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SUMMARY:Tensor Methods for Parameter Estimation and Bifurcation Analysis o
 f Stochastic Reaction Networks - Shuohao Liao (University of Oxford)
DTSTART:20160315T150000Z
DTEND:20160315T160000Z
UID:TALK64954@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Intracellular networks of interacting bio-molecules carry out 
 many essential functions in living cells\, but the molecular events underl
 ying the functioning of such networks are ubiquitously random. Stochastic 
 modelling provides an indispensable tool for understanding how cells contr
 ol\, exploit and tolerant the biological noise. A common challenge of stoc
 hastic modelling is to calibrate a large number of model parameters agains
 t the experimental data. Another difficulty is to study how the behaviours
  of a stochastic model depends on its parameters\, i.e. whether a change i
 n model parameters can lead to a significant qualitative change in model b
 ehaviours (bifurcation). One fundamental reason for these challenges is th
 at the existing computational approaches are susceptible to the curse of d
 imensionality\, i.e.\, the exponential growth in memory and computational 
 requirements in the dimension (number of species and parameters). Herein\,
  we have developed a tensor-based computational framew&nbsp\; ork to addre
 ss this computational challenge. It is based on recently proposed low-para
 metric\, separable tensor-structured representations of classical matrices
  and vectors. The framework covers the whole process from solving the unde
 rlying equations to automated parametric analysis of the stochastic models
  such that the high cost of working in high dimensions is avoided. One not
 able advantage of the proposed approach lies in its ability to capture all
  probabilistic information of stochastic models all over the parameter spa
 ce into one single tensor-formatted solution\, in a way that allows linear
  scaling of basic operations with respect to the number of dimensions. Wit
 hin such framework\, the existing algorithms commonly used in the determin
 istic framework can be directly used in stochastic models\, including para
 meter inference\, robustness analysis\, sensitivity analysis\, and stochas
 tic bifurcation analysis.
LOCATION:Seminar Room 2\, Newton Institute
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