Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation

Area of Science:

• Systems Biology
• Computational Biology
• Synthetic Biology

Background:

• Gene regulatory networks (GRNs) control cellular functions.
• Modeling GRNs is crucial for understanding biological systems.
• Current modeling approaches can be rigid in topology definition.

Purpose of the Study:

• To present a novel, compositional approach for constructing dynamic models of gene regulatory networks.
• To enable flexible and modular modeling of biological systems.
• To demonstrate the utility of the approach using a synthetic biological circuit.

Main Methods:

• Introduction of "gene gates" as computational elements defining input-output relationships.
• Mapping gene gate kinetics to stochastic processes and ordinary differential equations (ODEs).
• Utilizing stochastic pi-calculus for a compositional modeling framework.

Main Results:

• The proposed method allows for autonomous network elements with wiring defined by input-output relationships.
• The compositional scheme overcomes limitations of fixed topology ODE models.
• The stochastic repressilator, an artificial cellular clock, was successfully modeled and simulated, exhibiting oscillations without cooperative effects.

Conclusions:

• The gene gate approach provides a modular and flexible framework for dynamic GRN modeling.
• Stochastic pi-calculus facilitates compositional construction of complex biological models.
• This method simplifies the simulation of synthetic biological systems like cellular clocks.