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

2. SCB : 2 Department of Computer Science 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. SCB : 3 Department of Computer Science 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. SCB : 4 Department of Computer Science 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. SCB : 5 Department of Computer Science modelling and simulation the model (eg mathematical equations) the solution (consequences of the model) analysis (eg solving the equations) the domain (the real world) modelling the world (concept mapping) the prediction (real world consequences) deducing the consequences (concept mapping) formal informal update, refine, and iterate : if the model and reality disagree, it is the model that is wrong the difficult bit ! the easy bit !

6. SCB : 6 Department of Computer Science 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. SCB : 7 Department of Computer Science 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. SCB : 8 Department of Computer Science reaction-diffusion equations non-linear f and g, coupled –reaction rates, dependent on c 1 and c 2 spatial patterns –if different diffusion rates k 1  k 2 local activation + long range inhibition –animal coat patterns [Alan Turing 1952]

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

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

11. SCB : 11 Department of Computer Science L-systems : modelling plant morphology http://algorithmicbotany.org/vmm-deluxe/Section-09.html subapical growth in Capsella bursa-pastoris three signals used in Mycelis muralis

12. SCB : 12 Department of Computer Science 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. SCB : 13 Department of Computer Science hierarchies of emergence life emerges from matter with structure and dynamics –life as a structured, dynamical process (and not as a “thing”)