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Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering.

Benjamin Ballnus1,2, Steffen Schaper3, Fabian J Theis1,2

  • 1Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.

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This study introduces a novel adaptive parallel tempering algorithm for efficient parameter estimation in mathematical models. The new method improves sampling efficiency for complex biological models, outperforming existing techniques.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Mathematical models are crucial for studying cellular processes and signal transduction.
  • Parameter estimation for these models often uses optimization and sampling methods, but efficiency is limited by model properties like non-identifiabilities and complex posterior distributions.

Purpose of the Study:

  • To develop an improved optimization and sampling method for mathematical models.
  • To enhance the efficiency of parameter estimation, particularly for models with multi-modal posterior distributions.

Main Methods:

  • A region-based adaptive parallel tempering algorithm was developed.
  • This algorithm combines established methods to address shortcomings and improve sampling efficiency.
  • Its performance was evaluated on benchmark problems and biochemical reaction network models.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to state-of-the-art methods.
  • It showed improvements in calculation efficiency and mixing.
  • The algorithm successfully adapted to problem-specific posterior distributions, including modes and valleys.

Conclusions:

  • The novel algorithm offers enhanced efficiency for parameter estimation in mathematical models.
  • Its adaptive nature makes it broadly applicable to various model classes, including biochemical networks.
  • The method is particularly beneficial for models with challenging posterior distributions.