Upload
jonathan-blakes
View
921
Download
0
Tags:
Embed Size (px)
DESCRIPTION
2009 ASAP seminar
Citation preview
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