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Author:Paul, Debdas
Title:Efficient Parameter Inference for Stochastic Chemical Kinetics
Publication type:Master's thesis
Publication year:2014
Pages:34      Language:   eng
Department/School:Perustieteiden korkeakoulu
Main subject:Computational Systems Biology   (IL3013)
Supervisor:Lähdesmäki, Harri ; Aurell, Erik
Instructor:Engblom, Stefan
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201410042708
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  1729   | Archive
Keywords:stochastic chemical kinetics
systems biology
parameter inference
Markov Chain Monte Carlo
Chemical Master Equation
Abstract (eng):Parameter inference for stochastic systems is considered as one of the fundamental classical problems in the domain of computational systems biology.
The problem becomes challenging and often analytically intractable with the large number of uncertain parameters.
In this scenario, Markov Chain Monte Carlo (MCMC) algorithms have been proved to be highly effective.
For a stochastic system, the most accurate description of the kinetics is given by the Chemical Master Equation (CME).
Unfortunately, analytical solution of CME is often intractable even for considerably small amount of chemically reacting species due to its super exponential state space complexity.
As a solution, Stochastic Simulation Algorithm (SSA) using Monte Carlo approach was introduced to simulate the chemical process defined by the CME.
SSA is an exact stochastic method to simulate CME but it also suffers from high time complexity due to simulation of every reaction.
Therefore computation of likelihood function (based on exact CME) and hence the rejection step (in an acceptance-rejection based MCMC like Metropolis-Hastings) becomes expensive.
In this generic work, we introduce different approximations of CME as a pre-conditioning step to the full MCMC in order to make rejection cheaper.
The goal is to avoid expensive computation of exact CME as far as possible.
We show that, with effective pre-conditioning scheme, one can save a considerable amount of exact CME computations maintaining similar convergence characteristics.
Additionally, we investigate three different sampling techniques (dense sampling of the same process, longer time sampling of the same process and i.i.d sampling of different processes) under which convergence of MCMC using exact CME for parameter inference can be analyzed.
We find that under i.i.d sampling, better convergence can be achieved than that of other two techniques (atleast for the processes, we have investigated).
We verify our theoretical findings for two different fundamental processes: linear birth-death and dimerization.
Although, we succeed in saving a considerable amount of CME computations for two simple one-dimesional processes, challenges remain in extending it for higher dimensions which is a non-trivial problem.
ED:2014-10-05
INSSI record number: 49769
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