20090219 The case for another systems biology modelling environment
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20090219 The case for another systems biology modelling environment

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2009 ASAP seminar

2009 ASAP seminar

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  • a set of equations that together completely describe the dynamics of the system small systems can be solved, larger systems simulated using Rutta-Kunge or other one output
  • mescoscopic (a physics term), talking about individual molecules, not populations, but also not atoms
  • mescoscopic (a physics term), talking about individual molecules, not populations, but also not atoms
  • David S. Goodsell

20090219 The case for another systems biology modelling environment 20090219 The case for another systems biology modelling environment Presentation Transcript

  • The case for another systems biology modelling environment Jonathan Blakes 19/2/2009 ASAP seminar
  • Abstract
    • Systems biology is a growing interdisciplinary field that aims understand the normal and abnormal functioning of organisms by modelling the relationships between molecular species in living cells. There is a large body of software, often developed in academia, to assist with the building and simulation of such models. This talk will give an overview of recent computational modelling approaches in systems biology and the associated software landscape. It presents the case for the development of a new modelling environment aims to improve the user experience of biologists by making models easier to build and to refine, while increasing the complexity of and scales over which models can be built.
  • Outline
    • Problem domain
      • Systems Biology
      • Synthetic Biology
    • Modelling formalisms
    • Existing software
    • Room for improvement
    • Implementation
    • Conclusions
  • 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
  • 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
  • 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
  • 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
    Canton 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
  • Aguda B, Goryachev A. From Pathways Databases to Network Models. ISMB 2006 Formalism-space qualitative ↔ quantitative continuous ↔ discrete mechanistic ↔ symbolic ODE stochsim Petri Nets Boolean networks Process calculi BaN
  • 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
  • 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 1 10 100
  • Computational Modelling Formalisms
    • Petri nets
    • Process calculi
    • Kappa
    • P systems
    • Many more: Statecharts, Pathway Logic, BioCham, DEVS…
  • Petri nets
    • Unambiguous visual formalism
    • Molecules as tokens in places
    • Reactions are transitions
    • Non-deterministic
    • Properties:
      • Reachability
      • T-invariants
      • P-invariants
      • Boundedness
  • Process calculi
    • Algebra for reasoning about concurrent, mobile systems
    • Molecules are processes
    • Interactions through communication channels
    • Very active research area: S π , BioPEPA, BlenX (Beta binders), Brane calculi…
  • Graphical S π
    • S π 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
  • 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 Cellucidate
  • 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
  • Cellular automata
    • Well studied computational devices
    • Simple rules lead to emergent phenomena
    • Discrete notion of space
    • Highly parallel
  • 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
  • COPASI
  • COPASI
  • CellDesigner
  • Athena / TinkerCell
  • MetaPLab
  • 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
  • SBGN
  • 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)
  • 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
  • Simulation Results
  • 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
  • Acknowledgements
    • Infobiotics team
      • Dr. Natalio Krasnogor (supervisor)
      • Dr. Francisco Romero Campero
      • Dr. Hongqing Cao
      • Dr. Jamie Twycross
    • Programmers
      • James Smaldon
      • Pawel Widera
  • Macrophage and Bacterium 2,000,000X 2002 Watercolor by David S. Goodsell