introduction to rule-based modeling - eth z€¦ · computational biology group (cobi), d-bsse,...
TRANSCRIPT
| |
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)
| | 19.05.2016 3 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 4 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 5 Computational Biology Group (CoBI), D-BSSE, ETHZ
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.
| | 19.05.2016 6 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 8 Computational Biology Group (CoBI), D-BSSE, ETHZ
Rule-based modeling
| | 19.05.2016 9 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 10 Computational Biology Group (CoBI), D-BSSE, ETHZ
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.
| | 19.05.2016 11 Computational Biology Group (CoBI), D-BSSE, ETHZ
Concepts
| | 19.05.2016 12 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 13 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 14 Computational Biology Group (CoBI), D-BSSE, ETHZ
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.
| | 19.05.2016 15 Computational Biology Group (CoBI), D-BSSE, ETHZ
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.
| | 19.05.2016 16 Computational Biology Group (CoBI), D-BSSE, ETHZ
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”
| | 19.05.2016 17 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 18 Computational Biology Group (CoBI), D-BSSE, ETHZ
The Systems Biology Markup
Language (SBML)
| | 19.05.2016 19 Computational Biology Group (CoBI), D-BSSE, ETHZ
SBML as a proxy between tools!
Matlab
| | 19.05.2016 20 Computational Biology Group (CoBI), D-BSSE, ETHZ
Equation system (ODEs/PDEs)
Biochemical reaction
network
Rule-based interaction
network
Summary of SBML
| | 19.05.2016 21 Computational Biology Group (CoBI), D-BSSE, ETHZ
Tutorial
| | 19.05.2016 22 Computational Biology Group (CoBI), D-BSSE, ETHZ
• 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
| | 19.05.2016 23 Computational Biology Group (CoBI), D-BSSE, ETHZ
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
| | 19.05.2016 24 Computational Biology Group (CoBI), D-BSSE, ETHZ
What does a .bngl file look like?
| | 19.05.2016 25 Computational Biology Group (CoBI), D-BSSE, ETHZ
Thanks for your attention!
Slides for this talk will be available at:
http://www.bsse.ethz.ch/cobi/education