8 papers
Yielding in dense active matter
Adil Ghaznavi, Saverio Rossi, Francesco Zamponi +1
High-density granular active matter is a useful model for dense animal collectives and could be useful for designing reconfigurable materials that can flow or solidify on command.…
Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics
Matteo Bisardi, Leonardo Di Bari, Saverio Rossi +3
Protein sequences carry a record of evolutionary history shaped by mutation, selection, drift, and epistasis. Recent generative models trained on homologous sequence families offer…
Towards coevolution-aware ancestral sequence reconstruction
Alya Zeinaty, Leonardo di Bari, Saverio Rossi +3
Ancestral sequence reconstruction (ASR) is a powerful approach for studying molecular evolution and the emergence of protein function. Yet most ASR methods assume that sites evolve…
Dreaming improves memorization in a Hopfield model with bounded synaptic strength
Enzo Marinari, Saverio Rossi, Francesco Zamponi
The Hopfield model provides a paradigmatic framework for associative memory. Its classical implementation, based on the Hebbian learning rule, suffers from catastrophic forgetting:…
Functional bottlenecks can emerge from non-epistatic underlying traits
Anna Ottavia Schulte, Samar Alqatari, Saverio Rossi +1
Protein fitness landscapes frequently exhibit epistasis, where the effect of a mutation depends on the genetic context in which it occurs, i.e., the rest of the protein sequence. E…
Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics
Leonardo Di Bari, Thierry Mora, Andrea Pagnani +3
Generative models derived from large protein sequence alignments define complex fitness landscapes, but their utility for accurately modeling non-equilibrium evolutionary dynamics…