Is evolution predictable? Unlocking fundamental biological insights using new machine learning methods

UKRI FLF · United Kingdom government procurement

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June 01, 2028
Response Due
Active
Status

Opportunity Overview

Is evolution predictable? If I asked you whether a new antibiotic might face resistance, would you say yes? What about larger changes? Which animal species will be extinct in a million years, or ten? What will new ones look like? How will human culture itself have evolved?

The answers to questions like these matter deeply for the resilience of our ecosystem and society. But it is not yet known to what degree such questions are answerable. Is evolution predictable in theory and practice? From what? To what extent?

This Future Leaders Fellowship will empower me to lead an international network of researchers to give concrete answers to fundamental questions of evolutionary predictability.

A key to their answers is evolutionary convergence, the repeated evolution of similar phenotypes, or overall phenomes, like the suggested (but also disputed) repetition of body forms in placental and marsupial mammals. Convergence offers to tell us what we can expect from evolution, more of the same, or its alternative, something entirely new? But, while many examples of convergence have been hypothesised, no study has measured the full extent to which they vary with evolutionary distance and, therefore, just how predictable they are.

For the first time, new computational methods of machine learning will allow us to measure the extent of evolutionary convergence across entire visible phenomes. This fellowship will undertake the most comprehensive tests of evolutionary convergence ever performed, across two parallel research programs on diverse evolutionary groups, the butterflies and moths and the mammals, our own evolutionary clade. This will compare the extent and evolutionary patterns of convergence in all phenotype visible among hundreds of thousands of lepidopteran photographs and images from 3D mammal skull scans. This will test the extent to which evolution is ultimately, or only locally, predictable across real-world macroevolutionary diversifications.

This will give...

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Solicitation Details

Issuing agencyUKRI FLF
CountryUnited Kingdom
CategoryData & Analytics
PublishedJune 02, 2024
Procurement stageActive solicitation
Response dueJune 01, 2028
StatusOpen — accepting responses
Official sourceView original notice
Last verifiedAugust 12, 2026

Source: UK Research and Innovation (UKRI) — Open Government Licence v3.0.

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