2024-12-29 –, Saal GLITCH
Language: English
Biological evolution is a great inventor. Over 4 billion years, it has generated an astonishing diversity of lifeforms, from the tiniest bacteria to the tallest trees.
Each new organism inherits a genetic program from its parents - a set of instructions to “build” the organism itself. Random mutations in this program can alter the organism’s traits, affecting its ability to survive in its environment.
But how do these small changes combine over thousands of generations to yield the vast complexity we see in present-day lifeforms?
In this talk, we discuss examples from our research, using computer simulations to model the early evolution of animals, from single-celled microbes to complex multicellular organisms.
We show that evolution behaves a bit like a hacker, repurposing the programs it previously built in unexpected ways to create new functions and structures.
Understanding how evolution continually innovates is one of biology’s grand challenges. We also hope that uncovering these processes in biological systems will provide new perspectives on current debates about the generative and creative capabilities of AI.
The history of life abounds with examples of how biological evolution repurposes old tools for new functions.
Feathers, indispensable for bird flight, first appeared in dinosaurs, where they served an entirely different purpose: to stay warm in the Jurassic winter.
Analogously, the proteins that focus light in the lens of our eyes originally functioned as metabolic enzymes.
One of evolution’s most transformative repurposing events is the emergence of multicellularity — a transition that laid the groundwork for complex life as we know it.
Before multicellularity evolved, single cells lived autonomously, each with their own genetic program to find food and survive harsh environments. Evolution repurposed these cellular programs, to organise self-sufficient cells into cooperative multicellular groups, with surprising new capabilities and collective survival strategies. For example, cells in the group can divide tasks among each other and share resources, paving the way for the extreme specialisation we find in the organs of modern animals.
Our computational models simulate this evolutionary transition to explore how the rewriting of cellular programs sets the stage for further biological innovations.
One striking insight from our computational approach is that it requires little input data to generate novel solutions to evolutionary problems, revealing an inherent efficiency in biological systems that stands in contrast to modern generative AI.
Computational biologist - working at the University of Cambridge, soon starting as a PI in INRIA Lyon.
Computational biologist working at the University of Cambridge. I am interested in the evolution of plant growth.