Efficient Parameter Inference for Stochastic Chemical Kinetics

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Engblom, Stefan
dc.contributor.author Paul, Debdas
dc.date.accessioned 2014-10-03T07:21:17Z
dc.date.available 2014-10-03T07:21:17Z
dc.date.issued 2014-09-29
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/14089
dc.description.abstract 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 (at least 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. en
dc.format.extent 34+0
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Efficient Parameter Inference for Stochastic Chemical Kinetics en
dc.type G2 Pro gradu, diplomityö en
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword stochastic chemical kinetics en
dc.subject.keyword systems biology en
dc.subject.keyword parameter inference en
dc.subject.keyword Markov chain Monte Carlo en
dc.subject.keyword chemical master equation en
dc.identifier.urn URN:NBN:fi:aalto-201410042708
dc.programme.major Computational Systems Biology fi
dc.programme.mcode IL3013 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Lähdesmäki, Harri
dc.programme Master's Degree Programme in Computational and Systems Biology (euSYSBIO) fi


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