Engineering phenotypic heterogeneity for functional organization in microbial populations

Abstract

Engineering microbial populations to perform complex functions requires programming not only cellular behaviour but also the functional organization through which biological activities are distributed across populations. While phenotypic heterogeneity is often regarded as variability to suppress, it can also serve as a foundation for organizing specialized functions within genetically homogeneous microbial systems. Here, we introduce PROMETEO, a modular genetic-circuit framework that programs population composition through architecture-encoded regulatory and translational asymmetries. Using a library of 27 asymmetric bistable circuits, we demonstrate that circuit architecture reproducibly specifies phenotypic distributions spanning a broad range of population compositions without continuous external induction. Programmed population structures remained stable over serial propagation and were qualitatively conserved across Escherichia coli and Pseudomonas putida. Stochastic and deterministic modelling accurately predicted architecture-dependent population compositions and hysteresis regimes, providing a quantitative framework for rational design. We further show that programmable population composition supports multiple modes of functional organization, including stable parallel specialization, inducible temporal redistribution of cellular states, and spatial ecological compartmentalization through biofilm-associated and planktonic subpopulations. As a demonstration of these capabilities, architecture-programmed organization enabled distributed Congo Red biotransformation through coordinated reductive and oxidative activities, achieving up to 98% dye removal in spatially compartmentalized populations. Together, these results establish population composition as a programmable property of genetic circuit design and provide a general strategy for engineering distributed functions within genetically homogeneous microbial populations.