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Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
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Biochemical fluctuations, optimisation and the linear noise approximation.

Jürgen Pahle1, Joseph D Challenger, Pedro Mendes

  • 1School of Computer Science and Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, UK. juergen.pahle@manchester.ac.uk

BMC Systems Biology
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method combining linear noise approximation with optimization to efficiently study molecular fluctuations in biochemical systems. It helps identify parameter ranges that minimize or maximize these fluctuations, even with unknown parameters.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Stochastic fluctuations are vital in biochemical systems but computationally demanding to study.
  • High computational cost of stochastic simulation algorithms hinders analysis, especially with uncertain parameters.

Purpose of the Study:

  • To develop an efficient method for investigating stochastic fluctuations in biochemical models.
  • To combine linear noise approximation with optimization for parameter space exploration.

Main Methods:

  • Utilized linear noise approximation to estimate particle number covariances.
  • Integrated optimization methods in a closed-loop system to find extrema of covariances.
  • Applied the strategy to models of mitogen-activated protein kinase (MAPK) signaling.

Main Results:

  • Successfully identified parameter conditions for maximal covariances in an ERK signaling model.
  • Efficiently found conditions to minimize the coefficient of variation for Hsp27 in a p38 MAPK model.
  • Investigated correlations within parallel signaling branches (MKK3 and MKK6).

Conclusions:

  • The proposed strategy offers a practical and efficient approach for studying fluctuations in biochemical models.
  • This method is effective even when model parameters are not fully characterized.