TFs is sufficient for bistability. (B) An autoregulatorypositive feedback circuit. The two genes are uncoupled and thebistability is in the concentration of one TF only. In both (A) and (B),degradation of the TF monomers is not shown.doi:10.1371/journal.pcbi.1002039.g003

Figure 4. The fraction of networks of each size that wereestablished as bistable by ADT, bistable by network ancestry,having multiple steady states with unconfirmed stability,monostable, or with an unknown capacity for multiple stablesteady states. Network size is determined only by the number ofreactions (from Table 1) that are present. The total number of networksof each size is shown in parentheses.doi:10.1371/journal.pcbi.1002039.g004

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descendant network is identical to that of the parent, then thelarger network is also bistable for some set of parameter values.As an intuitive example, one can imagine a situation in which areaction is added to an existing network, that the surfacecontaining the dynamical trajectories of the network species’concentrations is not changed as a result of the addition, and thatthe added reaction has only a very small rate constant. In thiscase we should not expect a change from whatever qualitativephenomena were there before. Thus, if the parent network hadtwo stable equilibria, the descendant network will also have twostable equilibria. Example reactions that do not result in a changein the stoichiometric subspace if added include protein produc-

tion from a TF-bound gene (XiPj ? XiPjzPi, since the reactionvectors can be written as linear combinations of the vectorsassociated with XizPj ' XiPj , Pi ?1, and Pj ?1). Beginningwith the 14,721 known bistable networks and using this‘ancestry’-based method, we identified an additional 22,050bistable networks. Some of these networks had been previouslyfound by the Toolbox to have the capacity for multiple steadystates, but for which no example parameter sets leading to stableequilibria were given. The number of networks of each type–bistable by ADT, bistable by ancestry, multiple steady states withunconfirmed stability, monostable, or unknown–are shown as afunction of network size in Figure 4.

Figure 5. Minimal bistable networks. Only 11 of the 36,771 bistable networks identified lose bistability by the removal of any network reaction.That is, only 11 of the bistable networks contain no subset of reactions which is also bistable. Dashed-and-colored lines indicate regulation byheterodimer. Horizontal bars represent purely-repressive TF binding, and arrows indicate TF production from a bound gene (at a non-zero rate thatmay be either higher or lower than the basal rate). Degradation of the TF monomers is not shown.doi:10.1371/journal.pcbi.1002039.g005

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Minimal bistable networksOf the 36,771 bistable systems identified, only eleven do not

contain within them a smaller subnetwork that is also bistable. Forthese eleven networks, the removal of any single reaction wouldresult in a loss of bistability. We refer to these networks as minimalbistable networks (MBNs). Named according to the reaction labels inTable 1, the MBNs are: kqw, ckn, bcdh, ikno, jmpsv, bfjpv, abejp, jknptv,jkmnps, dhjknp, and aejknp. The two networks shown in Figure 3 areminimal (kqw and ckn); the full set is shown in Figure 5. Arrowscontaining the symbol (+) are used in the figure and all thatfollow to emphasize that, in assessing a networks capacity formultiple steady states, CRNT does not distinguish between up-regulation and down-regulation that results in reduced but non-zero expression. With the exception of bcdh (discussed in moredetail in the following section), all of these networks contain one ormore of the TF dimerization reactions common in bistable GRNs[23]. It can also be seen that each MBN contains feedback loopsthat for some parameter sets will be made positive, a characteristicshown to be generally necessary for multiple steady states in asystem of ODEs [33].

Cooperativity-free switchesAlthough cooperativity in gene regulation–via either the non-

independent binding of TFs to multiple promoter sites or themultimerization of TFs into functional units–is an importantcomponent of some bistable networks [34,35], it is not necessaryfor bistability. Indeed, a number of recent mathematical models ofGRNs have shown deterministic bistability without cooperativityof any kind [36–38]. Among the 40,680 two-gene networks are 45lacking cooperativity, and of these 31 were found to bemonostable, eight were identified as bistable directly by ADT,and three more were identified as bistable by network ancestry. Allof the bistable networks lacking cooperativity can be derived fromthe MBN bcdh, which is shown in Figure 6 along with a bifurcationdiagram showing the existence of two stable equilibria (and anunstable equilibrium) for a range of P1 degradation rate constants.The complete set of cooperativity-free bistable networks is shownin Figure 7. An essential feature of these circuits is the competitivebinding of P1 and P2 to the X2 promoter. Similar competitive orsequestration-type processes have been found to be key compo-nents in some switch-like systems [36–40].

Two-gene networks in S. cerevisiaeTo investigate how an in silico network topology survey such as

this can be used to better understand experimental results, wesearched for real biological examples of the bistable networksidentified in this study in the model organism S. cerevisiae. To ourknowledge, there is no single database that contains S. cerevisiaeGRN architecture, thus we combined protein-protein and protein-DNA interaction data with gene expression data to establish thelarge-scale empirical network shown in Supplementary Figure S1.Included in this network are 148 TFs participating in 205 protein-protein interactions (61 heterodimerization and 144 homodimer-ization reactions), along with 1,249 interactions between 139 TFsand 208 genes (37 ‘self-binding’ and 1,212 ‘cross-binding’reactions). To establish which of the two-gene bistable circuitsare present in the yeast network, it was first necessary to ‘translate’the bistable models from their ideal, theoretical description (thatdistinguishes between and allows for each elementary reaction)into a format that is more amenable to experimental data mining;see Supplementary Text S1. We were then able to identify in theyeast data a total of 1,289 two-gene GRNs, twelve of which havetopologies consistent with members of the MBN set (Table 2).Examples of these are highlighted in the next section.

Discussion

The idea of studying theoretical network models generated via‘random wiring’ was suggested at least fifty years ago by Monodand Jacob [41]. Only recently, with the development of powerfulcomputational tools, have a variety of simple gene regulatory andmetabolic network topologies been studied with surveys over largeranges of parameter space [42,43]. Parameter-free techniques suchas CRNT are particularly well-suited for general surveys aimed atbistable network discovery, as they may more definitively answerquestions regarding a mass action system’s ability to supportmultiple steady states. For example, using only the advanceddeficiency theory (ADT) algorithm implemented in the ChemicalReaction Network Toolbox we were able to establish that *36%of the 40,680 possible unique two-gene networks are bistable for atleast some sets of network parameters, another *9% have thecapacity for multiple steady states (which may or may not bestable), and only *6.5% are monostable regardless of the networkparameters.As network size and complexity increases, the ability of ADT to

draw conclusions becomes limited (Figure 2). One method putforward as a way to extend the usefulness of CRNT to largernetworks involves the analysis of simpler subnetworks correspond-ing with elementary flux modes of the system [25]. We haveintroduced a complementary subnetwork analysis method foridentifying bistability, termed network ancestry, which requiresonly a topological sorting of the networks based on the presence orabsence of individual reactions followed by inspection of thenetwork reaction vectors. If the parent network is determined to bebistable, and if the reaction vectors of the bistable parent andunknown descendant have the same span (i.e., the networks havean identical stoichiometric subspace), then the descendant is alsobistable. As a result of network ancestry, we were able to identifyan additional 22,050 networks with previously unknown stabilityas bistable, *54% of the total (Figure 4). We emphasize that achange in the size of the stoichiometric subspace does not in and ofitself imply that bistability will be lost; however, from a purelytopological perspective, it may not be obvious what the effect ofthe change may be. Our network ancestry method may thus beconsidered a relatively conservative one for establishing bistabilityin larger networks.

Figure 6. Example of a bistable network lacking cooperativity.TF P2 plays a dual role as an activator of X2 and a repressor of X1 . Thebifurcation plot shows the stable (solid lines) and unstable (dashedlines) steady state protein concentrations, in units relative to the DNAconcentration, for one set of parameter values as a function of the P1degradation rate. The network ODEs and parameter values are given inthe Supplementary Text S1.doi:10.1371/journal.pcbi.1002039.g006

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The assumption of mass action kinetics is an important aspectof CRNT. Consequently, Michaelis-Menten and Hill-typeexpressions are not used in our CRN approach, as they requireapproximations to mass action that cannot be validated in aparameter-free context. In addition, it was recently demonstratedfor a generic two-protein interaction network that bistabilitypresent under the ‘inconsistent’ assumption of Michaelis-Mentenkinetics is lost when the system is ‘unpacked’ into its fundamentalchemical steps [44]. For our two-gene networks, the Michaelis-Menten and CRN descriptions could be approximately equiva-lent only for specific parameters, and only if those parameters

were such that 1) the DNA-binding reactions reach theirequilibria much more quickly than other reactions in thenetwork, and 2) the equilibrium concentrations of any dimerspecies were proportional to the product of their constituentmonomer concentrations [45].In addition to the inherent consistency of CRN models, the

mathematical theory applicable to deterministic CRNs offerssignificant computational advantages over other methods, inparticular stochastic simulation. Furthermore, many deterministi-cally bistable networks have been shown to retain two long-livedstates when their models are reformulated to take stochasticity into

Figure 7. Bistable networks without cooperativity. Of the 45 two-gene networks lacking dimerization, 11 were identified as bistable eitherdirectly by advanced deficiency theory analysis or via network ancestry. All the dimer-free bistable networks shown here can be derived from theminimal bistable network bcdh through the addition of reactions from Table 1. Horizontal bars represent purely-repressive TF binding, and arrowsindicate TF production from a bound gene (at a non-zero rate that may be either higher or lower than the basal rate). Degradation of the TFmonomers is not shown.doi:10.1371/journal.pcbi.1002039.g007

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account [37,44,46,47]. Still, as biochemical noise has been shownto drive some systems to exhibit switch-like behavior not predictedby deterministic models [37,47–50], it should be considered in anycomplete study of a specific network of interest. For modelsalready formulated as CRNs, stochastic simulation is relativelystraightforward (see, e.g., [44,51]).We attempted to capture the most prevalent and basic

biochemical processes involved in transcriptional regulation inour network model construction, but our formalism is by nomeans exhaustive. One mechanism not included and throughwhich networks can achieve the nonlinearity required forbistability is the direct degradation of TF dimers (PiPj ?1)[38]. Given that dimerization regularly protects against proteol-ysis (see, e.g., [52,53]), its exclusion from our reaction set isreasonable. Furthermore, for most of the networks analyzed here,the addition of a dimer degradation reaction would have no effecton their capacity for bistability: since the reaction vector for PiPj

?1 can be written as a linear combination of the vectorsassociated with reactions PizPj ' PiPj , Pi ?1 and Pj ?1,any descendant network grown from a bistable parent via theaddition of a dimer degradation reaction would have the samestoichiometric subspace and would be bistable as a result ofnetwork ancestry.There remains a large amount of additional biological detail

which could be incorporated in future surveys, including post-translational modification, multiple promoter binding sites, andthe location of regulatory elements relative to the genes (whichhas been shown to play a role in network bistability [54]).However, any increase in the level of detail would result in anincrease in the combinatorial complexity and the size of thesurvey. For example, whereas the set of one-gene networks areconstructed using combinations of 5 different reactions [23], andour two-gene networks using 23 reactions (Table 1), the additionof a third gene alone would lead to 60 different reactions thatcould be ‘wired’ together. With the current version of theToolbox taking (at best) many seconds to import, analyze, andexport the results for every network model, it is perhaps not anideal software package for surveys significantly larger than thisone. New software implementations of CRNT continue to bedeveloped (e.g., [55]), and we anticipate that future programs will

allow for even more comprehensive computational studies. In themeantime, network ancestry offers an attractive solution to theproblem of scalability and applicability of CRNT to morecomplex networks: once all fundamental chemical reactionsinvolved in any network of interest are identified, one couldassemble the minimal network topologies covering all possibleunique stoichiometric subspaces and probe that smaller set ofnetworks for bistability. In essence, network ancestry allows forthe reduction of the problem of determining a large network’squalitative capacity for bistability to one of identifying theminimal bistable subnetworks within it.There is a strong biological motivation to consider individual

networks as parents and descendants with a topological ordering:rather than appearing de novo, modern GRNs grow from ancestornetwork kernels through mechanisms such as gene duplicationand the accretion of protein domains [56–59]. Domainaccretion, for example the acquisition of a DNA-binding domainby a monomer (modeled in this work by the addition of one ofthe promoter binding reactions a, b, c, or d), has been proposedto be particularly important for eukaryotic evolution [60,61].And there is evidence suggesting an even more direct role forbistability in evolution: it is the primary requirement forepigenetic inheritance mechanisms known to have importantevolutionary effects [62,63], and can also lead to increasedpopulation fitness in stressful or changing environments [64,65]by driving an increase in phenotypic heterogeneity [66]. Thus,the eleven MBNs identified here (Figure 5), which differ frommonostable networks by just a single reaction, may represent aninteresting class of networks from the standpoint of evolutionarybiology, as it may be that similarly-minimal networks haveplayed an important role in functional development and/orspeciation.We used the results of our in silico analysis to motivate a search

of–and add functional context to–existing yeast protein-DNA andprotein-protein interaction data, and in doing so were able toidentify a number of two-gene systems with topologies consistentwith bistability. For example, the FKH1 and FKH2 genes (andtheir associated proteins Fkh1p and Fkh2p, which compete fortarget promoter occupancy [67]) compose a network with atopology similar to the MBN bcdh (Table 2). FKH1 and FKH2belong to the pervasive winged-helix/forkhead (FOX) family ofTFs and are essential for proper regulation of the yeast cell cycle[68]. Other FOX genes have previously been shown to beinvolved in important biological functions including cell cycleregulation and cell differentiation [69], two processes for whichGRN bistability has been implicated [2–5].Additional gene pairs of interest include NRG1/RIM101 and

OAF1/PIP2, which are components of GRNs with topologiessimilar to that of MBNs abejp and aejknp, respectively. TheRim101p and Nrg1p proteins, both identified previously astranscriptional repressors, are components in an extracellularpH-responsive differentiation pathway in yeast [70]. Furtherevidence suggestive of bistability in this system can be found inC. albicans, in which Rim101p and Nrg1p homologs regulate themorphological switch [71] associated with a dramatic change inthe pathogen’s virulence [72]. Oaf1p and Pip2p, on the otherhand, are involved in the production of peroxisomal proteins inthe presence of fatty acids [73], and have been shown to beinvolved in the coordination of two different transcriptionalresponses to oleate [74]. We emphasize that while the two-genenetworks identified through our analysis are not guaranteed to bebistable, their known topologies and functions make themexcellent bistable network candidates, providing powerful hypoth-eses for further experimentation. The same approach may be used

Table 2. Two-gene networks found in S. cerevisiae that havetopologies consistent with members of the minimal bistablenetwork set.

Bistable model* X1 X2

ckn PDR1 (YGL013C) RPN4 (YDL020C)

bcdh FHL1 (YPR104C) MSN4 (YKL062W)

bcdh HMS1 (YOR032C) YAP6 (YDR259C)

bcdh IXR1 (YKL032C) PHD1 (YKL043W)

bcdh RPN4 (YDL020C) YAP1 (YML007W)

bcdh FKH1 (YIL131C) FKH2 (YNL068C)

jknptv MTH1 (YDR277C) RGT1 (YKL038W)

aejknp OAF1 (YAL051W) PIP2 (YOR363C)

abejp NRG1 (YDR043C) RIM101 (YHL027W)

abejp IFH1 (YLR223C) RAP1 (YNL216W)

bfjpv KSS1 (YGR040W) CST6 (YIL036W)

bfjpv OPI1 (YHL020C) INO2 (YDR123C)

*Model names refer to the constituent reactions as labeled in Table 1.doi:10.1371/journal.pcbi.1002039.t002

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to provide guidance or functional context to any system for whichthe necessary interaction data is available.High-throughput parameter-free analysis holds potential, not

just as a tool for the study of natural systems, but also as a designaid in the growing field of synthetic biology [75,76]. For example,a survey such as this can provide inspiration for the developmentof new bistable switches and a library of models to draw from;already we have proposed a set of novel bistable networks that lackcooperativity and which may be particularly good designs as aresult (e.g., because they do not require any ‘extra’ engineering ofdimerization domains). At the very least, such a broad applicationof CRNT may be used to rule out (possibly large numbers of)designs incapable of bistability. CRNT can be similarly used torule out circuits without the capacity for sustained oscillations [16]or those which cannot exhibit ‘absolute concentration robustness’[77].

It is worth emphasizing that the region of parameter spacesupporting bistability in any individual network cannot bedetermined via parameter-free techniques alone. For example,it may be that the necessary parameter values lie outside therange of biological reality or are difficult to engineer, or that thesize of the bistable region of parameter space is exceedingly small.However, in many large-scale studies, such as those that resultedin the yeast data sets used in this work, a high degree ofbiochemical detail is simply nonexistent. While this lack ofquantitative detail can make some analyses of biological networkschallenging, it also opens up opportunities for parameter-freestudies to provide experimental guidance and new functionalinsights [78]. Once identified, potentially interesting networkarchitectures may be analyzed in more detail, with rate constantschosen, for example, by Monte Carlo sampling of parameterspace.

Figure 8. Screening networks for different steady state behaviors. Networks are initially screened by the content of analysis reportsproduced by the Chemical Reaction Network Toolbox. The networks designated ‘multiple steady states’ are those determined by ADT to have thecapacity for multiple steady states but for which no example pair of asymptotically stable steady states could be found by the program. Bistablenetworks are those for which an example pair of asymptotically stable steady states was reported. The complete sorting procedure is described inMaterials and Methods.doi:10.1371/journal.pcbi.1002039.g008

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Materials and Methods

Two-gene network constructionTwo-gene networks were generated in MATLAB (2009a, The

MathWorks, Inc.) by first enumerating all possibilities and thenremoving one network from each symmetric pair (defined by twofunctionally-equivalent networks which can be made identicalthrough a simple change of component subscripts). The hetero-dimers P1P2 and P2P1 were assumed to be equivalent.

Chemical reaction network theory analysis and networkscreeningAdvanced deficiency theory analysis was done using a

preliminary version of the Chemical Reaction Network Toolboxv2.0 (http://www.chbmeng.ohio-state.edu/,feinberg/crntwin/)made available to us by M. Feinberg and automated with AutoItv3 (http://www.autoitscript.com/autoit3/index.shtml).Networks were screened based on the content of the analysis

reports generated by the Toolbox. These reports, though uniqueto each network, all contain one of three statements: either thenetwork ‘‘DOES have the capacity for multiple steady states’’,‘‘CANNOT admit multiple positive steady states’’, or ‘‘MAY havethe capacity for multiple steady states’’. Networks with reportscontaining one of the latter two statements were labeledmonostable and unknown, respectively. If a network wasdetermined by ADT to have the capacity for multiple steadystates, the analysis report also contained one (or more) exampleset(s) of rate constants and the associated pair(s) of distinct steadystates. However, each steady state may be either asymptoticallystable, unstable, or with a stability that is ‘‘left undetermined’’.Only those networks that could support multiple steady states andfor which an example pair of asymptotically stable steady stateswas given were deemed to be bistable networks. This is not toimply that multiple steady state networks without such an exampleare not bistable, only that we were unable to confirm theirbistability with ADT. The screening procedure is shownschematically in Figure 8.Network ancestry and minimal bistable network analysis was

done using MATLAB. Parent and descendant network pairs werefound by simple comparison of the networks stoichiometricsubspaces and their constituent reactions (descendant networkscontain all the same reactions as their parents plus one additionalreaction). Cooperativity-free networks were identified by their lackof dimerization reactions, since by construction, the model genesdo not have two TF binding sites that could be occupiedsimultaneously and there are no multi-protein complexes largerthan dimers.Additional data analysis was done with MATLAB and

Mathematica (Wolfram Research, Inc.). The bifurcation plotshown in Figure 6 was generated using XPPAUT (http://www.math.pitt.edu/,bard/xpp/xpp.html).

Identification of bistable networks in S. cerevisiaeA set of 228 yeast genes previously established as coding for

transcriptional regulators [79,80] was used as the primary sourcefor candidate network TF genes (Supplementary Table S1).Protein-protein interactions were retrieved from the BioGRIDdatabase [81] (Supplementary Table S2) and protein-DNAinteractions were retrieved from the Yeastract database [82](Supplementary Table S3). The effect of the protein-DNAinteractions on target gene expression (activation or repression)is usually unknown, and any information suggestive of a particulareffect was used supplementarily in the network discovery process(Supplementary Table S4).

Supporting Information

Figure S1 Large-scale GRN in S. cerevisiae. GRN was generatedthrough the combination of protein-protein interaction, protein-DNA interaction, and gene expression data.(EPS)

Table S1 List of genes/proteins considered as transcriptionalregulators in yeast. Data taken from [79,80].(XLS)

Table S2 List of protein-protein interactions. Physical protein-protein interactions between yeast transcriptional regulatorsextracted from BioGRID database [81].(XLS)

Table S3 List of protein-DNA interactions. Physical protein-DNA interactions were extracted from Yeastract database [82].(XLS)

Table S4 Transcriptional effect of protein-DNA interactions.(XLS)

Text S1 ODEs and parameter values for Fig. 6, and the methodused in translating bistable network models into the experimentaldata mining format.(PDF)

Acknowledgments

We are grateful to M. Feinberg for the preliminary version of the ChemicalReaction Network Toolbox v2.0 and for many useful discussions, and to G.Craciun, J. Stelling, A. P. Arkin, J. Paulsson, and J. J. Collins for theircomments as well. DSG was jointly mentored by GDS and EG.

Author Contributions

Conceived and designed the experiments: DSG GDS EG. Performed theexperiments: DSG MKMG GDS. Analyzed the data: DSG MKMG.Contributed reagents/materials/analysis tools: DSG GDS. Wrote thepaper: DSG MKMG GDS EG.

References

1. Pomerening JR (2008) Uncovering mechanisms of bistability in biologicalsystems. Curr Opin Biotechnol 19: 381–8.

2. Lai K, Robertson MJ, Schaffer DV (2004) The sonic hedgehog signaling systemas a bistable genetic switch. Biophys J 86: 2748–57.

3. Laslo P, Spooner CJ, Warmflash A, Lancki DW, Lee HJ, et al. (2006)Multilineage transcriptional priming and determination of alternate hematopoi-etic cell fates. Cell 126: 755–66.

4. Huang S, Guo YP, May G, Enver T (2007) Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol 305: 695–713.

5. Cross FR, Archambault V, Miller M, Klovstad M (2002) Testing a mathematicalmodel of the yeast cell cycle. Mol Biol Cell 13: 52–70.

6. Bagci EZ, Vodovotz Y, Billiar TR, Ermentrout GB, Bahar I (2006) Bistability inapoptosis: roles of Bax, Bcl-2, and mitochondrial permeability transition pores.Biophys J 90: 1546–59.

7. Eissing T, Conzelmann H, Gilles ED, Allgower F, Bullinger E, et al. (2004)Bistability analyses of a caspase activation model for receptor-induced apoptosis.J Biol Chem 279: 36892–7.

8. Legewie S, Bluthgen N, Herzel H (2006) Mathematical modeling identifiesinhibitors of apoptosis as mediators of positive feedback and bistability. PLoSComput Biol 2: e120.

9. Avery SV (2005) Cell individuality: the bistability of competence development.Trends Microbiol 13: 459–62.

Emergence of Switch-Like Behavior in Networks

PLoS Computational Biology | www.ploscompbiol.org 10 May 2011 | Volume 7 | Issue 5 | e1002039

10. Ninfa AJ, Mayo AE (2004) Hysteresis vs. graded responses: the connectionsmake all the difference. Sci STKE 2004: pe20.

11. Guidi G, Goldbeter A (1997) Bistability without hysteresis in chemical reactionsystems: a theoretical analysis of irreversible transitions between multiple steadystates. J Phys Chem A 101: 9367–9376.

12. Pomerening JR, Sontag ED, Ferrell JE (2003) Building a cell cycle oscillator:hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 5: 346–51.

13. ShaW, Moore J, Chen K, Lassaletta AD, Yi CS, et al. (2003) Hysteresis drivescell-cycle transitions in Xenopus laevis egg extracts. Proc Natl Acad Sci USA 100:975–80.

14. Ozbudak EM, Thattai M, Lim HN, Shraiman BI, van Oudenaarden A (2004)Multistability in the lactose utilization network of Escherichia coli. Nature 427:737–40.

15. Tyson JJ, Chen KC, Novak B (2001) Network dynamics and cell physiology. NatRev Mol Cell Biol 2: 908–16.

16. Feinberg M (1987) Chemical reaction network structure and the stability ofcomplex isothermal reactors–I. The Deficiency Zero and Deficiency OneTheorems. Chem Eng Sci 42: 2229–2268.

17. Feinberg M (1988) Chemical reaction network structure and the stability ofcomplex isothermal reactors. II: Multiple steady states for networks of deficiencyone. Chem Eng Sci 43: 1–25.

18. Ellison P, Feinberg M (2000) How catalytic mechanisms reveal themselves inmultiple steady-state data: I. Basic principles. J Mol Catal A-Chem 154:155–167.

19. Kaltenbach HM, Dimopoulos S, Stelling J (2009) Systems analysis of cellularnetworks under uncertainty. FEBS Lett 583: 3923–30.

20. Conradi C, Saez-Rodriguez J, Gilles ED, Raisch J (2005) Using chemicalreaction network theory to discard a kinetic mechanism hypothesis. Syst Biol152: 243–8.

21. Otero-Muras I, Banga JR, Alonso AA (2009) Exploring multiplicity conditions inenzymatic reaction networks. Biotechnol Prog 25: 619–31.

22. Craciun G, Tang Y, Feinberg M (2006) Understanding bistability in complexenzyme-driven reaction networks. Proc Natl Acad Sci USA 103: 8697–702.

23. Siegal-Gaskins D, Grotewold E, Smith GD (2009) The capacity for multistabilityin small gene regulatory networks. BMC Syst Biol 3: 96.

24. Ellison P (1998) The advanced deficiency algorithm and its applications tomechanism discrimination [PhD thesis]. Rochester (New York): Department ofChemical Engineering, University of Rochester.

25. Conradi C, Flockerzi D, Raisch J, Stelling J (2007) Subnetwork analysis revealsdynamic features of complex (bio)chemical networks. Proc Natl Acad Sci USA104: 19175–80.

26. Flockerzi D, Conradi C (2008) Subnetwork analysis for multistationarity in massaction kinetics. J Phys: Conf Ser 138: 012006.

27. Miller CA, Beard DA (2008) The effects of reversibility and noise on stochasticphosphorylation cycles and cascades. Biophys J 95: 2183–92.

28. Saez-Rodriguez J, Hammerle-Fickinger A, Dalal O, Klamt S, Gilles ED, et al.(2008) Multistability of signal transduction motifs. IET Syst Biol 2: 80–93.

29. Keller AD (1995) Model genetic circuits encoding autoregulatory transcriptionfactors. J Theor Biol 172: 169–85.

30. Hasty J, Isaacs F, Dolnik M, McMillen D, Collins JJ (2001) Designer genenetworks: Towards fundamental cellular control. Chaos 11: 207–220.

31. Isaacs FJ, Hasty J, Cantor CR, Collins JJ (2003) Prediction and measurement ofan autoregulatory genetic module. Proc Natl Acad Sci USA 100: 7714–9.

32. Becskei A, Seraphin B, Serrano L (2001) Positive feedback in eukaryotic genenetworks: cell differentiation by graded to binary response conversion. EMBO J20: 2528–35.

33. Cinquin O, Demongeot J (2002) Positive and negative feedback: striking abalance between necessary antagonists. J Theor Biol 216: 229–41.

34. Cherry JL, Adler FR (2000) How to make a biological switch. J Theor Biol 203:117–33.

35. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggleswitch in Escherichia coli. Nature 403: 339–42.

36. Francois P, Hakim V (2004) Design of genetic networks with specified functionsby evolution in silico. Proc Natl Acad Sci USA 101: 580–5.

37. Lipshtat A, Loinger A, Balaban NQ, Biham O (2006) Genetic toggle switchwithout cooperative binding. Phys Rev Lett 96: 188101.

38. Buchler NE, Louis M (2008) Molecular titration and ultrasensitivity in regulatorynetworks. J Mol Biol 384: 1106–19.

39. Sedlak TW, Oltvai ZN, Yang E, Wang K, Boise LH, et al. (1995) Multiple Bcl-2family members demonstrate selective dimerizations with Bax. Proc Natl AcadSci USA 92: 7834–8.

40. Basak S, Shih VFS, Hoffmann A (2008) Generation and activation of multipledimeric transcription factors within the NF-kB signaling system. Mol Cell Biol28: 3139–50.

41. Monod J, Jacob F (1961) Teleonomic mechanisms in cellular metabolism,growth, and differentiation. Cold Spring Harb Symp Quant Biol 26: 389–401.

42. Ramakrishnan N, Bhalla US (2008) Memory switches in chemical reactionspace. PLoS Comput Biol 4: e1000122.

43. Ma W, Trusina A, El-Samad H, Lim WA, Tang C (2009) Defining networktopologies that can achieve biochemical adaptation. Cell 138: 760–73.

44. Sabouri-Ghomi M, Ciliberto A, Kar S, Novak B, Tyson JJ (2008) Antagonismand bistability in protein interaction networks. J Theor Biol 250: 209–18.

45. Bundschuh R, Hayot F, Jayaprakash C (2003) Fluctuations and slow variables ingenetic networks. Biophys J 84: 1606–15.

46. Stamatakis M, Mantzaris NV (2009) Comparison of deterministic and stochasticmodels of the lac operon genetic network. Biophys J 96: 887–906.

47. Kepler TB, Elston TC (2001) Stochasticity in transcriptional regulation: origins,consequences, and mathematical representations. Biophys J 81: 3116–36.

48. Blake WJ, Kaern M, Cantor CR, Collins JJ (2003) Noise in eukaryotic geneexpression. Nature 422: 633–7.

49. Samoilov M, Plyasunov S, Arkin AP (2005) Stochastic amplification andsignaling in enzymatic futile cycles through noise-induced bistability withoscillations. Proc Natl Acad Sci USA 102: 2310–5.

50. Arkin AP, Ross J, McAdams HH (1998) Stochastic kinetic analysis ofdevelopmental pathway bifurcation in phage lambda-infected Escherichia colicells. Genetics 149: 1633–48.

51. Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev PhysChem 58: 35–55.

52. Jenal U, Hengge-Aronis R (2003) Regulation by proteolysis in bacterial cells.Curr Opin Microbiol 6: 163–72.

53. Johnson PR, Swanson R, Rakhilina L, Hochstrasser M (1998) Degradationsignal masking by heterodimerization of MATa2 and MATa1 blocks theirmutual destruction by the ubiquitinproteasome pathway. Cell 94: 217–27.

54. Kelemen JZ, Ratna P, Scherrer S, Becskei A (2010) Spatial epigenetic control ofmono- and bistable gene expression. PLoS Biol 8: e1000332.

55. Soranzo N, Altafini C (2009) ERNEST: a toolbox for chemical reaction networktheory. Bioinformatics 25: 2853–4.

56. Chothia C, Gough J, Vogel C, Teichmann SA (2003) Evolution of the proteinrepertoire. Science 300: 1701–3.

57. Force A, Cresko WA, Pickett FB, Proulx SR, Amemiya C, et al. (2005) Theorigin of subfunctions and modular gene regulation. Genetics 170: 433–46.

58. Lynch M, Conery JS (2003) The origins of genome complexity. Science 302:1401–4.

59. Buljan M, Frankish A, Bateman A (2010) Quantifying the mechanisms ofdomain gain in animal proteins. Genome Biol 11: R74.

60. Babushok DV, Ostertag EM, Kazazian HH (2007) Current topics in genomeevolution: molecular mechanisms of new gene formation. Cell Mol Life Sci 64:542–54.

61. Koonin EV, Aravind L, Kondrashov AS (2000) The impact of comparativegenomics on our understanding of evolution. Cell 101: 573–6.

62. Veening JW, Smits WK, Kuipers OP (2008) Bistability, epigenetics, and bet-hedging in bacteria. Annu Rev Microbiol 62: 193–210.

63. Jablonka E, Lamb M (1998) Epigenetic inheritance in evolution. J Evol Biol 11:159–183.

64. Bishop AL, Rab FA, Sumner ER, Avery SV (2007) Phenotypic heterogeneitycan enhance rare-cell survival in ‘stress-sensitive’ yeast populations. MolMicrobiol 63: 507–20.

65. Kussell E, Leibler S (2005) Phenotypic diversity, population growth, andinformation in fluctuating environments. Science 309: 2075–8.

66. Dubnau D, Losick R (2006) Bistability in bacteria. Mol Microbiol 61: 564–72.67. Hollenhorst PC, Pietz G, Fox CA (2001) Mechanisms controlling differential

promoter-occupancy by the yeast forkhead proteins Fkh1p and Fkh2p:implications for regulating the cell cycle and differentiation. Genes Dev 15:2445–56.

68. Zhu G, Spellman PT, Volpe T, Brown PO, Botstein D, et al. (2000) Two yeastforkhead genes regulate the cell cycle and pseudohyphal growth. Nature 406:90–4.

69. Hannenhalli S, Kaestner KH (2009) The evolution of Fox genes and their role indevelopment and disease. Nat Rev Genet 10: 233–40.

70. Lamb TM, Mitchell AP (2003) The transcription factor Rim101p governs iontolerance and cell differentiation by direct repression of the regulatory genesNRG1 and SMP1 in Saccharomyces cerevisiae. Mol Cell Biol 23: 677–86.

71. Bensen ES, Martin SJ, Li M, Berman J, Davis DA (2004) Transcriptionalprofiling in Candida albicans reveals new adaptive responses to extracellular pHand functions for Rim101p. Mol Microbiol 54: 1335–51.

72. Kumamoto CA, Vinces MD (2005) Contributions of hyphae and hypha-co-regulated genes to Candida albicans virulence. Cell Microbiol 7: 1546–54.

73. Baumgartner U, Hamilton B, Piskacek M, Ruis H, Rottensteiner H (1999)Functional analysis of the zn(2)cys(6) transcription factors oaf1p and pip2p.different roles in fatty acid induction of beta-oxidation in saccharomycescerevisiae. J Biol Chem 274: 22208–16.

74. Smith JJ, Ramsey SA, Marelli M, Marzolf B, Hwang D, et al. (2007)Transcriptional responses to fatty acid are coordinated by combinatorial control.Mol Syst Biol 3: 115.

75. Andrianantoandro E, Basu S, Karig DK, Weiss R (2006) Synthetic biology: newengineering rules for an emerging discipline. Mol Syst Biol 2: 2006.0028.

76. Ellis T, Wang X, Collins JJ (2009) Diversity-based, model-guided construction ofsynthetic gene networks with predicted functions. Nat Biotechnol 27: 465–71.

77. Shinar G, Feinberg M (2010) Structural sources of robustness in biochemicalreaction networks. Science 327: 1389–91.

78. Bailey JE (2001) Complex biology with no parameters. Nat Biotechnol 19:503–4.

79. Harbison C, Gordon D, Lee T, Rinaldi N, Macisaac K, et al. (2004)Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99–104.

80. Drobna E, Bialkova A, Subik J (2008) Transcriptional regulators of seven yeastspecies: Comparative genome analysis - review. Folia Microbiol 53: 275–287.

Emergence of Switch-Like Behavior in Networks

PLoS Computational Biology | www.ploscompbiol.org 11 May 2011 | Volume 7 | Issue 5 | e1002039

81. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, et al. (2008) TheBioGRID interaction database: 2008 update. Nucleic Acids Res 36:D637–D640.

82. Teixeira MC, Monteiro P, Jain P, Tenreiro S, Fernandes AR, et al. (2006) TheYEASTRACT database: a tool for the analysis of transcription regulatoryassociations in Saccharomyces cerevisiae. Nucleic Acids Res 34: D446–D451.

Emergence of Switch-Like Behavior in Networks

PLoS Computational Biology | www.ploscompbiol.org 12 May 2011 | Volume 7 | Issue 5 | e1002039

Emergence of switch-like behavior in a large family of simple biochemical networks Dan Siegal-Gaskins1,2, Maria Katherine Mejia-Guerra2, Gregory D. Smith3, and Erich Grotewold2 1. Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA 2. Department of Plant Cellular and Molecular Biology and Plant Biotechnology Center, The Ohio State University,

Columbus, OH 43210, USA 3. Department of Applied Science, The College of William and Mary, Williamsburg, VA 23187, USA Supplementary Information ODEs and parameter values for Fig. 7 The dimer-free network shown in Fig. 7 can be described with the following set of ODEs: P1' = k1*(X1

tot - X1P2) - k7*(X2tot - X2P1 - X2P2)*P1 + k8*X2P1 - k11*P1

P2' = k2*(X2

tot - X2P1 - X2P2) - k5*(X1tot - X1P2)*P2 + k6*X1P2 - k9*(X2

tot - X2P1 - X2P2)*P2 + k10*X2P2 - k12*P2 + k16*X2P2 X1P2' = k5*(X1

tot - X1P2)*P2 - k6*X1P2 X2P1' = k7*(X2

tot - X2P1 - X2P2)*P1 - k8*X2P1 X2P2' = k9*(X2

tot - X2P1 - X2P2)*P2 - k10*X2P2 where the Pi are the concentrations of the free protein monomers, the XiPj are the concentrations of the protein-DNA complexes, the Xi

tot are the total DNA (free + bound) concentrations, and the ki are model parameters. The bifurcation plot was generated with the following parameter values: X1

tot = X2tot = 10 C

(C being an arbitrary unit of concentration), k1 = 7.22 time-1, k2 = 0.63 time-1, k5 = 0.36 C-1 time-1, k6 = 0.40 time-1, k7 = 0.63 C-1 time-1, k8 = 0.25 time-1, k9 = 0.057 C-1 time-1, k10 = 0.17 time-1, k12 = 0.5 time-1, and k16 = 1.71 time-1. The degradation rate k11 is used as the bifurcation parameter. Identification of bistable networks in S. cerevisiae (supplementary methods) Because limitations in the data-gathering techniques do not allow for the identification of interactions between heterodimers and DNA (reactions m, p, s, and v) with any certainty, we did not consider these reactions when searching for networks. Similarly, it cannot be determined from the data whether the TFs bind to DNA as monomers or dimers; for example, although we distinguish in our modeling framework between P1 binding to X1 and P1P1 binding to X1, that resolution does not exist in the experimental data. We therefore generated new ‘experimental evidence’ labels for pairs of reactions that cannot be distinguished: reactions b and n are referred to with label b, c and o with label c, a and l with label a, and d and q with label d. Lastly, for most of the pairs of TFs and promoters which are known to associate, the effect of that association on target gene expression (activation or repression) is usually unknown. Information suggestive of a particular effect, gathered from a large number of TF deletion strains and listed in Supplementary Table S4, was viewed as only supplementary in the process of network discovery. With these experimental limitations in mind, networks may be ‘translated’ from their theoretical description into one that takes the limitations into account. For example, the minimal bistable networks may be written as (theoretical name ! translated name) are:

1) kqw ! dk(w) 2) ckn ! bck 3) bcdh ! bcd(h) 4) abejp ! ab(e)j 5) bfjpv ! b(f)j 6) jmpsv ! j 7) ikno ! bcik 8) jknptv ! bjk(t) 9) aejknp ! ab(e)jk 10) jkmnps ! bjk 11) abdfjkmnpq ! - 12) dhjknp ! bd(h)jk

Labels in parenthesis indicate a reaction that is supplementary to the network discovery. Note that network abdfjkmnpq cannot be translated because it contains both monomeric and dimeric TF-promoter binding explicitly. Supplementary Table S1. List of genes/proteins considered as transcriptional regulators in yeast, taken from [1] and [2]. Supplementary Table S2. List of protein-protein interactions. Physica protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database [3]. Supplementary Table S3. List of protein-DNA interactions. Physical protein-DNA interactions were extracted from YEASTRACT database [4]. Supplementary Table S4. Protein-DNA binding reactions which renders activation or repression extracted from literature. Supplementary Figure S1. Large-scale GRN in S. cerevisiae, generated through the combination of protein-protein interaction, protein-DNA interaction, and gene expression data.

References

1. Harbison C, Gordon D, Lee T, Rinaldi N, Macisaac K, Danford T, Hannett N, Tagne J, Reynolds D, Yoo J, Jennings E, Zeitlinger J, Pokholok D, Kellis M, Rolfe P, Takusagawa K, Lander E, Gifford D, Fraenkel E, and Young R (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431:99-104.

2. Drobna E, Bialkova A, and Subik J (2008) Transcriptional regulators of seven yeast species:

Comparative genome analysis - Review. Folia Microbiol., 53:275–287. 3. Breitkreutz B, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH,

Bahler J, Wood V, Dolinski K, and Tyers M (2008) The BioGRID interaction database: 2008 update. Nucleic Acids Res, 36:D637–D640.

4. Teixeira MC, Monteiro P, Jain P, Tenreiro S, Fernandes AR, Mira NP, Alenquer M, Freitas AT,

Oliveira AL, and Sá-Correia I (2006) The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res, 34:446-451.

Page 1 of 6

Systematic Gene Name

Common Gene Name Protein Name

YCR040W A1 A1pYKL112W ABF1 Abf1pYER045C ACA1 Aca1pYLR131C ACE2 Ace2pYDR216W ADR1 Adr1pYGL071W AFT1 Aft1pYPL202C AFT2 Aft2pYMR042W ARG80 Arg80pYML099C ARG81 Arg81pYDR421W ARO80 Aro80pYPR199C ARR1 Arr1pYKL185W ASH1 Ash1pYGR097W ASK10 Ask10pYOR113W AZF1 Azf1pYKR099W BAS1 Bas1pYKL005C BYE1 Bye1pYDR423C CAD1 Cad1pYJR060W CBF1 Cbf1pYLR098C CHA4 Cha4pYOR028C CIN5 Cin5pYNL027W CRZ1 Crz1pYIL036W CST6 Cst6pYPL177C CUP9 Cup9pYKR034W DAL80 Dal80pYIR023W DAL81 Dal81pYNL314W DAL82 Dal82pYML113W DAT1 Dat1pYPL049C DIG1 Dig1pYER088C DOT6 Dot6pYLR228C ECM22 Ecm22pYBR033W EDS1 Eds1pYNR054C ESF2 Esf2pYDL166C FAP7 Fap7pYPR104C FHL1 Fhl1pYIL131C FKH1 Fkh1pYNL068C FKH2 Fkh2pYGL254W FZF1 Fzf1pYDR009W GAL3 Gal3pYPL248C GAL4 Gal4pYML051W GAL80 Gal80pYFL021W GAT1 Gat1pYLR013W GAT3 Gat3pYEL009C GCN4 Gcn4p

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

Page 2 of 6

Systematic Gene Name

Common Gene Name Protein Name

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

YPL075W GCR1 Gcr1pYNL199C GCR2 Gcr2pYER040W GLN3 Gln3pYGL181W GTS1 Gts1pYJL110C GZF3 Gzf3pYPR008W HAA1 Haa1pYFL031W HAC1 Hac1pYOL089C HAL9 Hal9pYLR256W HAP1 Hap1pYGL237C HAP2 Hap2pYBL021C HAP3 Hap3pYKL109W HAP4 Hap4pYOR358W HAP5 Hap5pYBL008W HIR1 Hir1pYOR038C HIR2 Hir2pYJR140C HIR3 Hir3pYOR032C HMS1 Hms1pYJR147W HMS2 Hms2pYLR113W HOG1 Hog1pYGL073W HSF1 Hsf1pYLR223C IFH1 Ifh1pYJR094C IME1 Ime1pYGL192W IME4 Ime4pYDR123C INO2 Ino2pYOL108C INO4 Ino4pYKL032C IXR1 Ixr1pYNL132W KRE33 Kre33pYGR040W KSS1 Kss1pYLR451W LEU3 Leu3pYMR021C MAC1 Mac1pYGR288W MAL13 Mal13pYBR297W MAL33 Mal33pYOR298C-A MBF1 Mbf1pYDL056W MBP1 Mbp1pYMR043W MCM1 Mcm1pYGL197W MDS3 Mds3pYIL128W MET18 Met18pYIR017C MET28 Met28pYPL038W MET31 Met31pYDR253C MET32 Met32pYNL103W MET4 Met4pYGR249W MGA1 Mga1pYGL035C MIG1 Mig1p

Page 3 of 6

Systematic Gene Name

Common Gene Name Protein Name

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

YGL209W MIG2 Mig2pYER028C MIG3 Mig3pYER068W MOT2 Mot2pYMR070W MOT3 Mot3pYOL116W MSN1 Msn1pYMR037C MSN2 Msn2pYKL062W MSN4 Msn4pYMR164C MSS11 Mss11pYDR277C MTH1 Mth1pYOR372C NDD1 Ndd1pYHR124W NDT80 Ndt80pYGR089W NNF2 Nnf2pYDR043C NRG1 Nrg1pYAL051W OAF1 Oaf1pYHL020C OPI1 Opi1pYDR081C PDC2 Pdc2pYGL013C PDR1 Pdr1pYBL005W PDR3 Pdr3pYKL043W PHD1 Phd1pYDL106C PHO2 Pho2pYFR034C PHO4 Pho4pYOR363C PIP2 Pip2pYLR014C PPR1 Ppr1pYKL015W PUT3 Put3pYNL216W RAP1 Rap1pYMR075W RCO1 Rco1pYOR380W RDR1 Rdr1pYCR106W RDS1 Rds1pYBR049C REB1 Reb1pYLR176C RFX1 Rfx1pYMR182C RGM1 Rgm1pYKL038W RGT1 Rgt1pYHL027W RIM101 Rim101pYPL089C RLM1 Rlm1pYGR044C RME1 Rme1pYPR065W ROX1 Rox1pYER169W RPH1 Rph1pYIL119C RPI1 Rpi1pYDL020C RPN4 Rpn4pYJR127C RSF2 Rsf2pYOL067C RTG1 Rtg1pYBL103C RTG3 Rtg3pYOR077W RTS2 Rts2p

Page 4 of 6

Systematic Gene Name

Common Gene Name Protein Name

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

YOR140W SFL1 Sfl1pYLR403W SFP1 Sfp1pYNL257C SIP3 Sip3pYJL089W SIP4 Sip4pYHR206W SKN7 Skn7pYNL167C SKO1 Sko1pYPR054W SMK1 Smk1pYBR182C SMP1 Smp1pYDR477W SNF1 Snf1pYGL131C SNT2 Snt2pYMR016C SOK2 Sok2pYJL127C SPT10 Spt10pYER161C SPT2 Spt2pYKL020C SPT23 Spt23pYCR018C SRD1 Srd1pYNL309W STB1 Stb1pYMR053C STB2 Stb2pYMR019W STB4 Stb4pYHR178W STB5 Stb5pYKL072W STB6 Stb6pYHR084W STE12 Ste12pYDR463W STP1 Stp1pYHR006W STP2 Stp2pYDL048C STP4 Stp4pYDR310C SUM1 Sum1pYGL162W SUT1 Sut1pYPR009W SUT2 Sut2pYER111C SWI4 Swi4pYDR146C SWI5 Swi5pYLR182W SWI6 Swi6pYBR150C TBS1 Sbs1pYBR083W TEC1 Tec1pYBR240C THI2 Thi2pYNL139C THO2 Tho2pYGL096W TOS8 Tos8pYOR344C TYE7 Tye7pYDL170W UGA3 Uga3pYDR207C UME6 Ume6pYDR213W UPC2 Upc2pYPL230W USV1 Usv1pYML076C WAR1 War1pYOR230W WTM1 Wtm1pYOR229W WTM2 Wtm2p

Page 5 of 6

Systematic Gene Name

Common Gene Name Protein Name

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

YIL101C XBP1 Xbp1pYML007W YAP1 Yap1pYHL009C YAP3 Yap3pYIR018W YAP5 Yap5pYDR259C YAP6 Yap6pYOL028C YAP7 Yap7pYBL054W YBL054W Ybl054wpYBR239C YBR239C Ybr239cpYBR267W YBR267W Ybr267wpYDR026C YDR026C Ydr026cpYDR049W YDR049W Ydr049wpYDR266C YDR266C Ydr266cpYDR520C YDR520C Ydr520cpYER051W YER051W Yer051wpYER130C YER130C Yer130cpYER184C YER184C Yer184cpYFL044C YFL044C Yfl044cpYFL052W YFL052W Yfl052wpYGR067C YGR067C Ygr067cpYDR451C YHP1 Yhp1pYJL206C YJL206C Yjl206cpYKL222C YKL222C Ykl222cpYKR064W YKR064W Ykr064wpYLR278C YLR278C Ylr278cpYML081W YML081W Yml081wpYNR063W YNR063W Ynr063wpYML027W YOX1 Yox1pYPR022C YPR022C Ypr022cpYPR196W YPR196W Ypr196wpYOR162C YRR1 Yrr1pYJL056C ZAP1 Zap1pYBL066C SEF1 Sef1pYBR066C NRG2 Nrg2pYCR065W HCM1 Hcm1pYDR006C SOK1 Sok1pYDR017C KCS1 Kcs1pYDR034C LYS14 Lys14pYDR096W GIS1 Gis1pYDR303C RSC3 Rsc3pYER164W CHD1 Chd1pYGL166W CUP2 Cup2pYHR056C RSC30 Rsc30pYIL130W ASG1 Asg1p

Page 6 of 6

Systematic Gene Name

Common Gene Name Protein Name

Supplementary Table S1: 228 yeast genes previously established as coding for transcriptional regulators.

YJL103C GSM1 Gsm1pYLR266C PDR8 Pdr8pYMR168C CEP3 Cep3pYMR213W CEF1 Cef1pYMR280C CAT8 Cat8pYOR172W YRM1 Yrm1pYOR337W TEA1 Tea1pYPL133C RDS2 Rds2pYPR186C PZF1 Pzf1pYER109C FLO8 Flo8pYMR172W HOT1 Hot1pYIR033W MGA2 Mga2pYCL055W KAR4 Kar4p

Page 1 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

A1p Mcm1p Reconstituted Complex 15118075Adr1p Snf1p Co-localization 12167649Adr1p Cat8p Co-localization 15743812Aft1p Aft1p Two-hybrid 17538022Aft1p Aft1p Affinity Capture-Western 17538022Aft1p Cbf1p Two-hybrid 16172405Aft1p Yap5p Two-hybrid 10688190Arg80p Arg81p Two-hybrid 10632874Arg80p Arg81p Two-hybrid 12138185Arg80p Arg81p Two-hybrid 10688655Arg80p Arg81p Affinity Capture-Western 10688655Arg80p Mcm1p Two-hybrid 10632874Arg80p Mcm1p Two-hybrid 12138185Arg80p Mcm1p Affinity Capture-Western 15289616Arg80p Ume6p Two-hybrid 10809695Arg81p Arg81p Two-hybrid 10632874Arg81p Mcm1p Two-hybrid 10632874Arg81p Mcm1p Two-hybrid 10688655Arg81p Mcm1p Affinity Capture-Western 10688655Arg81p Ume6p Two-hybrid 10809695Ash1p Ash1p Affinity Capture-MS 16314178Ash1p Ume6p Affinity Capture-MS 16314178Bas1p Pho2p Two-hybrid 19528318Bas1p Pho2p Affinity Capture-Western 12110691Bas1p Pho2p Two-hybrid 12110691Bas1p Pho2p Two-hybrid 11689683Bas1p Pho2p Two-hybrid 11095676Cbf1p Cbf1p PCA 18467557Cbf1p Cbf1p Reconstituted Complex 9894911Cbf1p Met28p Two-hybrid 8665859Cbf1p Met28p Reconstituted Complex 9171357Cbf1p Met32p Co-purification 18308733Cbf1p Met4p Affinity Capture-Western 18308733Cbf1p Met4p Two-hybrid 8665859Cbf1p Met4p Reconstituted Complex 9171357Cbf1p Cep3p Reconstituted Complex 11070082Crz1p Skn7p Reconstituted Complex 11432834Cst6p Kss1p Biochemical Activity 16319894Cst6p Oaf1p Affinity Capture-MS 16554755Dal80p Dal80p Two-hybrid 18719252Dal80p Dal80p Two-hybrid 9791119Dal80p Gzf3p Two-hybrid 9791119Dal80p Ydr520cp Two-hybrid 11283351

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Page 2 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Dal80p Ydr520cp Two-hybrid 18719252Dal81p Dal82p Two-hybrid 10906145Dal82p Dal82p Two-hybrid 10688190Dig1p Dig1p Two-hybrid 18719252Dig1p Gts1p Two-hybrid 18719252Dig1p Kss1p Two-hybrid 18719252Dig1p Kss1p Affinity Capture-Western 8918885Dig1p Kss1p Reconstituted Complex 14734536Dig1p Kss1p Two-hybrid 8918885Dig1p Kss1p Two-hybrid 9094309Dig1p Kss1p Biochemical Activity 8918885Dig1p Kss1p Biochemical Activity 11525741Dig1p Kss1p Affinity Capture-MS 11805837Dig1p Pho4p Affinity Capture-MS 14660704Dig1p Ste12p Affinity Capture-MS 17200106Dig1p Ste12p Two-hybrid 18719252Dig1p Ste12p FRET 19079053Dig1p Ste12p Affinity Capture-Western 8918885Dig1p Ste12p Reconstituted Complex 10825185Dig1p Ste12p Two-hybrid 9343403Dig1p Ste12p Affinity Capture-MS 12590263Dig1p Ste12p Affinity Capture-Western 9094309Dig1p Ste12p Two-hybrid 9094309Dig1p Ste12p Affinity Capture-MS 16554755Dig1p Ste12p Reconstituted Complex 16782869Dig1p Tec1p Affinity Capture-Western 19218425Dig1p Tec1p Affinity Capture-Western 16782869Dig1p Tec1p Reconstituted Complex 16782869Ecm22p Mot3p Affinity Capture-Western 16783004Esf2p Esf2p Affinity Capture-MS 15964808Esf2p Kre33p Affinity Capture-MS 16554755Fhl1p Ifh1p Two-hybrid 15620355Fhl1p Ifh1p Affinity Capture-Western 15620355Fhl1p Ifh1p Affinity Capture-Western 15692568Fhl1p Rap1p Affinity Capture-Western 17452446Fkh1p Mbp1p Affinity Capture-MS 11805837Fkh2p Mcm1p Reconstituted Complex 12711672Fkh2p Mcm1p Reconstituted Complex 10959837Fkh2p Mcm1p Reconstituted Complex 10899128Fkh2p Mcm1p Reconstituted Complex 10894549Fkh2p Ndd1p Affinity Capture-Western 12865300Fkh2p Ndd1p Reconstituted Complex 15509804Fkh2p Ndd1p Affinity Capture-Western 15509804

Page 3 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Gal3p Gal4p Reconstituted Complex 9670023Gal3p Gal80p Affinity Capture-Western 18245852Gal3p Gal80p Reconstituted Complex 18245852Gal3p Gal80p Two-hybrid 18245852Gal3p Gal80p FRET 18952899Gal3p Gal80p Affinity Capture-Western 9050845Gal3p Gal80p Affinity Capture-Western 9111326Gal3p Gal80p Affinity Capture-Western 8628318Gal3p Gal80p Reconstituted Complex 9670023Gal3p Gal80p Reconstituted Complex 15998719Gal3p Gal80p Two-hybrid 16219783Gal3p Gal80p Co-purification 11964151Gal4p Gal4p Co-crystal Structure 18611375Gal4p Gal4p Reconstituted Complex 18611375Gal4p Gal4p Co-crystal Structure 1557122Gal4p Gal80p Co-crystal Structure 18292341Gal4p Gal80p Affinity Capture-Western 11418596Gal4p Gal80p Affinity Capture-Western 7739564Gal4p Gal80p Protein-peptide 12706896Gal4p Gal80p Reconstituted Complex 10966808Gal4p Gal80p Reconstituted Complex 15998719Gal4p Gal80p Two-hybrid 11418596Gal4p Gal80p Affinity Capture-Western 10523671Gal4p Gal80p Two-hybrid 10523671Gal4p Gal80p Affinity Capture-Western 12417740Gal4p Gal80p Reconstituted Complex 10809742Gal4p Gal80p Reconstituted Complex 9670023Gal4p Gal80p Reconstituted Complex 8670900Gal4p Gal80p Two-hybrid 9159467Gal4p Gal80p Affinity Capture-Western 3316976Gal4p Gal80p Reconstituted Complex 3316976Gal4p Gal80p Reconstituted Complex 1985957Gal4p Gal80p Two-hybrid 11095729Gal4p Gal80p Reconstituted Complex 11478912Gal4p Gal80p Two-hybrid 15695361Gal4p Gal80p Affinity Capture-Western 15695361Gal4p Gal80p Co-purification 1406674Gal4p Hap5p Two-hybrid 11418596Gal4p Ino2p Reconstituted Complex 15719021Gal4p Rap1p Two-hybrid 17919657Gal4p Snf1p Reconstituted Complex 15719021Gal80p Gal80p PCA 18467557Gal80p Gal80p Reconstituted Complex 11179228

Page 4 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Gal80p Gal80p Two-hybrid 15695361Gal80p Ino2p Two-hybrid 18719252Gal80p Pho2p Two-hybrid 18719252Gal80p Put3p Two-hybrid 18719252Gal80p Rtg1p Two-hybrid 18719252Gal80p Swi5p Two-hybrid 18719252Gal80p Yer130cp Two-hybrid 18719252Gal80p Hcm1p Two-hybrid 18719252Gcn4p Gcn4p Protein-peptide 19331323Gcn4p Gcn4p Reconstituted Complex 3678204Gcn4p Mbf1p Affinity Capture-Western 9710580Gcn4p Mbf1p Reconstituted Complex 9710580Gcn4p Met31p Two-hybrid 10688190Gcr1p Gcr2p Two-hybrid 7713414Gcr1p Gcr2p Two-hybrid 1508187Gcr1p Rap1p Affinity Capture-Western 8508768Gcr1p Rap1p Reconstituted Complex 8649429Gcr1p Rap1p Reconstituted Complex 9826662Gcr1p Rap1p Two-hybrid 15300680Gln3p Gln3p Reconstituted Complex 19345193Gln3p Met4p Affinity Capture-MS 16554755Gln3p Snf1p Affinity Capture-Western 11809814Gln3p Snf1p Two-hybrid 11809814Gln3p Snf1p Biochemical Activity 11809814Gts1p Gts1p Reconstituted Complex 19345193Gts1p Rgm1p Two-hybrid 18719252Gts1p Yap6p Two-hybrid 11283351Gzf3p Gzf3p Two-hybrid 9791119Gzf3p Snf1p Biochemical Activity 16319894Gzf3p Ydr520cp Two-hybrid 18719252Gzf3p Cep3p Two-hybrid 17634282Hac1p Hac1p Two-hybrid 8932376Hal9p Tbs1p Two-hybrid 18719252Hap1p Hap1p Reconstituted Complex 10428861Hap1p Hap1p Reconstituted Complex 8464899Hap2p Hap3p Affinity Capture-MS 17200106Hap2p Hap3p Two-hybrid 11283351Hap2p Hap3p Reconstituted Complex 10972830Hap2p Hap3p Affinity Capture-MS 16554755Hap2p Hap3p Reconstituted Complex 16278450Hap2p Hap4p Reconstituted Complex 9372932Hap2p Hap4p Reconstituted Complex 16278450Hap2p Hap5p Affinity Capture-MS 17200106

Page 5 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Hap2p Hap5p Two-hybrid 11283351Hap2p Hap5p Reconstituted Complex 7828851Hap2p Hap5p Affinity Capture-MS 11805826Hap2p Hap5p Reconstituted Complex 10972830Hap2p Hap5p Affinity Capture-MS 11805837Hap2p Hap5p Affinity Capture-MS 16554755Hap2p Hap5p Reconstituted Complex 16278450Hap3p Hap4p Reconstituted Complex 9372932Hap3p Hap4p Reconstituted Complex 16278450Hap3p Hap5p Affinity Capture-MS 17200106Hap3p Hap5p Two-hybrid 11283351Hap3p Hap5p Reconstituted Complex 7828851Hap3p Hap5p Reconstituted Complex 10972830Hap3p Hap5p Affinity Capture-MS 16554755Hap4p Hap5p Reconstituted Complex 9372932Hap4p Hap5p Reconstituted Complex 16278450Hir1p Hir1p Affinity Capture-Western 9504914Hir1p Hir1p Affinity Capture-Western 9001207Hir1p Hir2p Affinity Capture-MS 17200106Hir1p Hir2p Affinity Capture-Western 9504914Hir1p Hir2p Affinity Capture-Western 9001207Hir1p Hir2p Affinity Capture-MS 16264190Hir1p Hir3p Affinity Capture-MS 16264190Hir2p Hir3p Affinity Capture-MS 17200106Hir2p Hir3p Affinity Capture-MS 16264190Hog1p Sko1p Affinity Capture-Western 11230135Hog1p Sko1p Affinity Capture-Western 12086627Hog1p Sko1p Reconstituted Complex 12086627Hog1p Sko1p Biochemical Activity 11230135Hog1p Smp1p Affinity Capture-Western 12482976Hog1p Smp1p Two-hybrid 12482976Hog1p Smp1p Biochemical Activity 12482976Hog1p Rsc3p Affinity Capture-Western 19153600Hog1p Hot1p Two-hybrid 10409737Hog1p Hot1p Two-hybrid 12743037Hog1p Hot1p Biochemical Activity 12743037Hsf1p Msn2p Co-localization 18070923Hsf1p Msn4p Co-localization 18070923Hsf1p Skn7p Affinity Capture-Western 10888672Hsf1p Snf1p Biochemical Activity 18793336Hsf1p Snf1p Two-hybrid 14612437Hsf1p Snf1p Biochemical Activity 14612437Ifh1p Rap1p Affinity Capture-Western 17452446

Page 6 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Ime1p Ime1p Two-hybrid 8628320Ime1p Ume6p Two-hybrid 10545448Ime1p Ume6p Two-hybrid 9372955Ime1p Ume6p Two-hybrid 9111339Ime1p Ume6p Two-hybrid 9889189Ime1p Ume6p Two-hybrid 8628320Ime4p Kar4p Two-hybrid 11283351Ino2p Ino4p Two-hybrid 10688190Ino2p Ino4p Two-hybrid 18542964Ino2p Ino4p Affinity Capture-Western 11071933Ino2p Ino4p Far Western 7862526Ino2p Ino4p Reconstituted Complex 10747047Ino2p Ino4p Reconstituted Complex 10361278Ino2p Ino4p Two-hybrid 11071933Ino2p Ino4p Two-hybrid 7862526Ino2p Ino4p Affinity Capture-Western 8195172Ino2p Ino4p Affinity Capture-MS 16554755Ino2p Opi1p Affinity Capture-Western 12753200Ino2p Opi1p Affinity Capture-Western 11454208Ino2p Opi1p Reconstituted Complex 11454208Ino2p Opi1p Reconstituted Complex 15819625Ino2p Opi1p Two-hybrid 15819625Ino4p Opi1p Two-hybrid 15819625Ino4p Pho4p Affinity Capture-Western 11071933Ino4p Pho4p Two-hybrid 11071933Ino4p Rtg1p Two-hybrid 11071933Ino4p Rtg3p Affinity Capture-Western 11071933Ino4p Rtg3p Two-hybrid 11071933Ino4p Tye7p Affinity Capture-Western 11071933Ino4p Tye7p Two-hybrid 11071933Kss1p Sip4p Biochemical Activity 16319894Kss1p Ste12p Two-hybrid 18719252Kss1p Ste12p Reconstituted Complex 14734536Kss1p Ste12p Reconstituted Complex 9744865Kss1p Ste12p Two-hybrid 7851759Kss1p Ste12p Reconstituted Complex 9393860Kss1p Ste12p Biochemical Activity 11525741Kss1p Ste12p Affinity Capture-MS 11805837Kss1p Tec1p Affinity Capture-Western 15558284Kss1p Tec1p Affinity Capture-MS 11805837Kss1p Asg1p Biochemical Activity 16319894Mac1p Mac1p Two-hybrid 18719252Mac1p Mac1p Two-hybrid 10506178

Page 7 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Mac1p Mac1p Two-hybrid 9867833Mac1p Mac1p Two-hybrid 11297731Mbp1p Skn7p Reconstituted Complex 10512874Mbp1p Skn7p Two-hybrid 10512874Mbp1p Swi6p Affinity Capture-MS 17200106Mbp1p Swi6p Two-hybrid 19820714Mbp1p Swi6p Affinity Capture-Western 19820714Mbp1p Swi6p Reconstituted Complex 19820714Mbp1p Swi6p Affinity Capture-Western 8649372Mbp1p Swi6p Two-hybrid 10512874Mbp1p Swi6p Affinity Capture-MS 11805826Mbp1p Swi6p Affinity Capture-MS 16554755Mcm1p Mcm1p Two-hybrid 10632874Mcm1p Ste12p Reconstituted Complex 1756728Mcm1p Ste12p Reconstituted Complex 8139556Mcm1p Yhp1p Affinity Capture-Western 12464633Mcm1p Yox1p Affinity Capture-Western 12464633Met28p Met4p Affinity Capture-Western 17157252Met28p Met4p Two-hybrid 9799240Met28p Met4p Reconstituted Complex 9171357Met28p Met4p Two-hybrid 8665859Met31p Met4p Affinity Capture-Western 17157252Met31p Met4p Two-hybrid 9799240Met31p Met4p Reconstituted Complex 9799240Met32p Met4p Two-hybrid 9799240Met32p Met4p Reconstituted Complex 9799240Met4p Met4p Two-hybrid 11087867Mig1p Snf1p Affinity Capture-Western 17178716Mig1p Snf1p Biochemical Activity 12748292Mig1p Snf1p Affinity Capture-Western 9774644Mig1p Snf1p Two-hybrid 9774644Mig1p Snf1p Biochemical Activity 12684376Mig1p Snf1p Biochemical Activity 12393914Mig1p Snf1p Biochemical Activity 10403407Mig1p Snf1p Biochemical Activity 16319894Mig1p Snf1p Biochemical Activity 16847059Mig3p Snf1p Biochemical Activity 14993292Mot2p Mot2p Affinity Capture-Western 19707589Mot3p Mot3p PCA 18467557Mot3p Mot3p Reconstituted Complex 19345193Msn1p Ydr520cp Two-hybrid 18719252Msn1p Ygr067cp Two-hybrid 18719252Msn2p Snf1p Biochemical Activity 16281053

Page 8 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Msn2p Sok2p Affinity Capture-Western 11238897Mss11p Ste12p Affinity Capture-Western 15485921Mss11p Tec1p Affinity Capture-Western 15485921Mss11p Flo8p Affinity Capture-Western 15485921Mth1p Rgt1p Affinity Capture-Western 14508605Mth1p Rgt1p Two-hybrid 14508605Mth1p Rgt1p Two-hybrid 15489524Mth1p Rgt1p Reconstituted Complex 15489524Nnf2p Nnf2p Two-hybrid 11087867Nnf2p Swi5p Two-hybrid 11087867Nrg1p Rim101p Affinity Capture-Western 16024810Nrg1p Snf1p Affinity Capture-Western 11404322Oaf1p Oaf1p Reconstituted Complex 18671944Oaf1p Oaf1p Co-localization 9288897Oaf1p Pip2p Reconstituted Complex 18671944Oaf1p Pip2p Co-localization 18285336Oaf1p Pip2p Affinity Capture-Western 8972187Oaf1p Pip2p Reconstituted Complex 12709061Oaf1p Pip2p Co-localization 9288897Pdr1p Pdr1p Reconstituted Complex 19345193Pdr1p Pdr3p Co-fractionation 12453227Pdr1p Stb5p Affinity Capture-Western 15123673Pdr1p Stb5p Reconstituted Complex 15123673Pho2p Pho4p Two-hybrid 19528318Pho2p Pho4p Affinity Capture-Western 19528318Pho2p Pho4p Affinity Capture-Western 9354395Pho2p Pho4p Reconstituted Complex 12136204Pho2p Pho4p Reconstituted Complex 7957107Pho2p Pho4p Two-hybrid 12219216Pho2p Pho4p Two-hybrid 8676879Pho2p Pho4p Reconstituted Complex 10884387Pho2p Pho4p Two-hybrid 10884387Pho2p Pho4p Reconstituted Complex 10320381Pho2p Swi5p Reconstituted Complex 9111337Pho2p Swi5p Reconstituted Complex 7493941Pho2p Swi5p Reconstituted Complex 8355698Pho2p Swi5p Reconstituted Complex 7902583Pho2p Swi5p Reconstituted Complex 9774660Pho2p Swi5p Two-hybrid 9774660Pho4p Pho4p Two-hybrid 18719252Pho4p Pho4p Reconstituted Complex 9443961Pho4p Pho4p Protein-peptide 11967834Pho4p Pho4p Co-crystal Structure 9303313

Page 9 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Pho4p Snf1p Affinity Capture-MS 14660704Ppr1p Ppr1p Co-crystal Structure 7958913Put3p Put3p Co-crystal Structure 9303003Put3p Put3p Co-crystal Structure 9303004Put3p Put3p Reconstituted Complex 8846888Rco1p Rco1p Affinity Capture-MS 16286008Rdr1p Rdr1p Two-hybrid 11283351Reb1p Reb1p Affinity Capture-MS 14759368Reb1p Rfx1p Affinity Capture-MS 11805826Reb1p Rsc3p Affinity Capture-MS 11805826Reb1p Rsc3p Affinity Capture-MS 16429126Rfx1p Rsc3p Affinity Capture-MS 11805826Rgt1p Rgt1p Two-hybrid 15489524Rim101p Zap1p Two-hybrid 10688190Rlm1p Rlm1p Reconstituted Complex 19345193Rlm1p Smp1p Affinity Capture-Western 9121433Rtg1p Rtg3p Affinity Capture-MS 17200106Rtg1p Rtg3p Two-hybrid 9242640Rtg1p Rtg3p Affinity Capture-Western 10848632Rtg1p Rtg3p Reconstituted Complex 9032238Rtg1p Rtg3p Affinity Capture-MS 16554755Sfl1p Sfl1p Affinity Capture-Western 12024012Sip3p Snf1p Two-hybrid 8127709Sip3p Snf1p Two-hybrid 1496382Sip4p Sip4p Two-hybrid 11486018Sip4p Snf1p Affinity Capture-Western 10581241Sip4p Snf1p Two-hybrid 11486018Sip4p Snf1p Two-hybrid 10581241Sip4p Snf1p Two-hybrid 8628258Sip4p Cat8p Two-hybrid 11486018Skn7p Skn7p Affinity Capture-Western 10888672Skn7p Skn7p Two-hybrid 8598053Skn7p Skn7p Protein-peptide 11967834Sko1p Sko1p Affinity Capture-Western 11500510Smk1p Yap7p Affinity Capture-MS 16554755Snf1p Snf1p Co-crystal Structure 16531232Snf1p Snf1p Reconstituted Complex 16531232Snf1p Snf1p Affinity Capture-Western 16531232Snf1p Snf1p Biochemical Activity 12748292Snf1p Nrg2p Affinity Capture-Western 11404322Snf1p Nrg2p Two-hybrid 11404322Snf1p Gsm1p Biochemical Activity 16319894Snf1p Rds2p Biochemical Activity 17875938

Page 10 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Snf1p Rds2p Biochemical Activity 16319894Snt2p Xbp1p Affinity Capture-MS 16554755Sok2p Sok2p Reconstituted Complex 19345193Spt2p Spt2p Affinity Capture-Western 9632800Spt23p Spt23p Two-hybrid 11733065Spt23p Spt23p Affinity Capture-Western 11733065Spt23p Yfl044cp Affinity Capture-Western 16427015Stb1p Swi6p Affinity Capture-Western 18794370Stb1p Swi6p Affinity Capture-Western 10409718Stb1p Swi6p Reconstituted Complex 12832490Stb4p Swi5p Affinity Capture-MS 11805837Stb5p Stb5p Affinity Capture-Western 15123673Ste12p Tec1p Reconstituted Complex 9036858Ste12p Tec1p Reconstituted Complex 9234690Ste12p Tec1p Affinity Capture-MS 11805837Ste12p Tec1p Affinity Capture-Western 16782869Ste12p Tec1p Reconstituted Complex 16782869Ste12p Flo8p Affinity Capture-Western 15485921Sum1p Sum1p Affinity Capture-Western 18268008Swi4p Swi4p Reconstituted Complex 10490612Swi4p Swi6p Affinity Capture-MS 17200106Swi4p Swi6p Two-hybrid 19820714Swi4p Swi6p Affinity Capture-Western 19820714Swi4p Swi6p Reconstituted Complex 19820714Swi4p Swi6p Affinity Capture-Western 12024050Swi4p Swi6p Affinity Capture-Western 8649372Swi4p Swi6p Reconstituted Complex 10490612Swi4p Swi6p Reconstituted Complex 9521763Swi4p Swi6p Reconstituted Complex 2649246Swi4p Swi6p Reconstituted Complex 1465410Swi4p Swi6p Affinity Capture-MS 16554755Swi4p Swi6p Affinity Capture-MS 16429126Tec1p Flo8p Affinity Capture-Western 15485921Tho2p Tho2p Affinity Capture-MS 14759368Ume6p Ume6p Affinity Capture-MS 16314178Ume6p Tea1p Two-hybrid 11238941Wtm1p Wtm1p Affinity Capture-Western 9234739Wtm1p Wtm1p Two-hybrid 9234739Wtm1p Wtm2p Two-hybrid 18719252Wtm1p Wtm2p Affinity Capture-Western 9234739Wtm1p Wtm2p Two-hybrid 9234739Wtm1p Wtm2p Affinity Capture-MS 11805837Wtm2p Wtm2p PCA 18467557

Page 11 of 11

Transcriptional Regulatory Protein

Transcriptional Regulatory Protein Experimental Evidence PubMed ID

Supplementary Table S2: Physical protein-protein interactions between yeast transcriptional regulators extracted from BioGRID database.

Wtm2p Wtm2p Two-hybrid 18719252Wtm2p Wtm2p Affinity Capture-Western 9234739Wtm2p Wtm2p Two-hybrid 9234739Yap5p Yap5p Protein-peptide 11967834Ybr239cp Rds2p Two-hybrid 11283351Ybr267wp Ybr267wp Affinity Capture-MS 14759368Ybr267wp Ybr267wp Affinity Capture-Western 16651379Ydr266cp Ydr266cp Protein-peptide 11967834Ydr520cp Ydr520cp Two-hybrid 18719252Ydr520cp Ydr520cp Protein-peptide 11967834Yer184cp Yer184cp Two-hybrid 18719252Ypr022cp Ypr022cp Reconstituted Complex 19345193Yrr1p Yrr1p Two-hybrid 18719252Yrr1p Yrr1p Affinity Capture-Western 15123673Yrr1p Yrr1p Reconstituted Complex 15123673Gis1p Mga2p Two-hybrid 17043893Rsc3p Rsc3p Affinity Capture-MS 14759368Rsc3p Rsc30p Affinity Capture-MS 17200106Rsc3p Rsc30p Affinity Capture-MS 16429126Rsc3p Rsc30p Affinity Capture-Western 16204215Chd1p Chd1p Affinity Capture-MS 14759368Cep3p Cep3p Co-crystal Structure 18064045Cep3p Cep3p Co-purification 10352012Cef1p Cef1p Two-hybrid 10092627Cat8p Cat8p Two-hybrid 11486018Cat8p Cat8p Protein-peptide 11967834

Page 1 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Arr1p YCR040W 15343339Cha4p YCR040W 15343339Dal80p YCR040W 15343339Gat3p YCR040W 12399584Gzf3p YCR040W 15343339Hap4p YCR040W 15343339Rap1p YCR040W 3315231Stp2p YCR040W 15343339Uga3p YCR040W 15343339Yap5p YCR040W 15343339Yap6p YCR040W 12399584Zap1p YCR040W 15343339Abf1p YKL112W 15192094Mal33p YKL112W 15343339Mbp1p YKL112W 16709784Ash1p YER045C 12399584Cad1p YER045C 16709784Cin5p YER045C 16709784Fhl1p YER045C 17646381Hap5p YER045C 15343339Mbp1p YER045C 12399584Mga1p YER045C 16449570Nrg1p YER045C 15343339Phd1p YER045C 16449570Pho2p YER045C 15343339Rap1p YER045C 17646381Rim101p YER045C 15343339Rox1p YER045C 12399584Sok2p YER045C 16449570Ste12p YER045C 19159457Xbp1p YER045C 15343339Yap6p YER045C 15343339Flo8p YER045C 16449570Fkh1p YLR131C 16709784Fkh2p YLR131C 16709784Ino4p YLR131C 16709784Mcm1p YLR131C 16709784Rap1p YLR131C 16709784Zap1p YLR131C 12399584Fhl1p YDR216W 17646381Msn4p YDR216W 12399584Pho4p YDR216W 19108609Rgt1p YDR216W 12399584

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Page 2 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Ste12p YDR216W 17638031Stp2p YDR216W 16709784Yhp1p YDR216W 12464632Gsm1p YDR216W 16785442Aft1p YGL071W 15343339Aft2p YGL071W 15343339Fhl1p YGL071W 17646381Phd1p YGL071W 16449570Put3p YGL071W 15343339Rap1p YGL071W 17646381Ino4p YPL202C 16709784Ndt80p YPL202C 15343339Skn7p YPL202C 15343339Yap1p YPL202C 18627600Fhl1p YMR042W 17646381Ste12p YMR042W 19159457Arg80p YML099C 12399584Arg81p YML099C 15343339Dal82p YML099C 16709784Gat1p YML099C 15343339Aro80p YDR421W 16709784Cin5p YDR421W 12399584Yap1p YDR421W 18627600Ace2p YKL185W 17898805Mcm1p YKL185W 18303948Smp1p YKL185W 15343339Sok2p YKL185W 17638031Swi5p YKL185W 17898805Cin5p YGR097W 12399584Phd1p YGR097W 16449570Ste12p YGR097W 19159457Yap6p YGR097W 12399584Rap1p YOR113W 17646381Gat1p YKR099W 15343339Rtg1p YKR099W 12399584Sip4p YKR099W 15343339Yap7p YKR099W 15343339Abf1p YKL005C 18305101Dot6p YKL005C 16709784Gts1p YKL005C 15343339Abf1p YDR423C 16709784Yap5p YDR423C 12464632Cbf1p YJR060W 15343339

Page 3 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Cin5p YJR060W 12399584Pho4p YJR060W 19108609Tos8p YJR060W 12464632Mss11p YLR098C 12399584Stb5p YLR098C 16914749Cin5p YOR028C 15343339Mga1p YOR028C 16449570Phd1p YOR028C 16449570Sko1p YOR028C 18931682Sok2p YOR028C 16449570Ste12p YOR028C 16449570Tec1p YOR028C 16449570Yap1p YOR028C 18627600Yap6p YOR028C 15343339Flo8p YOR028C 16449570Pho2p YNL027W 15343339Pho4p YNL027W 15343339Rap1p YNL027W 17646381Abf1p YIL036W 16709784Ino4p YIL036W 16709784Pho4p YIL036W 15343339Put3p YIL036W 12399584Ste12p YIL036W 15343339Arg80p YPL177C 15343339Arg81p YPL177C 15343339Fhl1p YPL177C 17646381Fkh2p YPL177C 12399584Mac1p YPL177C 16709784Mga1p YPL177C 16449570Phd1p YPL177C 16449570Pho4p YPL177C 19108609Sok2p YPL177C 16449570Stb5p YPL177C 16914749Ste12p YPL177C 17638031Tec1p YPL177C 16449570Flo8p YPL177C 16449570Gln3p YKR034W 9171383Ste12p YKR034W 16449570Sum1p YKR034W 12399584Tec1p YKR034W 16449570Abf1p YIR023W 16709784Yap6p YIR023W 15343339Mga1p YPL049C 16449570

Page 4 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Phd1p YPL049C 16449570Sok2p YPL049C 17638031Ste12p YPL049C 17638031Tos8p YPL049C 12464632Fhl1p YER088C 17646381Rap1p YER088C 17646381Skn7p YER088C 12399584Sok2p YER088C 16449570Stb5p YER088C 16914749Cin5p YLR228C 16709784Sok2p YLR228C 17638031Ste12p YLR228C 17638031Hcm1p YLR228C 12464632Hal9p YBR033W 12399584Rap1p YBR033W 17646381Rgt1p YBR033W 12399584Rph1p YBR033W 15343339Dig1p YNR054C 12399584Hap3p YNR054C 12399584Hap5p YNR054C 12399584Hms2p YNR054C 15343339Ppr1p YNR054C 15343339Sfp1p YNR054C 12399584Ste12p YNR054C 17638031Swi6p YNR054C 12399584Upc2p YNR054C 15343339Hcm1p YNR054C 12464632Abf1p YPR104C 18305101Fkh1p YPR104C 15343339Msn4p YPR104C 12399584Fkh2p YIL131C 15343339Fhl1p YNL068C 17646381Fkh1p YNL068C 11562353Fkh2p YNL068C 18057023Rap1p YNL068C 17646381Abf1p YGL254W 18305101Cin5p YGL254W 12399584Gcr2p YGL254W 15343339Gln3p YGL254W 15343339Met31p YGL254W 15343339Met32p YGL254W 15343339Put3p YGL254W 15343339Rtg1p YGL254W 15343339

Page 5 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Swi4p YGL254W 12399584Gal4p YDR009W 16709784Mig1p YDR009W 8114729Pdr3p YDR009W 16914749Ste12p YDR009W 19159457Mig1p YPL248C 8114729Stp1p YPL248C 12399584Stp2p YPL248C 12399584Tos8p YPL248C 12464632Fkh2p YML051W 12399584Gal4p YML051W 16709784Ace2p YFL021W 15343339Dal80p YFL021W 8622686Dal81p YFL021W 15343339Dal82p YFL021W 15343339Fhl1p YFL021W 17646381Fkh2p YFL021W 15343339Gln3p YFL021W 8622686Hap2p YFL021W 15343339Pho4p YFL021W 19108609Rap1p YFL021W 17646381Skn7p YFL021W 15343339Smp1p YFL021W 12399584Ste12p YFL021W 19159457Swi5p YFL021W 15343339Ume6p YFL021W 15343339Yap5p YFL021W 12464632Nrg1p YLR013W 16709784Rap1p YLR013W 16709784Swi5p YLR013W 15343339Ume6p YLR013W 15343339Fhl1p YEL009C 17646381Gln3p YEL009C 15343339Hap2p YEL009C 15343339Hap4p YEL009C 12399584Mga1p YEL009C 16449570Rap1p YEL009C 17646381Sok2p YEL009C 17638031Ste12p YEL009C 17638031Stp1p YEL009C 12399584Tec1p YEL009C 17638031Yap1p YEL009C 18627600Flo8p YEL009C 16449570

Page 6 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Rap1p YPL075W 17646381Ste12p YPL075W 19159457Sum1p YPL075W 16709784Yox1p YPL075W 12464632Ino4p YNL199C 16709784Aft1p YER040W 15343339Gcn4p YER040W 15343339Gln3p YER040W 9171383Mal33p YER040W 16709784Mig1p YER040W 16709784Rap1p YER040W 16709784Reb1p YER040W 16709784Aft1p YGL181W 15343339Aft2p YGL181W 15343339Dal82p YGL181W 16709784Put3p YGL181W 15343339Rap1p YGL181W 17646381Reb1p YGL181W 16709784Abf1p YJL110C 18305101Gat1p YJL110C 15343339Sip4p YJL110C 12399584Tos8p YJL110C 12464632Rap1p YPR008W 17646381Skn7p YPR008W 12399584Ste12p YPR008W 19159457Swi4p YPR008W 12399584Cbf1p YFL031W 15343339Hac1p YFL031W 15009095Spt23p YFL031W 16543154Ste12p YFL031W 19159457Mga2p YFL031W 16543154Dot6p YOL089C 16709784Fhl1p YLR256W 17646381Hap1p YLR256W 17706600Mcm1p YLR256W 18303948Rap1p YLR256W 17646381Sok2p YLR256W 17638031Ste12p YLR256W 19159457Swi4p YLR256W 16709784Yap1p YLR256W 18627600Aft1p YGL237C 15343339Met32p YGL237C 15343339Oaf1p YGL237C 15343339

Page 7 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Reb1p YGL237C 16709784Rap1p YBL021C 11455386Cin5p YKL109W 16709784Hir2p YKL109W 16709784Mga1p YKL109W 16449570Mig1p YKL109W 8114729Nrg1p YKL109W 15343339Pdr1p YKL109W 16914749Phd1p YKL109W 16449570Rox1p YKL109W 15343339Skn7p YKL109W 15343339Sok2p YKL109W 16449570Stb5p YKL109W 16914749Ste12p YKL109W 16449570Swi4p YKL109W 16709784Tec1p YKL109W 16449570Yap1p YKL109W 18627600Gsm1p YKL109W 16785442Cat8p YKL109W 12125049Rds2p YKL109W 17875938Flo8p YKL109W 16449570Dot6p YOR358W 16709784Fkh1p YOR358W 15343339Rap1p YOR358W 17646381Yox1p YOR358W 12464632Cad1p YBL008W 15343339Spt23p YBL008W 15343339Cbf1p YOR038C 16709784Sok2p YOR038C 12464632Tye7p YOR038C 15343339Yox1p YOR038C 12464632Adr1p YJR140C 15343339Rap1p YJR140C 17646381Cup9p YOR032C 12399584Fhl1p YOR032C 17646381Hal9p YOR032C 12399584Mga1p YOR032C 16449570Nrg1p YOR032C 12399584Phd1p YOR032C 16449570Rap1p YOR032C 17646381Rox1p YOR032C 15343339Sfp1p YOR032C 12399584Sok2p YOR032C 16449570

Page 8 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Ste12p YOR032C 16449570Tec1p YOR032C 16449570Tos8p YOR032C 12464632Yap6p YOR032C 12399584Flo8p YOR032C 16449570Ace2p YJR147W 15343339Cbf1p YJR147W 15343339Cin5p YJR147W 16709784Fhl1p YJR147W 17646381Gcn4p YJR147W 17224918Rap1p YJR147W 17646381Skn7p YJR147W 12399584Swi4p YJR147W 12399584Xbp1p YJR147W 15343339Yap1p YJR147W 18627600Yap6p YJR147W 15343339Aft1p YLR113W 15343339Fhl1p YLR113W 17646381Fkh1p YLR113W 12399584Gat1p YLR113W 16709784Ino4p YLR113W 16709784Mcm1p YLR113W 18303948Pdr1p YLR113W 17158869Phd1p YLR113W 16449570Rap1p YLR113W 17646381Reb1p YLR113W 16709784Rtg3p YLR113W 12399584Sok2p YLR113W 16449570Stb5p YLR113W 16914749Ste12p YLR113W 16449570Swi4p YLR113W 16709784Swi6p YLR113W 16709784Tec1p YLR113W 17638031Yap1p YLR113W 18627600Flo8p YLR113W 16449570Rlm1p YLR113W 16709784Fhl1p YGL073W 17646381Rap1p YGL073W 17646381Ifh1p YLR223C 15616569Rap1p YLR223C 17646381Reb1p YLR223C 15343339Yap5p YLR223C 18287073Adr1p YJR094C 15743812

Page 9 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Cin5p YJR094C 16709784Fkh1p YJR094C 12399584Mcm1p YJR094C 18303948Mga1p YJR094C 16449570Msn2p YJR094C 9528770Msn4p YJR094C 9528770Nrg1p YJR094C 16709784Phd1p YJR094C 16449570Rap1p YJR094C 16709784Rme1p YJR094C 7851756Rox1p YJR094C 12399584Sok2p YJR094C 16449570Ste12p YJR094C 17638031Tec1p YJR094C 17638031Xbp1p YJR094C 15343339Yap6p YJR094C 16709784Yhp1p YJR094C 10705372Flo8p YJR094C 16449570Abf1p YGL192W 16709784Dot6p YGL192W 16709784Hap4p YGL192W 12399584Reb1p YGL192W 12399584Tec1p YGL192W 15343339Dal82p YDR123C 12399584Hsf1p YDR123C 15343339Ino4p YDR123C 16709784Dot6p YOL108C 16709784Ino4p YOL108C 16709784Tos8p YOL108C 12464632Fhl1p YKL032C 17646381Mcm1p YKL032C 18303948Phd1p YKL032C 16449570Rap1p YKL032C 17646381Sok2p YKL032C 16449570Ste12p YKL032C 17638031Tec1p YKL032C 16449570Flo8p YKL032C 16449570Ino4p YNL132W 16709784Phd1p YNL132W 16449570Ste12p YNL132W 16449570Tec1p YNL132W 16449570Yap6p YNL132W 18287073Ace2p YGR040W 12399584

Page 10 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Cst6p YGR040W 15343339Fkh1p YGR040W 12399584Ino4p YGR040W 16709784Msn4p YGR040W 15343339Ste12p YGR040W 17638031Swi4p YGR040W 12399584Swi5p YGR040W 12399584Swi6p YGR040W 12399584Tec1p YGR040W 17638031Yap1p YGR040W 18627600Ash1p YLR451W 16709784Gcn4p YLR451W 15343339Pho4p YLR451W 12399584Rsf2p YLR451W 12399584Dal82p YMR021C 12399584Leu3p YMR021C 16923194Sok2p YGR288W 17638031Ste12p YGR288W 19159457Hcm1p YGR288W 12464632Mac1p YBR297W 15343339Mbp1p YBR297W 11206552Hsf1p YOR298C-A 12399584Mth1p YOR298C-A 12399584Sok2p YOR298C-A 16449570Tec1p YOR298C-A 16449570Cup9p YDL056W 12399584Hal9p YDL056W 12399584Hms1p YDL056W 12399584Pho2p YDL056W 16709784Rap1p YDL056W 16709784Rfx1p YDL056W 12399584Fhl1p YMR043W 17646381Oaf1p YMR043W 15343339Pdr1p YIL128W 15343339Swi5p YIL128W 15343339Yap5p YIL128W 16709784Cbf1p YIR017C 15343339Gcn4p YIR017C 17224918Hsf1p YIR017C 12399584Met28p YIR017C 12399584Met31p YIR017C 9799240Met32p YIR017C 9799240Met4p YIR017C 12399584

Page 11 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Pdr1p YIR017C 16914749Ste12p YIR017C 19159457Tec1p YIR017C 16449570Yap6p YIR017C 12399584Gat1p YDR253C 16709784Leu3p YDR253C 16923194Mal33p YDR253C 16709784Mcm1p YDR253C 16709784Met4p YDR253C 16709784Cbf1p YNL103W 15343339Fhl1p YNL103W 17646381Gcn4p YNL103W 17224918Ino4p YNL103W 16709784Sfp1p YNL103W 12399584Yap1p YNL103W 18627600Cad1p YGR249W 16709784Cin5p YGR249W 16709784Fhl1p YGR249W 17646381Hms2p YGR249W 15343339Hsf1p YGR249W 15343339Mga1p YGR249W 16449570Msn2p YGR249W 15343339Nrg1p YGR249W 16709784Pdr1p YGR249W 16914749Pdr3p YGR249W 16914749Phd1p YGR249W 16449570Rap1p YGR249W 16709784Rim101p YGR249W 15343339Sfl1p YGR249W 12399584Skn7p YGR249W 15343339Sko1p YGR249W 18931682Sok2p YGR249W 16449570Ste12p YGR249W 16449570Swi4p YGR249W 16709784Tec1p YGR249W 16449570Xbp1p YGR249W 15343339Yap5p YGR249W 12399584Yap6p YGR249W 15343339Flo8p YGR249W 16449570Rlm1p YGR249W 12399584Mig1p YGL035C 8114729Pdr3p YGL209W 16914749Rgt1p YGL209W 14871952

Page 12 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Sok2p YGL209W 17638031Ste12p YGL209W 16449570Hcm1p YGL209W 12464632Cin5p YER028C 16709784Nrg1p YER028C 15343339Phd1p YER028C 16449570Rgt1p YER028C 15343339Sok2p YER028C 16449570Ste12p YER028C 19159457Flo8p YER028C 16449570Aft1p YER068W 15343339Arg80p YER068W 12399584Arg81p YER068W 12399584Ecm22p YER068W 16709784Gcn4p YER068W 12399584Ino4p YER068W 16709784Msn2p YER068W 15343339Msn4p YER068W 15343339Reb1p YER068W 15343339Fhl1p YMR070W 17646381Hap1p YMR070W 15343339Ino2p YMR070W 15343339Mbp1p YMR070W 15343339Nrg1p YMR070W 15343339Pdr1p YMR070W 16914749Phd1p YMR070W 16449570Skn7p YMR070W 15343339Sko1p YMR070W 16709784Sok2p YMR070W 16449570Stb5p YMR070W 16914749Swi4p YMR070W 16709784Flo8p YMR070W 16449570Abf1p YOL116W 16709784Arg80p YOL116W 12399584Cin5p YOL116W 15343339Dot6p YOL116W 16709784Yap1p YOL116W 12399584Yap5p YOL116W 12399584Cbf1p YMR037C 15343339Gcr2p YMR037C 15343339Hap1p YMR037C 15343339Skn7p YMR037C 15343339Sko1p YMR037C 16709784

Page 13 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Sok2p YMR037C 17638031Ste12p YMR037C 17638031Tec1p YMR037C 17638031Tye7p YMR037C 15343339Fhl1p YKL062W 17646381Hal9p YKL062W 16709784Hap5p YKL062W 15343339Hir2p YKL062W 12399584Ino2p YKL062W 12399584Msn2p YKL062W 15343339Msn4p YKL062W 12399584Pdr1p YKL062W 16914749Phd1p YKL062W 15343339Rap1p YKL062W 17646381Skn7p YKL062W 15343339Sok2p YKL062W 17638031Stb5p YKL062W 16914749Cin5p YMR164C 15343339Sko1p YMR164C 16709784Ste12p YMR164C 16449570Tec1p YMR164C 17638031Dal81p YDR277C 12399584Gal4p YDR277C 15343339Hir2p YDR277C 12399584Ino4p YDR277C 16709784Rap1p YDR277C 17646381Rgt1p YDR277C 14871952Ste12p YDR277C 19159457Hcm1p YDR277C 12464632Fkh2p YOR372C 12399584Stb1p YOR372C 12399584Swi4p YOR372C 16709784Swi6p YOR372C 16709784Ume6p YHR124W 15343339Gcr2p YGR089W 12399584Ino4p YGR089W 16709784Msn2p YGR089W 15343339Msn4p YGR089W 15343339Rim101p YGR089W 12399584Sfl1p YGR089W 12399584Ste12p YGR089W 19159457Rlm1p YGR089W 12399584Cin5p YDR043C 18287073

Page 14 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Nrg1p YDR043C 16709784Rap1p YDR043C 16709784Rim101p YDR043C 12509465Skn7p YDR043C 15343339Sok2p YDR043C 17638031Ste12p YDR043C 17638031Tec1p YDR043C 17638031Rap1p YAL051W 17646381Ino2p YHL020C 15343339Ino4p YHL020C 16709784Rds2p YHL020C 17875938Dot6p YGL013C 16709784Ecm22p YGL013C 16709784Mbp1p YGL013C 15343339Met31p YGL013C 15343339Nrg1p YGL013C 15343339Rox1p YGL013C 15343339Rpn4p YGL013C 15343339Ste12p YGL013C 19159457Swi4p YGL013C 16709784Hsf1p YBL005W 16556235Pdr1p YBL005W 16914749Pdr3p YBL005W 16914749Rph1p YBL005W 12399584Cin5p YKL043W 16709784Fhl1p YKL043W 17646381Ixr1p YKL043W 16709784Mga1p YKL043W 16449570Phd1p YKL043W 16449570Sko1p YKL043W 16709784Sok2p YKL043W 16449570Ste12p YKL043W 16449570Swi4p YKL043W 16709784Swi6p YKL043W 16709784Tec1p YKL043W 16449570Yap1p YKL043W 18627600Yox1p YKL043W 12464632Flo8p YKL043W 16449570Abf1p YDL106C 18305101Ino4p YDL106C 16709784Pho4p YDL106C 19108609Usv1p YDL106C 12399584Fhl1p YFR034C 17646381

Page 15 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Rap1p YFR034C 17646381Yox1p YFR034C 12464632Flo8p YFR034C 16449570Adr1p YOR363C 18285336Oaf1p YOR363C 18285336Pip2p YOR363C 18285336Ste12p YLR014C 16449570Abf1p YKL015W 15343339Adr1p YKL015W 15343339Cbf1p YKL015W 15343339Gcn4p YKL015W 16709784Hap2p YKL015W 12399584Hap3p YKL015W 12399584Hap4p YKL015W 15343339Hap5p YKL015W 12399584Rap1p YKL015W 17646381Rpn4p YKL015W 15343339Tye7p YKL015W 15343339Rap1p YNL216W 17646381Reb1p YNL216W 2361590Ste12p YNL216W 15343339Ste12p YMR075W 17638031Cin5p YOR380W 12399584Pdr1p YOR380W 12399584Yap6p YOR380W 12399584Cbf1p YCR106W 15343339Crz1p YCR106W 12399584Gal4p YCR106W 12399584Gat3p YCR106W 12399584Rds1p YCR106W 15343339Rgm1p YCR106W 12399584Yap1p YCR106W 18627600Yap5p YCR106W 12399584Yap6p YCR106W 15343339Cbf1p YBR049C 15343339Hsf1p YBR049C 16709784Reb1p YBR049C 2204808Rpn4p YBR049C 15343339Ste12p YBR049C 19159457Fhl1p YLR176C 17646381Oaf1p YLR176C 15343339Rfx1p YLR176C 9741624Aft1p YMR182C 15343339

Page 16 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Fhl1p YMR182C 17646381Mac1p YMR182C 12399584Reb1p YMR182C 15343339Ste12p YMR182C 19159457Mbp1p YKL038W 11206552Abf1p YHL027W 15343339Gcn4p YHL027W 15343339Smp1p YHL027W 15343339Sok2p YHL027W 17638031Ste12p YHL027W 19159457Swi4p YHL027W 16709784Tos8p YHL027W 12464632Cad1p YPL089C 18287073Cin5p YPL089C 18287073Fhl1p YPL089C 17646381Rap1p YPL089C 17646381Ste12p YPL089C 19159457Yap5p YPL089C 18287073Yap6p YPL089C 18287073Rds2p YGR044C 17875938Cin5p YPR065W 16709784Fhl1p YPR065W 17646381Hap1p YPR065W 15343339Msn4p YPR065W 15343339Pdr1p YPR065W 17158869Rim101p YPR065W 15343339Rox1p YPR065W 15343339Skn7p YPR065W 15343339Sko1p YPR065W 16087739Sok2p YPR065W 16449570Ste12p YPR065W 17638031Tec1p YPR065W 17638031Yap1p YPR065W 16709784Yap6p YPR065W 15343339Flo8p YPR065W 16449570Rap1p YER169W 17646381Sok2p YER169W 17638031Ste12p YER169W 19159457Cad1p YIL119C 16709784Cin5p YIL119C 16709784Cup9p YIL119C 15343339Fhl1p YIL119C 17646381Gln3p YIL119C 15343339

Page 17 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Hms2p YIL119C 15343339Ino4p YIL119C 16709784Ixr1p YIL119C 15343339Mga1p YIL119C 16449570Mot3p YIL119C 15343339Nrg1p YIL119C 16709784Pdr1p YIL119C 16914749Pdr3p YIL119C 16914749Phd1p YIL119C 16449570Rap1p YIL119C 16709784Rim101p YIL119C 15343339Rox1p YIL119C 15343339Skn7p YIL119C 15343339Sko1p YIL119C 16087739Sok2p YIL119C 16449570Stb5p YIL119C 16914749Ste12p YIL119C 16449570Tec1p YIL119C 16449570Xbp1p YIL119C 15343339Yap1p YIL119C 18627600Yap6p YIL119C 16709784Hcm1p YIL119C 12464632Flo8p YIL119C 16449570Rlm1p YIL119C 12399584Abf1p YDL020C 18305101Cbf1p YDL020C 15343339Hsf1p YDL020C 16709784Msn4p YDL020C 15343339Pdr1p YDL020C 16914749Pdr3p YDL020C 16914749Yap1p YDL020C 16709784Yap7p YDL020C 15343339Abf1p YJR127C 15343339Aft1p YJR127C 15343339Fkh2p YJR127C 16709784Ino4p YJR127C 16709784Mga1p YJR127C 16449570Phd1p YJR127C 16449570Sok2p YJR127C 16449570Ste12p YJR127C 17638031Tec1p YJR127C 17638031Yap5p YJR127C 12399584Flo8p YJR127C 16449570

Page 18 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Azf1p YOL067C 12399584Gat1p YOL067C 16709784Gcn4p YBL103C 17224918Pdr3p YBL103C 16914749Tos8p YBL103C 12464632Leu3p YOR077W 16923194Hcm1p YOR077W 12464632Ace2p YOR140W 15343339Fzf1p YOR140W 15343339Gal4p YOR140W 15343339Gcr2p YOR140W 15343339Leu3p YOR140W 16923194Nrg1p YOR140W 16709784Pdr1p YOR140W 15343339Rap1p YOR140W 16709784Rme1p YOR140W 15343339Skn7p YOR140W 12399584Smp1p YOR140W 15343339Ste12p YOR140W 19159457Swi4p YOR140W 15343339Swi5p YOR140W 15343339Yap5p YOR140W 12399584Fkh2p YLR403W 16709784Rap1p YLR403W 17646381Tec1p YLR403W 17638031Dal80p YNL257C 15343339Ino4p YNL257C 16709784Mss11p YNL257C 15343339Arg80p YJL089W 12399584Arg81p YJL089W 12399584Dal81p YJL089W 12399584Gcn4p YJL089W 12399584Rap1p YJL089W 17646381Sko1p YJL089W 18931682Ume6p YJL089W 15343339Hot1p YJL089W 18931682Dal82p YHR206W 16709784Sok2p YHR206W 17638031Gts1p YNL167C 15343339Ste12p YNL167C 19159457Abf1p YPR054W 9742114Ndt80p YPR054W 12832469Reb1p YPR054W 12399584

Page 19 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Sum1p YPR054W 12832469Cin5p YBR182C 12399584Fhl1p YBR182C 17646381Phd1p YBR182C 16449570Rap1p YBR182C 17646381Rim101p YBR182C 12509465Smp1p YBR182C 16709784Ste12p YBR182C 19159457Xbp1p YBR182C 15343339Pho4p YGL131C 12399584Cin5p YMR016C 12399584Cup9p YMR016C 12399584Fhl1p YMR016C 17646381Mga1p YMR016C 16449570Nrg1p YMR016C 12399584Pdr1p YMR016C 16914749Phd1p YMR016C 16449570Rap1p YMR016C 17646381Rox1p YMR016C 12399584Skn7p YMR016C 15343339Sko1p YMR016C 16709784Sok2p YMR016C 16709784Ste12p YMR016C 17638031Swi4p YMR016C 16709784Swi6p YMR016C 12399584Tec1p YMR016C 17638031Yap6p YMR016C 12399584Flo8p YMR016C 16449570Ste12p YJL127C 19159457Tos8p YJL127C 12464632Tos8p YER161C 12464632Abf1p YKL020C 18305101Hap1p YKL020C 15343339Yap1p YKL020C 15343339Yap5p YKL020C 12464632Ste12p YCR018C 19159457Gcn4p YNL309W 17224918Pho4p YNL309W 15343339Rap1p YNL309W 17646381Pho2p YMR053C 15343339Rme1p YMR053C 12399584Aft1p YMR019W 15343339Gat1p YMR019W 15343339

Page 20 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Gcn4p YMR019W 15343339Hal9p YMR019W 15343339Hms2p YMR019W 15343339Msn2p YMR019W 15343339Msn4p YMR019W 15343339Phd1p YMR019W 15343339Put3p YMR019W 15343339Reb1p YMR019W 16709784Rtg1p YMR019W 15343339Rtg3p YMR019W 15343339Skn7p YMR019W 15343339Stp1p YMR019W 15343339Cin5p YHR178W 12399584Hsf1p YHR178W 12399584Rap1p YHR178W 17646381Stb5p YHR178W 16914749Yap1p YHR178W 12399584Yap3p YHR178W 15343339Arg81p YKL072W 12399584Aro80p YKL072W 15343339Cin5p YKL072W 16709784Hap3p YKL072W 15343339Mcm1p YKL072W 15343339Mga1p YKL072W 15343339Rds1p YKL072W 15343339Reb1p YKL072W 15343339Dot6p YHR084W 16709784Fkh1p YHR084W 15343339Phd1p YHR084W 15343339Ste12p YHR084W 16449570Tec1p YHR084W 16449570Abf1p YDR463W 15343339Pdc2p YDR463W 15343339Tec1p YDR463W 15343339Ino4p YHR006W 16709784Mcm1p YHR006W 18303948Tye7p YHR006W 12464632Cin5p YDL048C 18287073Fhl1p YDL048C 17646381Mga1p YDL048C 16449570Phd1p YDL048C 16449570Sok2p YDL048C 16449570Ste12p YDL048C 17638031

Page 21 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Swi4p YDL048C 16709784Tec1p YDL048C 16449570Yap6p YDL048C 18287073Flo8p YDL048C 16449570Cha4p YDR310C 15343339Ste12p YDR310C 15343339Sum1p YDR310C 16709784Uga3p YDR310C 12399584Fhl1p YGL162W 17646381Fkh2p YGL162W 15343339Phd1p YGL162W 16449570Sok2p YGL162W 17638031Ste12p YGL162W 16449570Tec1p YGL162W 16449570Yox1p YGL162W 12464632Flo8p YGL162W 16449570Hms2p YPR009W 15343339Skn7p YPR009W 15343339Sok2p YPR009W 17638031Ste12p YPR009W 17638031Swi4p YPR009W 16709784Swi6p YPR009W 16709784Tec1p YPR009W 17638031Abf1p YER111C 18305101Cbf1p YER111C 16709784Mal33p YER111C 15343339Mbp1p YER111C 15343339Mcm1p YER111C 16709784Phd1p YER111C 16449570Sok2p YER111C 17638031Ste12p YER111C 19159457Swi4p YER111C 16709784Swi6p YER111C 12399584Ume6p YER111C 15343339Fkh1p YDR146C 10894549Fkh2p YDR146C 16709784Hap3p YDR146C 12399584Ino2p YDR146C 15343339Ino4p YDR146C 16709784Mcm1p YDR146C 16709784Rap1p YDR146C 16709784Reb1p YDR146C 2181283Rtg3p YDR146C 16709784

Page 22 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Sfp1p YDR146C 12399584Ste12p YDR146C 15343339Uga3p YDR146C 16709784Yap5p YDR146C 16709784Nrg1p YBR150C 15343339Oaf1p YBR150C 15343339Pip2p YBR150C 15343339Ste12p YBR150C 19159457Tec1p YBR150C 17638031Fhl1p YBR083W 17646381Hap1p YBR083W 15343339Hsf1p YBR083W 16709784Ino4p YBR083W 16709784Pdr1p YBR083W 16914749Phd1p YBR083W 16449570Sok2p YBR083W 16449570Ste12p YBR083W 16449570Sum1p YBR083W 12399584Swi5p YBR083W 11572776Tec1p YBR083W 16449570Flo8p YBR083W 16449570Fhl1p YBR240C 17646381Leu3p YBR240C 16923194Abf1p YNL139C 18305101Tos8p YNL139C 12464632Ash1p YGL096W 16709784Cin5p YGL096W 16709784Fhl1p YGL096W 17646381Mga1p YGL096W 16449570Phd1p YGL096W 16449570Sfp1p YGL096W 12399584Sok2p YGL096W 16449570Ste12p YGL096W 16449570Swi4p YGL096W 16709784Tec1p YGL096W 16449570Flo8p YGL096W 16449570Cbf1p YOR344C 15343339Cin5p YOR344C 16709784Fhl1p YOR344C 17646381Gcr2p YOR344C 15343339Hap5p YOR344C 15343339Hsf1p YOR344C 15343339Ino4p YOR344C 16709784

Page 23 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Mcm1p YOR344C 18303948Mga1p YOR344C 16449570Msn2p YOR344C 15343339Pdr1p YOR344C 16914749Pdr3p YOR344C 16914749Phd1p YOR344C 16449570Pho2p YOR344C 12399584Pho4p YOR344C 19108609Rap1p YOR344C 17646381Sok2p YOR344C 16449570Stb5p YOR344C 16914749Ste12p YOR344C 16449570Swi4p YOR344C 15343339Swi6p YOR344C 16709784Tec1p YOR344C 16449570Yap1p YOR344C 18627600Flo8p YOR344C 16449570Adr1p YDL170W 16709784Dal81p YDL170W 15343339Dal82p YDL170W 15343339Gcn4p YDL170W 16709784Gln3p YDL170W 16709784Hap2p YDL170W 15343339Hap3p YDL170W 15343339Ste12p YDL170W 19159457Yap1p YDL170W 18627600Rds2p YDL170W 17875938Abf1p YDR207C 18305101Fhl1p YDR207C 17646381Leu3p YDR207C 16923194Mac1p YDR207C 12399584Met32p YDR207C 15343339Mth1p YDR207C 12399584Oaf1p YDR207C 15343339Phd1p YDR207C 16449570Pip2p YDR207C 15343339Rme1p YDR207C 12399584Rox1p YDR207C 12399584Sfp1p YDR207C 12399584Dal82p YDR213W 16709784Hsf1p YDR213W 12399584Ino4p YDR213W 16709784Fhl1p YPL230W 17646381

Page 24 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Nrg1p YPL230W 15343339Phd1p YPL230W 15343339Rap1p YPL230W 17646381Rox1p YPL230W 15343339Skn7p YPL230W 15343339Sok2p YPL230W 17638031Gcn4p YML076C 15343339Hap1p YML076C 15343339Ino4p YML076C 16709784Ace2p YOR230W 16709784Cin5p YOR230W 16709784Fkh2p YOR230W 15343339Gcr2p YOR230W 15343339Gln3p YOR230W 15343339Ino2p YOR230W 15343339Ino4p YOR230W 16709784Mbp1p YOR230W 11206552Phd1p YOR230W 16449570Sok2p YOR230W 16449570Flo8p YOR230W 16449570Cin5p YOR229W 12399584Ino4p YOR229W 12399584Msn4p YOR229W 15343339Aft2p YIL101C 15343339Cin5p YIL101C 16709784Fhl1p YIL101C 17646381Gat1p YIL101C 12399584Ino4p YIL101C 16709784Mal33p YIL101C 15343339Mga1p YIL101C 16449570Nrg1p YIL101C 16709784Phd1p YIL101C 16449570Pip2p YIL101C 15343339Rap1p YIL101C 16709784Rgt1p YIL101C 16709784Rox1p YIL101C 16709784Sko1p YIL101C 18931682Sok2p YIL101C 16449570Ste12p YIL101C 19159457Yap5p YIL101C 16709784Ace2p YML007W 15343339Aro80p YML007W 12399584Azf1p YML007W 15343339

Page 25 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Hap1p YML007W 15343339Hap3p YML007W 12399584Msn2p YML007W 15343339Msn4p YML007W 15343339Rpn4p YML007W 15343339Skn7p YML007W 15343339Stb5p YML007W 16914749Ste12p YML007W 19159457Yap1p YML007W 16709784Mcm1p YHL009C 18303948Ste12p YHL009C 17638031Usv1p YHL009C 12399584Cbf1p YIR018W 15343339Gcn4p YIR018W 17224918Leu3p YIR018W 16923194Met28p YIR018W 12399584Met4p YIR018W 12399584Pdr1p YIR018W 16914749Skn7p YIR018W 12399584Ste12p YIR018W 19159457Swi4p YIR018W 12464632Tec1p YIR018W 16449570Yap6p YIR018W 12399584Azf1p YDR259C 15343339Cin5p YDR259C 15343339Cup9p YDR259C 15343339Fhl1p YDR259C 17646381Fkh2p YDR259C 15343339Hms1p YDR259C 12399584Hsf1p YDR259C 12399584Mga1p YDR259C 16449570Nrg1p YDR259C 16709784Pdr1p YDR259C 16914749Phd1p YDR259C 16449570Rap1p YDR259C 16709784Rox1p YDR259C 15343339Sok2p YDR259C 16449570Stb5p YDR259C 16914749Ste12p YDR259C 17638031Tec1p YDR259C 17638031Yap1p YDR259C 18627600Yap6p YDR259C 15343339Flo8p YDR259C 16449570

Page 26 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Hap1p YOL028C 15343339Nrg1p YOL028C 15343339Yap1p YOL028C 18627600Yap7p YOL028C 15343339Usv1p YBR239C 12399584Mot2p YDR026C 12399584Hcm1p YDR026C 12464632Arg80p YDR049W 12399584Aro80p YDR049W 12399584Gcr2p YDR049W 12399584Hms1p YDR049W 12399584Met31p YDR049W 12399584Smp1p YDR049W 12399584Ste12p YDR049W 19159457Stp1p YDR049W 12399584Abf1p YDR266C 18305101Dot6p YDR266C 16709784Fhl1p YER130C 17646381Phd1p YER130C 16449570Rap1p YER130C 17646381Sok2p YER130C 17638031Ste12p YER130C 19159457Ume6p YER130C 15343339Rox1p YER184C 15343339Ste12p YER184C 17638031Tec1p YER184C 17638031Yap7p YER184C 15343339Ste12p YFL052W 19159457Fhl1p YGR067C 17646381Gcn4p YGR067C 15343339Nrg1p YGR067C 15343339Ume6p YGR067C 15343339Arg80p YDR451C 15343339Ash1p YDR451C 15343339Fhl1p YDR451C 17646381Fkh1p YDR451C 16709784Fkh2p YDR451C 16709784Ino4p YDR451C 16709784Mbp1p YDR451C 16709784Mcm1p YDR451C 16709784Phd1p YDR451C 15343339Rap1p YDR451C 16709784Sok2p YDR451C 17638031

Page 27 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Ste12p YDR451C 17638031Swi4p YDR451C 16709784Swi6p YDR451C 16709784Tec1p YDR451C 17638031Rap1p YJL206C 17646381Sko1p YJL206C 16709784Ino4p YLR278C 16709784Msn1p YLR278C 12399584Phd1p YLR278C 16449570Sok2p YLR278C 17638031Flo8p YLR278C 16449570Abf1p YML081W 15343339Aft1p YML081W 15343339Gat1p YML081W 16709784Dot6p YNR063W 16709784Mcm1p YNR063W 16709784Fhl1p YML027W 17646381Mbp1p YML027W 15965243Mcm1p YML027W 18303948Swi4p YML027W 16709784Swi6p YML027W 16709784Cin5p YPR022C 15343339Sok2p YPR022C 17638031Ecm22p YPR196W 16709784Fkh1p YPR196W 16709784Fkh2p YPR196W 16709784Hsf1p YPR196W 15343339Ino4p YPR196W 16709784Mcm1p YPR196W 18303948Ste12p YPR196W 19159457Cbf1p YOR162C 15343339Pho4p YOR162C 19108609Yap1p YOR162C 18627600Yap7p YOR162C 15343339Leu3p YJL056C 16923194Pho4p YJL056C 19108609Zap1p YJL056C 16709784Hir2p YBR066C 12399584Ino4p YBR066C 12399584Mcm1p YBR066C 18303948Nrg1p YBR066C 15343339Pho4p YBR066C 19108609Skn7p YBR066C 15343339

Page 28 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Sok2p YBR066C 16449570Ste12p YBR066C 16449570Tec1p YBR066C 17638031Yap1p YBR066C 18627600Flo8p YBR066C 16449570Fhl1p YCR065W 17646381Gln3p YCR065W 15343339Mal33p YCR065W 15343339Mbp1p YCR065W 16709784Mcm1p YCR065W 18303948Pdr1p YCR065W 16914749Phd1p YCR065W 16449570Pho4p YCR065W 12399584Rox1p YCR065W 15343339Ste12p YCR065W 19159457Swi4p YCR065W 16709784Swi6p YCR065W 16709784Yfl044cp YCR065W 12399584Flo8p YCR065W 16449570Azf1p YDR006C 12399584Fhl1p YDR006C 17646381Gal4p YDR006C 15343339Pdr3p YDR006C 16914749Skn7p YDR006C 12399584Pho4p YDR017C 19108609Gcn4p YDR034C 17224918Gcr2p YDR034C 15343339Hap5p YDR034C 15343339Phd1p YDR034C 16449570Ste12p YDR034C 16449570Tec1p YDR034C 16449570Sok2p YDR096W 17638031Abf1p YER164W 18305101Fhl1p YGL166W 17646381Rap1p YGL166W 17646381Sok2p YGL166W 12464632Fkh2p YIL130W 15343339Fkh1p YJL103C 12399584Ste12p YJL103C 19159457Ume6p YJL103C 15343339Gsm1p YJL103C 16785442Cbf1p YLR266C 15343339Dal81p YLR266C 16709784

Page 29 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Gcn4p YLR266C 15343339Nrg1p YLR266C 15343339Pho4p YLR266C 15343339Rtg3p YLR266C 15343339Reb1p YMR213W 12399584Abf1p YMR280C 18305101Aft1p YMR280C 15343339Crz1p YMR280C 18362157Reb1p YMR280C 15343339Sok2p YMR280C 17638031Ume6p YMR280C 15343339Cad1p YOR172W 12399584Gcn4p YOR337W 16709784Pho2p YOR337W 15343339Pho4p YOR337W 15343339Rap1p YOR337W 17646381Pho4p YPL133C 15343339Abf1p YPR186C 16709784Hap4p YPR186C 15343339Yjl206cp YPR186C 12399584Sok1p YER109C 17638031Ste12p YER109C 17638031Tec1p YER109C 17638031Cin5p YMR172W 15343339Hcm1p YMR172W 12464632Leu3p YMR172W 16923194Rim101p YMR172W 15343339Skn7p YMR172W 15343339Dal81p YIR033W 15343339Dal82p YIR033W 15343339Gln3p YIR033W 15343339Hap2p YIR033W 15343339Smp1p YIR033W 12399584Ste12p YIR033W 19159457Fkh1p YCL055W 12399584Fkh2p YCL055W 12399584Ino4p YCL055W 16709784Mbp1p YCL055W 12399584Mcm1p YCL055W 16709784Phd1p YCL055W 12399584Rim101p YCL055W 12399584Rme1p YCL055W 12399584Rpn4p YCL055W 16709784

Page 30 of 30

Transcription Factor - Protein name

Target - Systematic Gene Name PubMed ID

Supplementary Table S3: Physical protein-DNA interactions extracted from YEASTRACT database.

Ste12p YCL055W 19159457Swi4p YCL055W 12399584

Page 1 of 1

Transcription Factor - Protein name

Target - Systematic Gene Name

Transcriptional Effect PubMed ID

Abf1p YKL112W REPRESSION 15192094Aft1p YGL071W ACTIVATION 14534306Gal3p YPL248C ACTIVATION 9670023Gal80p YPL248C ACTIVATION 9670023Gal4p YML051W REPRESSION 9670023Ino4p YOL108C ACTIVATION 1461729Nrg1p YHL027W ACTIVATION 12509465Rox1p YPR065W REPRESSION 7768429Mga1p YNL167C ACTIVATION 16087739Msn2p YNL167C ACTIVATION 16087739Mot3p YNL167C ACTIVATION 16087739Ash1p YMR016C REPRESSION 11046133Hms2p YLR131C ACTIVATION 17417638Tye7p YNL199C REPRESSION 17417638Tye7p YMR043W REPRESSION 17417638Eds1p YNL216W REPRESSION 17417638Hms2p YNL216W ACTIVATION 17417638Msn4p YNL216W REPRESSION 17417638Smp1p YHL027W REPRESSION 17417638Stp4p YMR016C REPRESSION 17417638Phd1p YMR016C REPRESSION 17417638Dal80p YMR016C REPRESSION 17417638

Supplementary Table S4: Transcriptional effect of protein-DNA interactions extracted from literature.