A light tunable differentiation system for the creation and control of consortia in yeast

Aditya, Chetan; Bertaux, François; Batt, Grégory; Ruess, Jakob

doi:10.1101/2021.06.09.447744

Cited by 5 publications

(11 citation statements)

References 64 publications

(80 reference statements)

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“…Further work is thus necessary to develop and test approaches for approximately calculating with multi-scale stochastic kinetic models [34,14]. 15…”

Section: Discussionmentioning

confidence: 99%

“…To be able to study the role of cell-to-cell variability in the design and functionality of synthetic circuits, we constructed a yeast optogenetic differentiation system whose dynamics can be wellobserved at the single-cell level thanks to the simultaneous presence of four different fluorescent reporters. Concretely, we started from a circuit design that we recently published [15] and used a Cre-recombinase (Cre) under control of the EL222 promoter [16] to trigger differentiation in controllable population fractions using global light stimulation patterns (Figure 1). When expressed, Cre excises a DNA fragment that is designed such that, upon recombination, cells switch from constitutively producing blue fluorescent protein (mCerulean) to producing green fluorescent protein (mNeonGreen).…”

Section: A Yeast Optogenetic Differentiation Systemmentioning

confidence: 99%

“…Here, we construct an artificial yeast differentiation system in which a Cre-recombinase is expressed from a light-inducible promoter and used to create dynamically controllable yeast communities of differentiated and undifferentiated cell types [15]. The system is equipped with four fluorescent reporter proteins to allow for simultaneous measurements of cellular processes and the differentiation state of cells.…”

Section: Introductionmentioning

confidence: 99%

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Using single-cell models to predict the functionality of synthetic circuits at the population scale

Aditya

Bertaux

Batt

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Significance At the single-cell level, biochemical processes are inherently stochastic. For many natural systems, the resulting cell-to-cell variability is exploited by microbial populations. In synthetic biology, however, the interplay of cell-to-cell variability and population processes such as selection or growth often leads to circuits not functioning as predicted by simple models. Here we show how multiscale stochastic kinetic models that simultaneously track single-cell and population processes can be obtained based on an augmentation of the chemical master equation. These models enable us to quantitatively predict complex population dynamics of a yeast optogenetic differentiation system from a specification of the circuit’s components and to demonstrate how cell-to-cell variability can be exploited to purposefully create unintuitive circuit functionality.

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“…Further work is thus necessary to develop and test approaches for approximately calculating with multi-scale stochastic kinetic models [34,14]. 15…”

Section: Discussionmentioning

confidence: 99%

Section: A Yeast Optogenetic Differentiation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using single-cell models to predict the functionality of synthetic circuits at the population scale

Aditya

Bertaux

Batt

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…A fundamental open problem in multicellular control applications is to guarantee the coexistence of different microbial strains growing in the same environment. Although some solutions were proposed in the literature that rely on the use of additional genetic pathways embedded into the cells and non-reversible differentiation systems [28,44], the ratiometric control framework we presented here provides an alternative approach that might be more appropriate in other scenarios; for example, in industrial applications, where efficient production is strongly required. We wish to emphasize that the framework can be used as a guideline to design control strategies that are able to work at scales larger than microfluidics.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in [21], the authors considered a microbial consortium already comprising two different cell strains, adjusting the dilution rate in a chemostat to regulate the relative numbers of the populations in the consortium, while the platform developed in [27], although powerful, relies on delivering a different control input to each cell and, in addition, all controlled cells are cultured in spatially distinct environments. Moreover, in [28], a non-reversible, efficient differentiation control mechanism has been proposed for the creation and maintenance of cellular sub-populations in single-strain microbial consortia, while a computer-controlled optogenetic platform for the regulation of the ratio of a two-strain Escherichia coli community has been recently presented in [29].…”

Section: Introductionmentioning

confidence: 99%

Ratiometric control of cell phenotypes in monostrain microbial consortia

Salzano

Fiore

Bernardo

2022

J. R. Soc. Interface.

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We address the problem of regulating and keeping at a desired balance the relative numbers between cells exhibiting a different phenotype within a monostrain microbial consortium. We propose a strategy based on the use of external control inputs, assuming each cell in the community is endowed with a reversible, bistable memory mechanism. Specifically, we provide a general analytical framework to guide the design of external feedback control strategies aimed at balancing the ratio between cells whose memory is stabilized at either one of two equilibria associated with different cell phenotypes. We demonstrate the stability and robustness properties of the control laws proposed and validate them in silico , implementing the memory element via a genetic toggle-switch. The proposed control framework may be used to allow long-term coexistence of different populations, with both industrial and biotechnological applications. As a representative example, we consider the realistic agent-based implementation of our control strategy to enable cooperative bioproduction of a dimer in a monostrain microbial consortium.

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Optimal control of bioproduction in the presence of population heterogeneity

Lunz

Bonnans²,

Ruess³

2023

J. Math. Biol.

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Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics this is typically accounted for. However, on the population level this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous counterparts and may thus lead to severely suboptimal control of bioproduction. In this work, we introduce a comprehensive mathematical framework for solving bioproduction optimal control problems in the presence of heterogeneity. We study population-level models in which such heterogeneity is retained, and propose order-reduction approximation techniques. The reduced-order models take forms typical of homogeneous bioproduction models, making them a useful benchmark by which to study the importance of heterogeneity. Moreover, the derivation from the heterogeneous setting sheds light on parameter selection in ways a direct homogeneous outlook cannot, and reveals the source of approximation error. With view to optimally controlling bioproduction in microbial communities, we ask the question: when does optimising the reduced-order models produce strategies that work well in the presence of population heterogeneity? We show that, in some cases, homogeneous approximations provide remarkably accurate surrogate models. Nevertheless, we also demonstrate that this is not uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.

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A light tunable differentiation system for the creation and control of consortia in yeast

Cited by 5 publications

References 64 publications

Using single-cell models to predict the functionality of synthetic circuits at the population scale

Using single-cell models to predict the functionality of synthetic circuits at the population scale

Ratiometric control of cell phenotypes in monostrain microbial consortia

Optimal control of bioproduction in the presence of population heterogeneity

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