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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

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Estimating cellular parameters through optimization procedures: elementary principles and applications.

Akatsuki Kimura¹, Antonio Celani², Hiromichi Nagao³

¹Cell Architecture Laboratory, National Institute of Genetics Mishima, Japan ; Department of Genetics, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies) Mishima, Japan ; Transdisciplinary Research Integration Center and Data Centric Science Research Commons, Research Organization of Information and Systems Tokyo, Japan.

Frontiers in Physiology

|March 19, 2015

Summary

This summary is machine-generated.

This study presents optimization methods for quantitative biology models to accurately fit experimental data. These techniques, including gradient and sampling approaches, help researchers find optimal model parameters for deeper biological insights.

Keywords:

likelihood model selection parameter optimization probability density function quantitative modeling

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Area of Science:

Quantitative biology
Systems biology
Computational biology

Background:

Quantitative biology aims to understand biological systems through mathematical models.
Developing accurate quantitative models requires fitting model parameters to experimental data.

Purpose of the Study:

To introduce optimization procedures for parameter searching in quantitative biological models.
To enable models to reproduce experimental data and gain mechanistic insights.

Main Methods:

Parameter optimization using gradient approaches to find local minima/maxima.
Stochastic processes and sampling approaches for global optimization.
Bayesian inference combined with optimization for parameter and likelihood estimation.

Main Results:

Optimization procedures effectively identify model parameters that reproduce experimental data.
Methods allow for estimation of optimal parameters and the likelihood function.
Successful application in transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation.

Conclusions:

Parameter optimization is crucial for developing realistic biological models.
These methods provide mechanistic insights into complex biological phenomena.
Empowers biologists to create and refine quantitative models for their research.