Control circuit design for multicellular communities

In synthetic biology, we hope to generate tunable and characterized biological modules, and design and implement biological circuits by rewiring these modules to achieve novel functions in living organisms. These functions often depend on stable, robust and controllable gene expression, metabolism, and intercellular interactions, therefore cells can function as reliable 'living machines' in a group or community without catastrophic failures. To engineer biological circuits that realize stable and robust population level behaviors, we apply feedback control mechanism as a theory foundation and investigate multiscale design principles from single-cell level to popualtion level.
Layerd structure of single-cell level dynamics and population level dynamics. A. Sketch of the population module derived from single-cell modules that consist of intracellular gene regulation networks. Intracellular gene expression is determined by an input ui and environmental disturbance wi and the output is the expression level of target gene yi. We consider the population level input and output are the sum of heterogeneous single cells. B. Block diagram of population module. C. An example of the bistale swtich circuit in a cell population. The single-cell level dynamics exhibit ON/OFF switching and the input/output response exhibits hysteresis. The population level dynamics exhibit stablized overall expression and ultrasensitive input/output response that only emerge from single cells.
We consider each single cell as a dynamical system with its own input/output response of gene expression. Then the total population level dynamics is a sum of all single cells' dynamics, forming a population level system with integrated input/output response. To apply population level controller, the key questions involve: 1. deriving population-level dynamics from single-cell level dynamics; 2. desiging population-level feedback control; 3. implementing control circuits at single-cell level. Due to the stochasticity of biomolecular reactions in cells, single-cell level dynamics may exhibit strong heterogeneity. Therefore, we focus on a set of dynamics that show emerging properties at population level from single-cell level heterogeneity, for example, bistable and multistable gene expression, oscillatory gene expression, etc.