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OptimHist

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Optimization and historical contingency in living systems a biophysical approac...

Optimization and historical contingency in living systems a biophysical approach Populations of living organisms are pushed to optimality by evolution, but may also be shaped by the contingency of their evolutionary history. The recent explosion of sequence data gives us access to the outcomes of molecular evo... ver más

28/02/2025

EPFL

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-11-13

ERC-2019-STG: ERC Starting Grant

I+D

Cerrada hace 6 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

EPFL

1.50M€ | Lider

LEAR CORPORATION HOLDING SPA...

1.87M€ | Participante

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Duración del proyecto: 63 meses Fecha Inicio: 2019-11-13
Fecha Fin: 2025-02-28

Líder del proyecto

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Populations of living organisms are pushed to optimality by evolution, but may also be shaped by the contingency of their evolutionary history. The recent explosion of sequence data gives us access to the outcomes of molecular evolution, and controlled microbial evolution experiments allow us to analyze the predictability of evolution. In this exciting context, I aim to explore quantitatively the importance of optimization and contingency both at the molecular scale and at the scale of populations of microorganisms, using a theoretical biophysical approach. First, I will assess how functional optimization and evolutionary history, i.e. phylogeny, shape protein sequences. Importantly, correlations arising from phylogeny are a double-edged sword, often confounding signal from functional optimization, but sometimes providing useful complementary information. I will improve sequence-based predictions for protein-protein interactions by exploiting information both from phylogeny and from the required complementarity of interacting residues. I will disentangle the collective modes of correlations in protein sequences due to optimization from those due to phylogeny, and investigate the importance of functional sectors as an organizing principle of proteins. This will be a breakthrough in our understanding of the sequence-function relationship of proteins. Second, I will analyze the impact of optimization and contingency on the evolution of microbial populations. I will study microorganisms with a rugged fitness landscape presenting several optima. In these realistic cases, populations tend to remain trapped in local optima. However, most real populations possess specific geographic structures. I will quantitatively study how structure helps populations to explore model and real rugged fitness landscapes. I will build a universal model of structured populations. I will then focus on important applications to antimicrobial resistance evolution and to expanding populations.

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