20090219 the case for another systems biology modelling environment

The case for another

systems biology modelling environment

Jonathan Blakes

19/2/2009

ASAP seminar

Outline

• Problem domain– Systems Biology– Synthetic Biology

• Modelling formalisms• Existing software• Room for improvement• Implementation• Conclusions

3

Systems Biology

• A wealth of knowledge from molecular biology and Omics projects

• We know the components, their interactions and locations (partially)

• Desire to integrate this knowledge• Networks of molecular interactions operate over

large scales - picoseconds to days, nanometers to meters – to produce complex phenotypes

• Truly interdisciplinary field

4

Systems Biology

• Simulate biochemical reactions and observe dynamics in time and space

• Obtain results that are comparable with laboratory observations

• Look “under the hood” and trace individual molecules

• Test hypotheses quickly in silico

5

Synthetic Biology• Construction of novel biological circuits from modules of co-

opted genes and proteins (BioBricks™) • Need CAD software to design synthetic circuits• Need models to check for unwanted side-effects• Software needs to deal with modularity and orthogonality

6

BioBricks• Standardised biological parts• Assembled into larger BioBricks

• DNA sequences for expression in host cell - need to model background context

• Each module changes context in which it is placed

7Canton B, Labno A, Endy D. Refinement and standardization of synthetic biological parts and devices. Nature Biotechnology (2008) 26: 787-793

Modelling formalisms

• To run simulations we first need to make computer ‘understand’ gene synthesis and regulation, diffusion, cell division, movement and death.

• Modelling formalism need to be an unambiguous, formal description of cellular processes.

• Choice of formalism determines the systems that be modelled as well as the scale and realism of the models

8

9

qualitative ↔ quantitative

cont

inuo

us ↔

dis

cret

e

mechanistic ↔

symbolicODE

sto

chsi

m

Petri Nets

Bo

ole

an

net

wo

rks

Process calculi

BaN

Aguda B, Goryachev A. From Pathways Databases to Network Models. ISMB 2006

Formalism-space

Established approaches

• Mathematical modelling (ODEs)– model the change in concentration of a molecular

species as functions of the concentrations of other species

– set of equations completely describe the dynamics of the system

– macroscopic, deterministic and continuous – one solution

10

11

Recent developments

• Computational modelling– model the individual interactions – mostly mesocopic, discrete, stochastic – many

trajectories– ‘Executable biology’

• model is a program• system and simulation are one

Stochastic vs Deterministic

12

110100

Computational Modelling Formalisms

• Petri nets• Process calculi• Kappa• P systems• Many more: Statecharts, Pathway Logic,

BioCham, DEVS…

13

Petri nets

• Unambiguous visual formalism• Molecules as tokens in places• Reactions are transitions• Non-deterministic• Properties:

– Reachability– T-invariants– P-invariants– Boundedness

14

Process calculi

• Algebra for reasoning about concurrent, mobile systems

• Molecules are processes• Interactions through

communication channels• Very active research area:

Sπ, BioPEPA, BlenX (Beta binders), Brane calculi…

15

Graphical Sπ

• Sπ with visual formalism which shows state-space of processes

Philips A, Cardelli L. Simulating Biological Systems in the Stochastic pi-calculus.

• Interactions are not visualised in formalism

16

Kappa• Fontana W, Krivine J, Danos V, Lavene C - Harvard• Molecules as agents with modification sites (state) that can

connect to each other• Rules modify sites and connections• “don’t know don’t care” syntax (like regular expressions)

avoids combinatorial explosion of rules for each state

• New web-based software Cellucidate17

18

P systems

• Computationally powerful formal language

• Molecules are objects• Reactions are rules• Compartments are membranes• Hierarchy of membranes analogous to

structure of eukaryotic cell• Our chosen formalism

19

Support Tools

• Markup languages - SBML, CellML– designed for machines not people

• Modelling environments– edit reactions, molecular quantities with a GUI - COPASI– visual model editors – CellDesigner, Athena/TinkerCell– run simulations

• Results manipulation– analysis - Excel– plotting - Matlab– publication - LaTeX

21

COPASI

22

COPASI

23

CellDesigner

24

Athena / TinkerCell

25

MetaPLab

26

Systems Biology Graphical Notation

• SBGN developed by systems biologists

• Several modes inc. Process Diagrams

• Maps: repeated elements have clone markers

• Submaps fold complexity• Can use submaps to wrap

modules and clone markers to check orthogonality

27

SBGN

28

Goals for a new tool

• Layer of abstraction between modeller and formalism:– SBGN editor ↔ GUI editor → P system– Perform ‘experiments’ not just simulations– Easy access to results

• Build other features around this– Parameter optimisation– Model checking

• Provide model background (minimal metabolism and expression machinery as proof of concept)

29

Implementation

• PyQt – Python bindings to C++ GUI framework Qt by Trolltech (Nokia)

• Matplotlib – plotting in Python• PyTables – Python HDF5 interface• JHotDraw – diagram editing framework by

Design Pattern’s guru Erich Gamma

30

Simulation Results

31

Conclusions

• Modelling is part science and part art• Models need rigorous foundation• Modellers need helpful software• Existing tools in various states of readiness• Model building should be intrinsic to practice

of biology in the laboratory• Computer scientists job to faciliate biologists

modelling

32

Acknowledgements

• Infobiotics team– Dr. Natalio Krasnogor (supervisor)– Dr. Francisco Romero Campero– Dr. Hongqing Cao – Dr. Jamie Twycross

• Programmers– James Smaldon– Pawel Widera

33

34

Macrophage and Bacterium 2,000,000X

2002

Watercolor by David S. Goodsell