Molecular Noise and Cooperativity Drive Balanced Fate Decisions and Clonal Heterogeneity in Bistable Gene Circuits

Genetically identical cell populations frequently exhibit remarkable phenotypic variation, a feature that is commonly ascribed to the interaction of molecular noise with nonlinear gene-regulatory networks. Here, we examine the traditional genetic toggle switch as a simple model for stochastic dynamics-based bistable cell-fate decisions. We used a dry-lab method to model clonal populations of 100 − 10,000 "cells" as autonomous realizations of noisy toggle-switch dynamics, taking into account noise throughout temporal evolution as well as initial condition heterogeneity. Cells deterministically resolve into one of two mutually exclusive states (A-dominant or B-dominant) when noise is absent. Clonal heterogeneity was recapitulated by adding molecular noise during dynamics, which significantly changed the results and produced balanced percentages of A- and B-dominant cells. The mean fractions of A-dominant cells stabilized around ~ 0.5 +/- 0.03 across replicates, indicating a shift from entirely deterministic outcomes (σ = 0) to noise-balanced destinies (σ ~ 0.15–0.6) according to a systematic sweep of noise amplitude (σ). This phenomenon was found to be consistent across several noise models (additive versus multiplicative), production strengths (α = 3–7), and Hill coefficients (n = 2–6), according to robustness analyses. Additionally, population-level fractions remained constant when scaling up to 10,000 cells, suggesting that the effect is not a result of a limited sample size. Collectively, our findings demonstrate that molecular noise can serve as a balancing mechanism that diversifies destiny in clonal populations rather than only upsetting bistability. This establishes a conceptual connection between microbial persistence, gene-circuit multistability, and differentiation processes in which genetically identical cells take on various phenotypes. Our results demonstrate how crucial stochastic modeling is for revealing broad rules of fate diversification in biological systems with noise.