Course Name:

Systems Biology IConducted by-

Shigehiko kanaya

&

Md. Altaf-Ul-Amin

Dates of Lectures:April: 7, 14, 21, 28

May: 12, 19, 26

June: 2

Lecture Time: Tuesdays 9:20-10:50

Website

http://csblab.naist.jp/library/

SyllabusIntroduction to Graphs/Networks, Different network models, Properties of Protein-Protein Interaction Networks, Different centrality measuresProtein Function prediction using network concepts, Application of network concepts in DNA sequencing, Line graphs Concept and types of metric, Hierarchical Clustering, Finding clusters in undirected simple graphs: application to protein complex detection Introduction to KNApSAcK database, Metabolic Reaction system as ordinary differential equations, Metabolic Reaction system as stochastic processMetabolic network and stoichiometric matrix, Information contained in stoichiometric matrix, Elementary flux modes and extreme pathwaysGraph spectral analysis/Graph spectral clustering and its application to metabolic networks Normalization procedures for gene expression data, Tests for differential expression of genes, Multiple testing and FDR, Reverse Engineering of genetic networks. Introduction to next generation sequencing.Finding Biclusters in Bipartite Graphs, Properties of transcriptional/gene regulatory networks, Introduction to software package ExpanderIntroduction to signaling pathways, Selected biological processes: Glycolytic oscillations, Sustained oscillation in signaling cascades

Central dogma of molecular biology

The crowded Environment inside the cell

Some of the physical characteristics are as follows:Viscosity > 100 × μ H20Osmotic pressure < 150 atmElectrical gradient ~300000 V/cmNear crystalline state

The osmotic pressure of ocean water is about 27 atm and that of blood is 7.7 atm at 25oC

Source: Systems biology by Bernhard O. Palsson

Without a complicated regulatory system all the processes inside the cell cannot be controlled properly.

Genome (DNAs)Genome (DNAs)

Transcriptome (mRNAs) Transcriptome (mRNAs)

Proteome (peptides) Proteome (peptides)

Metabolome (Metabolites) Metabolome (Metabolites)

Phenome (Phenotype) Phenome (Phenotype)

Big Picture of Hierarchy in Systems Biology

Nucleotide sequences---Double Helix

Bio-chemical molecules

Nucleotide sequences-Single Stranded

Proteins-Amino Acid Sequences

Bioinofomatics

a

b c

d e f g

h i k m

j l

5’

5’3’

3’

A B C D E F G H I J K L MProtein

A B C D EF

G H I JK L MFunctionUnit

Metabolite 1 Metabolite 2 Metabolite 3

Metabolite 4

Metabolite 5

Metabolite 6

B C

D EF

I L

H KMetabolic Pathway

G

Activation (+)A

GRepression (-)

ab c

d e f gh i k m

j l5’

5’3’3’

Genome:

Transcriptome ：

Proteome, Interactome

MetabolomeFT-MS

Integration of omicsto define elements(genome, mRNAs, Proteins, metabolites)

Understanding organism as a system (Systems Biology)

Understanding species-species relations (Survival Strategy)

comprehensive and global analysis of diverse metabolites produced in cells and organisms

Introduction to Graphs/Networks

Representing as a network often helps to understand a system

Konigsberg bridge problem

Konigsberg was a city in present day Germany encompassing two islands and the banks of Pregel River. The city was connected by 7 bridges.

Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?

Konigsberg bridge problem

Konigsberg was a city in present day Germany including two islands and the banks of Pregel River. The city was connected by 7 bridges.

Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?

Konigsberg bridge problem

Konigsberg was a city in present day Germany including two islands and the banks of Pregel River. The city was connected by 7 bridges.

Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?

Konigsberg bridge problem

Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?

This problem was solved by Leonhard Eular in 1736 by means of a graph.

Konigsberg bridge problem

Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?

This problem was solved by Leonhard Eular in 1736 by means of a graph.

A

B

C

D

Konigsberg bridge problem

Problem: Start at any point, walk over each bridge exactly once and return to the same point. Possible?

A

B

C

D The necessary condition for the existence of the desired route is that each land mass be connected to an even number of bridges.

A, B, C, D circles represent land masses and each line represent a bridge

The graph of Konigsberg bridge problem does not hold the necessary condition and hence there is no solution of the above problem.

This notion has been used in solving DNA sequencing problem

A graph G=(V,E) consists of a set of vertices V={v1, v2,…) and a set of edges E={e1,e2, …..) such that each edge ek is identified by a pair of vertices (vi, vj) which are called end vertices of ek.

A graph is an abstract representation of almost any physical situation involving discrete objects and a relationship between them.

Definition

A

B

C

D

It is immaterial whether the vertices are drawn rectangular or circular or the edges are drawn staright or curved, long or short.

A

B

C

D

Both these graphs are the same

Many systems in nature can be represented as networks

The internet is a network of computers

Very high degree nodeNo such node exists

Road Network Air route Network

Many systems in nature can be represented as networks

Printed circuit boards are networks

Many systems in nature can be represented as networks

Network theory is extensively used to design the wiring and placement of components in electronic circuits

Protein-protein interaction network of e.coli

Many systems in nature can be represented as networks

Some Basic Concepts regarding networks:

•Average Path length

•Diameter

•Eccentricity

•Clustering Coefficient

•Degree distribution

a

db f

e

c

Distance between node u and v called d(u,v) is the least length of a path from u to v.

d(a,e) = ?

Average Path length

a

db f

e

c

Distance between node u and v called d(u,v) is the least distance of a path from u to v.

d(a,e) = ?Length of a-b-c-d-f-e path is 5

Average Path length

a

db f

e

c

Distance between node u and v called d(u,v) is the least distance of a path from u to v.

d(a,e) = ?Length of a-b-c-d-f-e path is 5

Length of a-c-d-f-e path is 4

Average Path length

a

db f

e

c

Distance between node u and v called d(u,v) is the least length of a path from u to v

d(a,e) = ?

Length of a-b-c-d-f-e path is 5

Length of a-c-d-f-e path is 4

Length of a-c-d-e path is 3

The minimum length of a path from a to e is 3 and therefore

d(a,e) = 3.

Average Path length

a

db f

e

cThere are 6 nodes and

6C2 = (6!)/(2!)(4!)=15 distinct pairs for example (a,b), (a,c)…..(e,f).

We have to calculate distance between each of these 15 pairs and average them

Average Path length

Average path length L of a network is defined as the mean distance between all pairs of nodes.

Average Path length

Average path length L of a network is defined as the mean distance between all pairs of nodes.

a to b 1

a to c 1

a to d 2

a to e 3

a to f 3--------------------------------------------____________________15 pairs 27(total length)

L=27/15=1.8

Average path length of most real complex network is small

a

db f

e

c

Finding average path length is not easy when the network is big enough. Even finding shortest path between any two pair is not easy.

A well known algorithm is as follows:

Dijkstra E.W., A note on two problems in connection with Graphs”, Numerische Mathematik, Vol. 1, 1959, 269-271.

Dijkstra’s algorithm can be found in almost every book of graph theory.

There are other algorithms for finding shortest paths between all pairs of nodes.

Average Path length

Diameter

a

db f

e

c

Distance between node u and v called d(u,v) is the least length of a path from u to v.

The longest of the distances between any two node is called Diameter

a to b 1

a to c 1

a to d 2

a to e 3

a to f 3--------------------------------------------15 pairs

Diameter of this graph is 3

Eccentricity And Radius

a

db f

e

c

Eccentricity of a node u is the maximum of the distances of any other node in the graph from u.

The radius of a graph is the minimum of the eccentricity values among all the nodes of the graph.

a to b 1

a to c 1

a to d 2

a to e 3

a to f 3

Therefore eccentricity of node a is 3Radius of this graph is 2

3

3

3

3

2

2

The degree distribution is the probability distribution function P(k), which shows the probability that the degree of a randomly selected node is k.

Degree Distribution

1 2 43

10

# of

nod

es

havi

ng d

egre

e k

Degree Distribution

Degree

1 2 43

1

P(k

)

Degree Distribution

Any randomness in the network will broaden the shape of this peak

Degree

1 2 43

2

4

# of

nod

es

havi

ng d

egre

e k

Degree Distribution

Degree

1 2 43

0.25

0.5

P(k

)

Degree Distribution

Degree

Degree Distribution

( )!

k

P k ek

Poisson’s Distribution

Degree distribution of random graphs follow Poisson’s distribution

e = 2.71828..., the Base of natural Logarithms

Connectivity k

P(k)

P(k) ~ k-γ

Power Law Distribution

Degree distribution of many biological networks follow Power Law distribution

Degree Distribution

Power Law Distribution on log-log plot is a straight line

Clustering coefficient

2

( 1)i

ii i

EC

k k

1

1 N

ii

C CN

ki = # of neighbors of node i

Ei = # of edges among the neighbors of node i

a

db f

e

c

Clustering coefficient

2

( 1)i

ii i

EC

k k

1

1 N

ii

C CN

Ca=2*1/2*1= 1

ki = # of neighbors of node i

Ei = # of edges among the neighbors of node i

a

db f

e

c

Clustering coefficient

2

( 1)i

ii i

EC

k k

1

1 N

ii

C CN

Ca=2*1/2*1= 1

Cb=2*1/2*1= 1

Cc=2*1/3*2= 0.333

Cd=2*1/3*2= 0.333

Ce=2*1/2*1= 1

Cf=2*1/2*1= 1

Total = 4.666

C =4.666/6= 0.7776

ki = # of neighbors of node i

Ei = # of edges among the neighbors of node i

a

db f

e

c

Clustering coefficient

By studying the average clustering C(k) of nodes with a given degree k, information about the actual modular organization can be extracted.

a

db f

e

c

Ca=2*1/2*1= 1

Cb=2*1/2*1= 1

Cc=2*1/3*2= 0.333

Cd=2*1/3*2= 0.333

Ce=2*1/2*1= 1

Cf=2*1/2*1= 1C(1)=0

C(2)=(Ca+Cb+Ce+Cf)/4=1

C(3)=(Cc+Cd)/2=0.333

Clustering coefficient

By studying the average clustering C(k) of nodes with a given degree k, information about the actual modular organization can be extracted.

For most of the known metabolic networks the average clustering follows the power-law.

C(k) ~ k-γ

Power Law Distribution

Subgraphs

Consider a graph G=(V,E). The graph G'=(V',E') is a subgraph of G if V' and E' are respectively subsets of V and E.

a

db f

e

c

a

b

c

df

c

Graph G

Subgraph of G

Subgraph of G

Induced Subgraphs

An induced subgraph on a graph G on a subset S of nodes of G is obtained by taking S and all edges of G having both end-points in S.

a

db f

e

c

a

b

c

df

c

Graph G

Induced subgraph of G for S={a, b, c}

Induced subgraph of G for S={c, d, f}

Graphlets

Graphlets are non-isomprphic induced subgraphs of large networks

T. Milenkovic, J. Lai, and N. Przulj, GraphCrunch: A Tool for Large Network Analyses, BMC Bioinformatics, 9:70, January 30, 2008.

Partial subgraphs/Motifs

A partial subgraph on a graph G on a subset S of nodes of G is obtained by taking S and some of the edges in G having both end-points in S. They are sometimes called edge subgraphs.

a

db f

e

c

a

b

c

Graph G

Partial subgraph of G

For S={a, b, c}

Partial subgraphs/Motifs

SIM MIM FFL

Genomic analysis of regulatory network dynamics reveals large topological changesNicholas M. Luscombe, M. Madan Babu, Haiyuan Yu, Michael Snyder, Sarah

A. Teichmann & Mark Gerstein, NATURE | VOL 431| 2004

SIM=Single input motif

MIM= Multiple input motif

FFL=Feed forward loop

This paper searched for these motifs in transcriptional regulatory network of Saccharomyces cerevisiae

Junker, Björn H., and Falk Schreiber. Analysis of biological networks. Vol. 2. John Wiley & Sons, 2011.

Three node motifs with bi-directional edges found in regulatory network of yeast (Saccharomyces cerevisiae)

Harary, Frank, and Edgar M. Palmer. Graphical enumeration. Elsevier, 2014

Genomic analysis of regulatory network dynamics reveals large topological changesNicholas M. Luscombe, M. Madan Babu, Haiyuan Yu, Michael Snyder, Sarah

A. Teichmann & Mark Gerstein, NATURE | VOL 431| 2004

Partial subgraphs/Motifs

Introduction to Cytoscape

http://www.cytoscape.org/

Data Types in computational biology/Systems biologyUseful websites

What is systems biology?

Each lab/group has its own definition of systems biology.

This is because systems biology requires the understanding and integration of different levels of OMICS information utilizing the knowledge from different branches of science and individual labs/groups are working on different area.

Theoretical target: Understanding life as a system.Practical Targets: Serving humanity by developing new generation medical tests, drugs, foods, fuel, materials, sensors, logic gates……

Understanding life or even a cell as a system is complicated and requires comprehensive analysis of different data types and/or sub-systems.Mostly individual groups or people work on different sub-systems---

Some of the currently partially available and useful data types:

Genome sequencesBinding motifs in DNA sequences or CIS regulatory regionCODON usageGene expression levels for global gene sets/microRNAsProtein sequencesProtein structuresProtein domainsProtein-protein interactionsBinding relation between proteins and DNARegulatory relation between genesMetabolic PathwaysMetabolite profilesSpecies-metabolite relationsPlants usage in traditional medicines

Usually in wet labs, experiments are conducted to generate such dataIn dry labs like ours we analyze these data to extract targeted information using different algorithms and statistics etc.

Data Types in computational biology/Systems biology

>gi|15223276|ref|NP_171609.1| ANAC001 (Arabidopsis NAC domain containing protein 1); transcription factor [Arabidopsis thaliana]MEDQVGFGFRPNDEELVGHYLRNKIEGNTSRDVEVAISEVNICSYDPWNLRFQSKYKSRDAMWYFFSRRENNKGNRQSRTTVSGKWKLTGESVEVKDQWGFCSEGFRGKIGHKRVLVFLDGRYPDKTKSDWVIHEFHYDLLPEHQRTYVICRLEYKGDDADILSAYAIDPTPAFVPNMTSSAGSVVNQSRQRNSGSYNTYSEYDSANHGQQFNENSNIMQQQPLQGSFNPLLEYDFANHGGQWLSDYIDLQQQVPYLAPYENESEMIWKHVIEENFEFLVDERTSMQQHYSDHRPKKPVSGVLPDDSSDTETGSMIFEDTSSSTDSVGSSDEPGHTRIDDIPSLNIIEPLHNYKAQEQPKQQSKEKVISSQKSECEWKMAEDSIKIPPSTNTVKQSWIVLENAQWNYLKNMIIGVLLFISVISWIILVG

Sequence data (Genome /Protein sequence)

Usually BLAST algorithms based on dynamic programming are used to determine how two or more sequences are matching with each other

Sequence matching/alignments

Twenty amino acids

Four nucleotides

Four nucleotides

CODONS

http://en.wikipedia.org/wiki/File:Peptide_syn.png

CODON USAGE

CODON USAGE

Multivariate data (Gene expression data/Metabolite profiles)

There are many types of clustering algorithms applicable to multivariate data e.g. hierarchical, K-mean, SOM etc.

Multivariate data also can be modeled using multivariate probability distribution function

Binary relational Data (Protein-protein interactions, Regulatory relation between genes, Metabolic Pathways) are networks.

Clustering is usually used to extract information from networks.

Multivariate data and sequence data also can be easily converted to networks and then network clustering can be applied.

AtpB AtpAAtpG AtpEAtpA AtpHAtpB AtpHAtpG AtpHAtpE AtpH

Useful Websites

www.geneontology.org www.genome.ad.jp/kegg www.ncbi.nlm.nih.gov www.ebi.ac.uk/databases http://www.ebi.ac.uk/uniprot/ http://www.yeastgenome.org/ http://mips.helmholtz-muenchen.de/proj/ppi/ http://www.ebi.ac.uk/trembl http://dip.doe-mbi.ucla.edu/dip/Main.cgi www.ensembl.org

Some websites

Some websites where we can find different types of data and links to other databases

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

NETWORK TOOLSSource: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

NETWORK TOOLSSource: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)

Source: Knowledge-Based Bioinformatics: From Analysis to InterpretationGil Alterovitz, Marco Ramoni (Editors)