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Monte Carlo samplers for efficient network inference.

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This study introduces a novel Bayesian nonparametric framework to infer biological reaction networks from snapshot data. The method efficiently estimates network structure and parameters, overcoming challenges in gene regulatory network analysis.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Biological processes are often studied using snapshot data, which can be stochastic and require probabilistic models for network inference.
  • Inferring underlying reaction networks, including the number of nodes and kinetic parameters, from snapshot data presents significant challenges due to data uncertainty and timescale separations.

Purpose of the Study:

  • To develop a Bayesian nonparametric framework capable of simultaneously estimating the number of nodes and parameters in biological reaction networks from snapshot data.
  • To address the limitations of existing parametric Bayesian methods in handling large timescale separations and unknown network structures.

Main Methods:

  • A hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler combining Hamiltonian Monte Carlo (HMC), Adaptive Metropolis Hastings (AMH), and Parallel Tempering.
  • HMC for efficient parameter space exploration, AMH for proposing probable models, and Parallel Tempering for enhanced sampling efficiency.

Main Results:

  • The proposed hybrid MCMC sampler effectively addresses the challenges of inferring network structure and parameters from stochastic snapshot data.
  • Demonstrated application to synthetic data mimicking single molecule RNA fluorescence in situ hybridization (RNA-FISH) data.

Conclusions:

  • The developed Bayesian nonparametric framework provides a robust solution for learning dynamical models of biological networks from snapshot data.
  • This method advances the analysis of transcriptional networks and similar biological systems where inferring underlying dynamics from limited data is crucial.