Introduction to Rule-based modeling - ETH Z€¦ · Computational Biology Group (CoBI), D-BSSE,...

| |

Prof Dagmar Iber, PhD DPhil

BSc Biotechnology 2016/17

19.05.2016 1

Rule-based Modeling

Computational Biology Group (CoBI), D-BSSE, ETHZ

| | 19.05.2016 2 Computational Biology Group (CoBI), D-BSSE, ETHZ

Contents

• Rule-based modeling

Background

Definition

Components and visualization

SBML

• Tutorial

Software (BioNetGen - .bngl)


In biochemical modeling, what do we

• usually know?

Information about protein-interactions

Information about kinetic laws

• need to have?

Consistent system of interactions of the required components

Set of ODE’s to describe the behavior of this system

Flexibility to define observables from our system

• want to avoid?

Missing any important interactions

Making unjustified assumptions


Modeling biological pathways as differential

equations

The theory of dynamical systems offers an extensive repertoire of

mathematical techniques for reasoning about such networks:

• well-understood ontologies

• behaviors: like steady states, oscillations, and chaos, along with their linear

stability properties.

• numerical procedures for integrating systems of equations (while varying

over parameters and initial conditions)

a powerful workbench that has enabled the study of physics and

bio-chemical kinetics


A common setup for ODE-based modeling

1. We assign a single state variable xi(t) to

each icon.

2. One state variable would be the unbound

ligand x1(t). Another state variable would be

the ligand-receptor complex, x2(t), and so

on.

3. The set of values of all state variables fx1(t);

x2(t); at a given time point t constitutes the

state of the system at time t.


Temporal evolution in ODE-based modeling

In order to calculate the evolution of xi (t) with time we formulate a differential equation of

the form:

In most biological networks such an approach typically leads to huge dynamic systems

that are based on hundreds of ODEs to describe the interactions between less than 10

components.

| |

Many ion channels, kinases, phosphatases, and receptors:

phosphorylation, ubiquitination, methylation, glycosylation, and a

plethora of other chemical tagging processes.

If a protein has at least 8 modifiable sites, which means 256 states. A

simple heterodimer, this would equal ~ 65,000 equations.

Many more possible chemical species than what can be realized by

the actual number of molecules involved in a cellular process of this

kind.

19.05.2016 7

Combinatorial complexity

Computational Biology Group (CoBI), D-BSSE, ETHZ


Rule-based modeling


Rule-based Modeling

Can effectively replace the modeling with large system of ODEs!

A rule-set can be used to generate the system of ODEs • Using only the set of sensible biochemical rules for what is known about

our system it is easy to generate a comprehensive system and avoid

making any errors due to missing interactions and/or unjustified

assumptions.

Tools can directly work on the rule-set in place of a translated model

Rule-sets greatly simplify the model normally implied in a system

of many ODEs


When should we use RBM?

Many interacting components

Multiple components interact with each other and create large

ensembles of complexes.

Multiple states for each component

Post-translational modifications alter the behavior of the proteins in

the system.


Concepts


Structure of a rule-based model

• Parameters: Define the parameters that govern the dynamics of the system (rate constants,

the values for initial concentrations of species in the biological system)

• Molecule types: Define molecules, including components and allowed component states

• Seed species: Define the initial state of system (initial species and their concentrations)

• Observables: Define model outputs, which are functions of concentrations of species having

particular attributes

• Reaction rules: Define rules that describe how molecules interact

• Actions: Methods to generate and simulate network


1. Complex formation:

A bond can be formed to link two molecules through their available

binding sites.

2. Complex dissociation:

An existing bond between two molecules can be removed.

3. Change the state-label of a component:

A molecule undergoes a certain post-translational state modification (e.g.

become phosphorylated), or alters the state-label of its functional shape

or conformation (e.g. open/closed conformation of integrins).

4. Add a molecule:

The production of a species.

5. Delete a molecule:

The degradation of a species.

From interactions to rules


Visualizing Network Interactions

• When modeling a biochemical system in biology, it is not

very clear how to best illustrate a system.

• Conceptual cartoons that are usually found in textbooks are

too abstract to reflect the full system that needs to be

modeled mathematically.

• On the other hand writing down the full biochemically

rigorous system can be very difficult, and its visualization

might not be intuitive anymore. In rule-based modeling the

concept of contact maps is used.


Visualizing Network Interactions Contact Maps

What is depicted in these maps is just the set of possible

interactions among the basic elements of our system,

without writing down the actual transitions from reactants to

products explicitly.


Contact map - BioNetGen

Complex association

“A(a)+B(b) –> A(a!1).B(b!1) k_bind”

Complex disocciation

“A(a!1).B(b!1) –> A(a) + B(b) k_unbind”

Change a component state label

“EGFR(Y1068 ~ P) –> EGFR(Y1068 ~ U)

k_dephos”

Add a molecule

“I() –> I() + A(a,Y~U) k_synth”

Delete a molecule

“A() –>Trash() k_deg”


Simplify your modeling

Define the differential equation for each

species manually

Define mechanistic rules, and generate

the system of equations automatically

Reaction Network

(A) Contact Map

(B) State transition


The Systems Biology Markup

Language (SBML)


SBML as a proxy between tools!

Matlab


Equation system (ODEs/PDEs)

Biochemical reaction

network

Rule-based interaction

network

Summary of SBML


Tutorial


• There are different software that have been designed to enable rule-based modeling.

• Among other software, most popular are BioNetGen and Kappa. The syntax and example below are based on the BioNetGen language (BNGL).

• Attention: you will need Perl (www:perl:org) to run this software –Windows users

Software


Rule-based modeling using BioNetGen

1. Widely-used (Kappa is the alternative)

2. GNU GPL

3. Amicable language for entity/kinetics description

4. Work on rule set directly


What does a .bngl file look like?


Thanks for your attention!

Slides for this talk will be available at:

http://www.bsse.ethz.ch/cobi/education

Introduction to Rule-based modeling - ETH Z€¦ · Computational Biology Group (CoBI), D-BSSE,...

Documents

Transcript of Introduction to Rule-based modeling - ETH Z€¦ · Computational Biology Group (CoBI), D-BSSE,...