Simulation and Complexity SCB : Simulating Complex Biosystems

1. Simulation and ComplexitySCB : Simulating Complex Biosystems Leo Caves Department of Biology Susan Stepney Department of Computer Science

2. 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

3. 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

4. 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, …

5. 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) the difficult bit ! informal 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

6. 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, …

7. modelling networks • networks everywhere • regulatory networks • metabolic networks • signalling networks • … • connectivity and topology • random • hierarchical • scale free, small world, … • “robust yet fragile” • motifs, modules, …

8. reaction-diffusion equations • non-linearf 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]

9. Petri net example : Fas-induced apoptosis [Matsuno et al, 2003] as a “cartoon” as a Petri Net

10. state chart example : immune system model [Kam, Cohen, Harel. The Immune System as a Reactive System.]

11. 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

12. Sydney Brenner’s 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 ?

13. hierarchies of emergence • life emerges from matter with structure and dynamics • life as a structured, dynamical process(and not as a “thing”)