Novel Tunable Spatio-Temporal Patterns From a Simple Genetic Oscillator Circuit

Abstract

Multicellularity, the coordinated collective behavior of cell populations, gives rise to the emergence of self-organized phenomena at many different spatio-temporal scales. At the genetic scale, oscillators are ubiquitous in regulation of multicellular systems, including during their development and regeneration. Synthetic biologists have successfully created simple synthetic genetic circuits that produce oscillations in single cells. Studying and engineering synthetic oscillators in a multicellular chassis can therefore give us valuable insights into how simple genetic circuits can encode complex multicellular behaviors at different scales. Here we develop a study of the coupling between the repressilator synthetic genetic ring oscillator and constraints on cell growth in colonies. We show in silico how mechanical constraints generate characteristic patterns of growth rate inhomogeneity in growing cell colonies. Next, we develop a simple one-dimensional model which predicts that coupling the repressilator to this pattern of growth rate via protein dilution generates traveling waves of gene expression. We show that the dynamics of these spatio-temporal patterns are determined by two parameters; the protein degradation and maximum expression rates of the repressors. We derive simple relations between these parameters and the key characteristics of the traveling wave patterns: firstly, wave speed is determined by protein degradation and secondly, wavelength is determined by maximum gene expression rate. Our analytical predictions and numerical results were in close quantitative agreement with detailed individual based simulations of growing cell colonies. Confirming published experimental results we also found that static ring patterns occur when protein stability is high. Our results show that this pattern can be induced simply by growth rate dilution and does not require transition to stationary phase as previously suggested. Our method generalizes easily to other genetic circuit architectures thus providing a framework for multi-scale rational design of spatio-temporal patterns from genetic circuits. We use this method to generate testable predictions for the synthetic biology design-build-test-learn cycle.

Bacterial degrons in synthetic circuits

“…Another clever use of degradation tags is employing them to create localized protein expression patterns. There is an increasing interest in developing spatial patterning strategies in bacteria [ 139 – 141 ], as this could give us a better understanding of fundamental biological processes from developmental biology to tissue engineering [ 142 , 143 ]. For example, FtsH [ 144 ] and Prc (Tsp) [ 145 ] are proteases responsible for the degradation of membrane proteins and periplasmic proteins, respectively, and hold potential for the design of membrane-localized or periplasmic circuits.…”

“…Another clever use of degradation tags is employing them to create localized protein expression patterns. There is an increasing interest in developing spatial patterning strategies in bacteria [139][140][141], as this could give us a better understanding of fundamental biological processes from developmental royalsocietypublishing.org/journal/rsob Open Biol. 12: 220180 biology to tissue engineering [142,143].…”

WebCM: A Web-Based Platform for Multiuser Individual-Based Modeling of Multicellular Microbial Populations and Communities

“…CellModeller 2 is a Python framework for simulating such IBMs that requires minimal Python scripting abilities to construct and run models. It has been applied to model and analyze many multicellular behaviors of microbial populations, including the effect of contact-dependent inhibition (CDI) systems at the single-cell level, 3 emergent properties due to physical interactions between cells, 4 interactions between antibiotic-resistant and susceptible strains in the pathogen Pseudomonas aeruginosa, 5 impact of cell morphology on microbial community structure, 6 influence of adhesive intercellular interactions on biofilm spatial structure, 7 understanding the evolutionary function of bacterial weapons and their effect on the function of probiotics, 8 symmetry-breaking and domain-specific cell regulation as elementary functions for the prototyping of morphogenetic programs, 9 formation of traveling waves of gene expression due to growth rate heterogeneities in colonies, 10 and training deep convolutional neural networks (CNNs) using simulated biofilm images for automated morphometric cell classifications in multipopulation biofilms. 11 In order to run large simulations required to model typical bacterial populations, CellModeller requires a processor with multiple cores or a graphics processing unit (GPU) enabling parallelization of the core algorithms.…”

Voronoi spatial pattern construction through bacterial colony simulation

“…For example, as observed in 14 , cell morphology significantly influences cell lineage fate. Patterns can also be affected by protein degradation and the maximum repressor expression ratio 15 .…”

“…Using individual-based models (IBMs), valuable information is obtained on the emerging collective properties of bacterial populations due to their interactions with the environment. These models simulate cell growth and gene expression analogously to controlled experiments with specific properties, making IBMs an essential tool for analyzing and designing emergent properties of genetic circuits operating in multicellular environments 15 .…”