Effects of spatial heterogeneity on bacterial genetic circuits

Abstract

Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell's volume and a circuit's DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reactiondiffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects.

Context-aware synthetic biology by controller design: Engineering the mammalian cell

“…The eukaryotic cell context presents a further contextual challenge due to the non-homogeneous distribution of cellular parts and resources in cellular compartments, often requiring shuttling of components for synthetic genetic parts between the nucleus and cytoplasm. As a result, design of exogenously expressed parts needs to consider additional requirements to ensure their correct spatial localization following expression (Alberts et al, 2014;Barajas and Vecchio, 2020). For example, nuclear localiza-tion and export signals are used to direct proteins into and out of the nucleus, respectively.…”

Optimal control of bioproduction in the presence of population heterogeneity

“…Deriving these reduced models from the heterogeneous setting provides several key advantages over more common ODE approaches that ignore heterogeneity from the outset. For example, many parameters in the reduced-order ODE models may be expressed as functions of the underlying heterogeneous single-cell dynamics, allowing closed-form expressions for parameters that would otherwise have to be fitted [5]. Moreover, the derivation allows us to clearly identify the source of the discrepancy between the reduced-order models and their heterogeneous counterparts.…”

In VivoSampling of Intracellular Heterogeneity ofPseudomonas putidaEnables Multiobjective Optimization of Genetic Devices

“…Furthermore, proximity to the origin of replication − and inclusion in different chromosomal superloops − may enter considerable differences in protein–DNA interaction parameters. , Finally, every chromosomal location is submitted to a very variable degree of readthrough and pervasive transcription . While the ensuing context sensitivity has been generally considered a limitation for sound biological engineering, we wondered whether such intracellular heterogeneity could be instead leveraged to optimize the performance of genetic constructs. ,− Not by diversifying regulatory sequences but by physically sampling the three-dimensional (3D) parameter landscape that different chromosomal locations are exposed to.…”