Innovating Works

FIT2GO

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A toolbox for fitness landscapes in evolution
A major challenge in evolutionary biology is to quantify the processes and mechanisms by which populations adapt to new environments. In particular, the role of epistasis, which is the genetic-background dependent effect of mutati... A major challenge in evolutionary biology is to quantify the processes and mechanisms by which populations adapt to new environments. In particular, the role of epistasis, which is the genetic-background dependent effect of mutations, and the constraints it imposes on adaptation, has been contentious for decades. This question can be approached using the concept of a fitness landscape: a map of genotypes or phenotypes to fitness, which dictates the dynamics and the possible paths towards increased reproductive success. This analogy has inspired a large body of theoretical work, in which various models of fitness landscapes have been proposed and analysed. Only recently, novel experimental approaches and advances in sequencing technologies have provided us with large empirical fitness landscapes at impressive resolution, which call for the evaluation of the related theory. The aim of this proposal is to build on the theory of fitness landscapes to quantify epistasis across levels of biological organization and across environments, and to study its impact on the population genetics of adaptation and hybridization. Each work package involves classical theoretical modelling, statistical inference and method development, and data analysis and interpretation; a combination of approaches for which my research group has strong expertise. In addition, we will perform experimental evolution in Escherichia coli and influenza to test hypotheses related to the change of fitness effects across environments, and to adaptation by means of highly epistatic mutations. We will specifically apply our methods to evaluate the potential for predicting routes to drug resistance in pathogens. The long-term goal lies in the development of a modeling and inference framework that utilizes fitness landscape theory to infer the ecological history of a genome, which may ultimately allow for a prediction of its future adaptive potential. ver más
31/08/2024
UNIVERSITAET BERN
1M€
Duración del proyecto: 69 meses Fecha Inicio: 2018-11-19
Fecha Fin: 2024-08-31

Línea de financiación: concedida

El organismo H2020 notifico la concesión del proyecto el día 2024-08-31
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
ERC-2018-STG: ERC Starting Grant
Cerrada hace 7 años
Presupuesto El presupuesto total del proyecto asciende a 1M€
Líder del proyecto
UNIVERSITAET BERN No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5