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ANC Workshop: David Sterratt and Iain Murray, Chair: Paolo Puggioni

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  • ANC Workshop Talk
When Oct 22, 2013
from 11:00 AM to 12:00 PM
Where IF 4.31/4.33
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David Sterratt

Integrating rule-based models with compartmental models of neurons OR

3001: A synaptic odyssey

Memories and behaviours are encoded in the structure of synapses, the connections between neurons. Synapses change their structure in response to the activity of the pre- and postsynaptic neurons they connect, along with other chemical signals in the nervous system.

This synaptic plasticity depends on the synaptic proteome, the collection of over 1000 proteins in the post-synaptic density. A subset of the proteome containing over 100 proteins associated with membrane-bound neurotransmitter receptors is particularly associated with synaptic plasticity (Pocklington & al. 2006, Molecular Systems Biology 2). Mutations in the genes underlying many of these proteins are linked to cognitive disorders such as schizophrenia and and mental retardation (ibid).

Our aims are to (i) understand how mutations in genes can lead to modifications in synaptic plasticity, which may underlie cognitive and affective disorders; and (ii) suggest potential targets for drug treatments.

In order to do this, we are working on combining models of the synaptic proteome (Sorokina & al. 2011, Molecular bioSystems 7, 2813--2823) with compartmental models of the electrical activity of neurons. Due to the number of proteins and the number of potential interactions, there is a combinatorially large number of complexes that can exist.  I will give a brief tutorial on how these networks can be modelled using rule-based modelling systems such as Kappa (Danos & al. 2007 "Scalable Simulation of Cellular Signaling Networks" 4807, 139--157, Shao: in: Programming Languages and Systems, Springer). I will then outline my work on incorporating these stochastic models into compartmental models of neurons, which are usually expressed as systems of coupled ordinary differential equations.


Iain Murray

Summarizing Bayesian posterior distributions

I will briefly discuss my entry last year to the Kaggle "Dark Worlds"

challenge, as a case study in approximate Bayesian inference, and what to do with the results.


Beni will talk in due course about more recent work: and