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Effective Multi-level Monte Carlo Methods for Stochastic Biochemical Kinetics

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thesis
posted on 24.05.2021, 12:46 by Priyabrata Senapati
Stochastic mathematical models are essential for an accurate description of biochemical processes at the cellular level. The effect of random fluctuations may be significant when some species have low molecular counts. While exact stochastic simulation methods exist, they are typically expensive on systems arising in applications. Thus more effective strategies are required for simulating complex stochastic models of biochemical system. Often, the expected value of some function of the final time solution of the stochastic model is of interest. Then, the approach employing multi-level Monte Carlo methods is more efficient than the traditional techniques. In this thesis, we study multi-level Monte Carlo (MLMC) schemes for a reliable and effective simulation of stochastic models of biochemical kinetics. The advantages of these MLMC strategies are illustrated on several biochemical models arising in applications.

History

Language

eng

Degree

Master of Science

Program

Applied Mathematics

Granting Institution

Ryerson University Silvana Ilie

LAC Thesis Type

Thesis