Periodic Reporting for period 1 - BridgingScales (From single cells to microbial consortia: bridging the gaps between synthetic circuit design and emerging dynamics of heterogeneous populations)
Berichtszeitraum: 2023-05-01 bis 2025-10-31
A key turning point in the evolution of life was the transition from single-cell to multicellular organisms and the optimization of fitness via division of labour and specialization. Similarly, microorganisms have evolved equivalent strategies by forming communities or consortia. Division of labour in isogenic microbial populations is often implemented by mechanisms that create or act upon population heterogeneity to diversify functionality. Rational design in synthetic biology, on the other hand, is focused on the engineering of gene circuits with deterministically predictable functionality within single cells. While synthetic biology has certainly come a long way, predictable functionality of circuits in growing microbial populations still remains elusive or limited to tightly constrained operating conditions. We will develop novel mathematical methods to characterize and control the dynamics of synthetic gene circuits within growing microbial populations. We will develop a modelling framework and novel computational methods that take both stochasticity of single-cell processes and consequences of heterogeneity for population dynamics into account. On the mathematical side, this necessitates coupling single-cell stochastic processes to state dependent population processes such as growth or selection. We will develop methods for parameter inference, experimental design and control for such models. This will enable the construction of models that can be used to design synthetic circuits that function as specified within growing populations and that can be deployed to regulate single-cell processes such that desirable dynamics emerge at the scale of populations and consortia. We will apply the methodology for bioproduction problems in which proteins that are hard to fold need to be produced. Overproducing such proteins impairs cellular growth, which creates couplings between single-cell and population processes and raises the need to feedback control production.
We have started the project by developing a multi-scale modeling framework that extends stochastic chemical kinetics and the classical chemical master equation to the context of growing populations of cell. We have derived a general equation for the time evolution of the expected population distribution for a stochastic biochemical process operating within a growing population. In the case where all single cells in a population are growing at a the same rate and this growth rate does not depend on the biochemical process that operates inside cells, our general equation reduces to the well-known standard chemical master equation. For applications in synthetic biology, however, single cell growth rates will typically depend on the synthetic gene network that is placed into cells and our general equation needs to be used.
Our novel multi-scale modeling framework allows us to adequately represent the coupled dynamics of engineered single-cell gene circuits and growth of microbial populations and consortia. As such, it can serve as the core modeling tool to address the applications that will be considered in this ERC project.
Our novel multi-scale modeling framework allows us to adequately represent the coupled dynamics of engineered single-cell gene circuits and growth of microbial populations and consortia. As such, it can serve as the core modeling tool to address the applications that will be considered in this ERC project.
Multi-scale models that aim to jointly track biochemical processes inside single cells and growth of the full cell population typically suffer from analytical and even computational intractability since every chemical reaction in every cell of a possibly large population needs to be tracked in the model. As such, the current state of the art in modeling stochastic biochemical processes inside single cells is to simply ignore any effects that the process may have on cellular growth rates since otherwise one would have to addtionally track dynamics at the population scale, which would render the model intractable. The results that we have obtained so far in this project show that it is possible to formulate a multi-scale model and then to reduced this model via calculation of an expectation to a model that is not any harder to simulate than a standard single-cell model but that does take the population context into account.