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Data-driven network models for genetic circuits from time-series data with incomplete measurements.

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Summary

This study introduces dynamical structure functions to model synthetic gene networks with incomplete data. This data-driven approach visualizes genetic circuit relationships as time-dependent functions, aiding in understanding network dynamics.

Keywords:
data-driven modellingdynamic networksdynamical structure functionsgenetic circuitsgenetic networksnetwork reconstruction

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Area of Science:

  • Synthetic biology
  • Systems biology
  • Computational biology

Background:

  • Synthetic gene networks are often viewed as static graphs, which contrasts with the dynamic nature of biomolecular interactions.
  • Unmeasured biological states complicate the verification of synthetic gene network topology and dynamics in vivo or in vitro.

Purpose of the Study:

  • To introduce a new class of mesoscopic, data-driven models called dynamical structure functions for gene networks with incomplete state measurements.
  • To enable the discovery and visualization of graphical relationships in genetic circuits as time-dependent functions.

Main Methods:

  • Development of a network reconstruction algorithm and codebase for inferring dynamical structure functions from data.
  • Theoretical proof demonstrating that dynamical structure functions can estimate crosstalk fluctuations.

Main Results:

  • The dynamical structure function provides a data-driven method to model gene networks with unmeasured states.
  • The approach enables visualization of genetic circuit relationships as dynamic functions, not static weights.
  • Numerical examples illustrate the application of dynamical structure functions.
  • The method explains failure modes in two experimental genetic circuits.

Conclusions:

  • Dynamical structure functions offer a powerful tool for analyzing and understanding the dynamics of synthetic gene networks, especially under conditions of incomplete data.
  • This data-driven approach enhances the ability to predict and troubleshoot the behavior of engineered biological systems.