Synthetic Pattern Formation

Luo, Nan; Wang, Shangying; Li, You

doi:10.1021/acs.biochem.8b01242

Cited by 41 publications

(39 citation statements)

References 54 publications

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“…While these circuits are often studied as dynamical systems in single cells or well mixed populations, the function of genetic circuits has also been studied in cell colonies (Luo et al, 2019 ; Santos-Moreno and Schaerli, 2019 ) through the engineering of patterns of gene expression such as symmetry breaking (Nuñez et al, 2017 ), Turing patterns (Karig et al, 2018 ), fractal patterns (Rudge et al, 2013 ), tissue like structures (Toda et al, 2018 ; Healy and Deans, 2019 ), among others. These emergent spatio-temporal patterns depend on mechanical constraints on cells, which are the result of cell-cell and cell-substrate interactions.…”

Section: Introductionmentioning

confidence: 99%

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

Feliú

Vidal

Silva

et al. 2020

Front. Bioeng. Biotechnol.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Feliú

Vidal

Silva

et al. 2020

Front. Bioeng. Biotechnol.

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“…quences that do not naturally coexist [45][46][47]. Such approach has enabled researchers to mimic gradient-based positional information developmental systems [48][49][50], however self-organized systems -characterized by simple interacting units and horizontal exchanges of information like in Turing's mechanism-have proven to be more elusive to engineer [51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Turing patterns in engineered microbial consortia

Duran-Nebreda

Pla

Vidiella

et al. 2020

Preprint

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Multicellular entities are characterized by exquisite spatial patterns, intimately related to the functions they perform. Oftentimes these patterns emerge as periodic structures with a well-defined characteristic scale. A candidate mechanism to explain their origins was early introduced by Alan Turing through the interaction and diffusion of two so called morphogens. Unfortunately, most available evidence for Turing patterns in biology is usually obscured by the tangled nature of regulatory phenomena, making difficult to validate Turing's proposal in developmental processes. Here we follow a different approach, by designing synthetic genetic circuits in engineered E. coli strains that implement the essential activator-inhibitor motif (AIM) using a two-cell consortium. The two diffusible compartments are one cell type (activator, small-diffusion component) and a small signal molecule (a homoserine lactone, acting as the fast-diffusing inhibitor). Using both experimental results, we show that the AIM is capable of generating diffusion-induced instabilities leading to regular spatial patterns. The artificial construction taken here can help validate developmental theories and identify universal properties underpinning biological pattern formation. The implications of the work for the area of synthetic developmental biology are outlined.

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“…Mechanism-based models are useful for testing our understanding of the systems of interest [7][8][9][10][11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Massive computational acceleration by using neural networks to emulate mechanism-based biological models

Wang

Fan

Luo

et al. 2019

Preprint

Self Cite

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Mechanism-based mathematical models are the foundation for diverse applications. It is often critical to explore the massive parametric space for each model. However, for many applications, such as agent-based models, partial differential equations, and stochastic differential equations, this practice can impose a prohibitive computational demand. To overcome this limitation, we present a fundamentally new framework to improve computational efficiency by orders of magnitude. The key concept is to train an artificial neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training of the neural network. The trained neural network can then be used to explore the system dynamics of a much larger parametric space. We demonstrate this notion by training neural networks to predict self-organized pattern formation and stochastic gene expression. With this framework, we can predict not only the 1-D distribution in space (for partial differential equation models) and probability density function (for stochastic differential equation models) of variables of interest with high accuracy, but also novel system dynamics not present in the training sets. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can potentially be a platform for faster parametric space screening of biological models with user defined objectives.

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Synthetic Pattern Formation

Cited by 41 publications

References 54 publications

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

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

Synthetic Turing patterns in engineered microbial consortia

Massive computational acceleration by using neural networks to emulate mechanism-based biological models

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