SCB : 1 Department of Computer Science Simulation and Complexity SCB : Simulating Complex Biosystems...

SCB : 1Department of Computer Science

Simulation and Complexity

SCB : Simulating Complex Biosystems

Susan StepneyDepartment of Computer

Science

Leo CavesDepartment of

Biology


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


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


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, …


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 difficul

t bit !

the easy bit !


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, …


modelling networks

• networks everywhere• regulatory networks• metabolic networks • signalling networks• …

• connectivity and topology• random• hierarchical• scale free, small world, …

• “robust yet fragile”

• motifs, modules, …


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]

211 2 1 1

221 2 2 2

( , )

( , )

cf c c k c

tc

g c c k ct


Petri net example : Fas-induced apoptosis

[Matsuno et al, 2003]

as a “cartoon”

as a Petri Net


state chart example : immune system model

[Kam, Cohen, Harel. The Immune System as a Reactive System.]


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


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 ?


hierarchies of emergence

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

“thing”)

SCB : 1 Department of Computer Science Simulation and Complexity SCB : Simulating Complex Biosystems...

Documents

Transcript of SCB : 1 Department of Computer Science Simulation and Complexity SCB : Simulating Complex Biosystems...