Simulation and Complexity SCB : Simulating Complex Biosystems

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Simulation and ComplexitySCB Simulating
Complex Biosystems

• Susan Stepney
• Department of Computer Science

Leo Caves Department of Biology
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Module Aims

• to provide an introduction to the structure,
  organisation and properties of biosystems and
  their analysis from the perspective of complex
  systems (e.g. self-organisation, emergence)
• to introduce the methods, applications and
  practical issues associated with the computer
  simulation of biosystems
• to explore the potential applications of such a
  systems approach to biology in medicine and
  engineering

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Systems biology

• An approach to Biology focusing on the
  integration of existing biological knowledge
  towards building predictive models of biological
  systems.
• a systems view, rather than a component view
• structure (anatomy components and interactions)
• dynamics (physiology)
• control mechanisms
• design methods
• a model-based view, rather than a descriptive view

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biological models languages and tools

• enormous amounts of data
• modelling at different biological levels
• metabolic networks, cell, organs, organisms,
  populations,
• biology-specific tools
• gene ontology a structured vocabulary
• systems biology markup language (SBML)
• generic tools
• mathematics
• differential equations, difference equations,
  fractals,
• computer modelling languages
• UML, petri nets,

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modelling and simulation
analysis(eg solving the equations)
the model (eg mathematical equations)
the solution (consequences of the model)
the easy bit !
formal
deducing the consequences (concept mapping)
modelling the world(concept mapping)
informal
the difficult bit !
the domain (the real world)
the prediction (real world consequences)
update, refine, and iterate if the model and
reality disagree, it is the model that is wrong
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modelling proteins

• based on the protein sequence
• what does it interact with?
• based on various inference methods / correlations
• what is the structure?
• thermodynamic methods
• simulations
• based on the structure
• what does it interact with?
• hybrid methods
• combining data, statistics, models,

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modelling networks

• networks everywhere
• regulatory networks
• metabolic networks
• signalling networks
• connectivity and topology
• random
• hierarchical
• scale free, small world,
• robust yet fragile
• motifs, modules,

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reaction-diffusion equations

• non-linear f and g, coupled
• reaction rates, dependent on c1 and c2
• spatial patterns
• if different diffusion rates k1 ? k2
• local activation long range inhibition
• animal coat patterns Alan Turing 1952

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Petri net example Fas-induced apoptosis

• Matsuno et al, 2003

as a cartoon
as a Petri Net
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state chart example immune system model
Kam, Cohen, Harel. The Immune System as a
Reactive System.
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L-systems modelling plant morphology
subapical growth in Capsella bursa-pastoris
three signals used in Mycelis muralis
http//algorithmicbotany.org/vmm-deluxe/Section-09
.html
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Sydney Brenners questions

• the process of life may be described in the
  dynamical terms of trajectories, attractors, and
  phase spaces
• how does the egg form the organism?
• developmental trajectory to an attractor in the
  phase space of the organism ?
• how does a wounded organism regenerate exactly
  the same structure as before?
• injury as a small perturbation from the attractor
  in the phase space of the organism ?

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hierarchies of emergence

• life emerges from matter with structure and
  dynamics
• life as a structured, dynamical process (and not
  as a thing)