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Quantifying Evolution: a platform for the synthetic evolution of molecular ecosystems.

Project description

Tracing evolution, one DNA strand at a time

Evolution is one of nature’s most basic yet least understood processes. Understanding it through universal laws is difficult due to the complexity of biological systems and the numerous variables in experimental models. The ERC-funded Q-EVO project addresses this issue with the Affinity-based DNA Synthetic Evolution (ADSE) platform, which examines evolution at the molecular level. By using DNA sequences as stand-ins for organisms, ADSE recreates ecological interactions such as competition, mutualism, and parasitism through predictable DNA-DNA hybridisation. Q-EVO pairs this with array-based biosensors, data science, and theoretical ecology to monitor hundreds of parallel evolutionary experiments across multiple generations. By mapping population dynamics, phylogenetic paths, and ecological networks, it aims to provide the first complete quantitative description of fitness, speciation, and environment-driven evolution, with all data made publicly available.

Objective

Among the fundamental processes governing Nature, evolution is one of the most thoroughlAmong the fundamental processes governing Nature, evolution is one of the most thoroughly described and less formalized. The grand goal of describing evolution via clean universal laws is made difficult by the inherent complexity of both the biological systems and the available experimental models, which generally involve a large number of potentially relevant variables of difficult quantification.
This project is built around the Affinity-based DNA Synthetic Evolution (ADSE) platform, an experimental strategy to generate and study evolution at a molecular level that enables full quantitative description of the eco-evolutionary processes. ADSE is inspired by the in-vitro molecular selection protocol “SELEX” and enables observing the evolution across generations of large populations of DNA oligomers whose identity is given by their sequence. The key innovation is that here evolutionary mechanisms and ecological interactions (selection of the fittest, resource gathering capacity, parasitism, mutualism etc…) are given by molecular interactions based on DNA-DNA hybridization, a process that is well known and predictable from sequences.
Q-EVO is based on a combination of ADSE with an innovative array-based biosensor, approaches of data science and notions of theoretical ecology. Q-EVO aims at providing a (still missing) quantitative description of the key concepts of fitness and speciation, and of the relationship between a given environment and the evolution of its ecosystem. I propose a program capable to produce hundreds of parallel evolutions for tens of generations exploring a vast variety of conditions. Raw data, formed by hundreds of millions of reads, will be analysed to follow population dynamics, extract phylogenetic paths, parametrizing the network of ecological interactions (e.g. competitors, mutualists, parasites) and will be made public in a repository created ad hoc.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2024-ADG

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Host institution

UNIVERSITA DEGLI STUDI DI MILANO
Net EU contribution
€ 2 435 177,00
Address
Via Festa Del Perdono 7
20122 Milano
Italy
Region
Nord-Ovest Lombardia Milano
Activity type
Higher or Secondary Education Establishments
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Total cost
No data

Beneficiaries (1)