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Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks.

Jukka Intosalmi1, Kari Nousiainen1, Helena Ahlfors2

  • 1Department of Computer Science, Aalto University, Aalto, FI-00076, Finland.

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Summary

We developed a new method to infer gene regulatory networks that change over time. This approach captures dynamic network rewiring, crucial for understanding complex biological processes like cell differentiation.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Systems

Background:

  • Mechanistic models using ordinary differential equations (ODEs) accurately describe gene regulation dynamics.
  • Current ODE models assume static network structures, limiting their application when transient phenomena cause network rewiring.
  • Inferring gene regulatory networks with dynamic structures is challenging due to unobserved transient events like signaling pathway or epigenome changes.

Purpose of the Study:

  • To introduce a novel method for inferring dynamically evolving gene regulatory networks from time-course data.
  • To address the limitations of static network models in capturing transient phenomena and network rewiring.
  • To apply the method to understand regulatory interactions during T helper 17 (Th17) cell differentiation.

Main Methods:

  • Developed a method coupling mechanistic ODE models with a latent process to approximate network structure rewiring.
  • Utilized time-course data for inferring dynamic regulatory networks.
  • Applied the method to simulated data for performance evaluation and to real time-course RNA sequencing data from Th17 cell differentiation.

Main Results:

  • The novel method successfully infers dynamically evolving regulatory networks from time-course data.
  • Computational experiments demonstrated the method's capability to capture experimentally verified rewiring effects in the core Th17 regulatory network.
  • Predicted sequential activation of Th17 lineage-specific subnetworks that control differentiation in an overlapping manner.

Conclusions:

  • The proposed method enables the inference of dynamic gene regulatory networks, overcoming limitations of static models.
  • This approach provides insights into transient phenomena and network rewiring crucial for biological processes.
  • The findings offer predictions of sequential and overlapping subnetworks governing Th17 cell differentiation.