Cell phenotypes as macrostates of the GRN dynamics

Area of Science:

• * Systems Biology
• * Evolutionary Developmental Biology
• * Theoretical Biology

Background:

• * Biological change occurs via development (individual lifetime) and evolution (across generations), operating on different timescales.
• * Gene regulatory networks (GRNs) are crucial for understanding these processes, but current models struggle to integrate both.
• * Existing theoretical models of GRNs face limitations in unifying developmental and evolutionary dynamics.

Purpose of the Study:

• * To develop a unified mathematical framework for modeling gene regulatory networks (GRNs) across developmental and evolutionary timescales.
• * To overcome limitations in current theoretical models that hinder the simultaneous study of development and evolution.
• * To enable the investigation of how genotypes change during evolution while observing phenotypic alterations.

Main Methods:

• * Review of theoretical modeling limitations in gene regulatory networks (GRNs).
• * Adaptation of Boolean network models for GRNs.
• * Introduction of a many-to-one mapping from phenotypes to attractors, decoupling genotype size from phenotype.
• * Utilizing generalized framework compatible with existing numerical techniques for identifying GRN control nodes.

Main Results:

• * A novel framework is proposed that accommodates phenotypic changes in evolving genotypes.
• * The many-to-one mapping allows phenotypes to correspond to multiple attractors, removing fixed genotype size constraints.
• * The generalized framework supports established methods for identifying key regulatory nodes controlling cell differentiation.
• * This approach bridges the gap between modeling development and evolution within a common mathematical language.

Conclusions:

• * The proposed generalized framework for gene regulatory networks (GRNs) effectively integrates developmental and evolutionary processes.
• * This model facilitates the study of phenotypic plasticity and evolution by allowing genotypes to change.
• * The framework maintains compatibility with existing computational tools for analyzing GRN control mechanisms.