Estimating cellular parameters through optimization procedures: elementary principles and applications

Frontiers in Physiology
Akatsuki KimuraKazuyuki Nakamura

Abstract

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge...Continue Reading

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Citations

Apr 14, 2016·Biophysical Journal·Vincent NierPhilippe Marcq
Nov 20, 2016·Annual Review of Physiology·Elizabeth A SpechtAmy E Palmer
Jul 2, 2019·Frontiers in Genetics·Pavel LoskotLyudmila Mihaylova

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Methods Mentioned

BETA
fluorescence recovery after photobleaching
histone acetylation

Software Mentioned

SAAM
NucPosSimulator
Mathematica
GA

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