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BridgingScales

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From single cells to microbial consortia: bridging the gaps between synthetic ci...

From single cells to microbial consortia: bridging the gaps between synthetic circuit design and emerging dynamics of heterogeneous populations 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 equivale... ver más

30/04/2028

INRIA

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQU... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 1 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-12-15

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2022-STG: ERC STARTING GRANTS

I+D

Cerrada hace 2 años

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1 Participantes

INRIA

1.50M€ | Lider

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Duración del proyecto: 64 meses Fecha Inicio: 2022-12-15
Fecha Fin: 2028-04-30

Presupuesto del proyecto 1M€

Descripción del proyecto 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.

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