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A mixed integer linear programming (MILP) framework for inferring time delay in gene regulatory networks.

M S Dasika1, A Gupta, C D Maranas

  • 1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA. msd179@psu.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|March 3, 2004
PubMed
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This study introduces a new method to model gene regulatory networks, accounting for time delays. Incorporating time delays results in sparser networks and better explains gene expression data variations.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) control cellular functions.
  • Understanding GRNs is crucial for deciphering complex biological processes.
  • Traditional models often overlook the impact of time delays in gene regulation.

Purpose of the Study:

  • To develop an optimization-based framework for inferring GRNs that explicitly accounts for time delays.
  • To assess the impact of time delays on the accuracy and sparsity of inferred GRNs.
  • To validate the framework using both simulated and real gene expression data.

Main Methods:

  • Utilized a basic linear model of gene regulation.
  • Incorporated Boolean variables to represent discrete time delays.

Related Experiment Videos

  • Inferred optimal time delays by minimizing prediction errors against experimental expression profiles.
  • Performed computational experiments on synthetic (in silico) and real microarray datasets.

    • Main Results:

    • Neglecting time delays in systems with inherent delays necessitates a larger number of parameters for accurate modeling.
    • Analysis of real microarray data revealed a significant prevalence of time-delayed interactions in gene regulation.
    • The inclusion of time delays led to the inference of sparser gene regulatory networks.
    • Accounting for time delays explained a greater proportion of variance in real data compared to randomized data.

    Conclusions:

    • Time delay is a ubiquitous factor in gene regulation that significantly impacts network inference.
    • The proposed optimization framework effectively infers GRNs with time delays, leading to more parsimonious and accurate models.
    • This approach enhances our understanding of gene regulatory dynamics and provides a more realistic representation of biological systems.