Search Results for author: Francesco Zamponi

Found 11 papers, 3 papers with code

Emergent time scales of epistasis in protein evolution

no code implementations14 Mar 2024 Leonardo Di Bari, Matteo Bisardi, Sabrina Cotogno, Martin Weigt, Francesco Zamponi

Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations.

Towards Parsimonious Generative Modeling of RNA Families

1 code implementation19 Oct 2023 Francesco Calvanese, Camille N. Lambert, Philippe Nghe, Francesco Zamponi, Martin Weigt

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences.

adabmDCA: Adaptive Boltzmann machine learning for biological sequences

1 code implementation9 Sep 2021 Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi

Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families.

BIG-bench Machine Learning

Modeling sequence-space exploration and emergence of epistatic signals in protein evolution

no code implementations4 Jun 2021 Matteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, Martin Weigt

During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection.

Efficient generative modeling of protein sequences using simple autoregressive models

no code implementations4 Mar 2021 Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin Weigt

Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases.

Protein Design

Low-frequency vibrational spectrum of mean-field disordered systems

no code implementations21 Dec 2020 Eran Bouchbinder, Edan Lerner, Corrado Rainone, Pierfrancesco Urbani, Francesco Zamponi

We study a recently introduced and exactly solvable mean-field model for the density of vibrational states $\mathcal{D}(\omega)$ of a structurally disordered system.

Disordered Systems and Neural Networks Soft Condensed Matter Statistical Mechanics

Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families

no code implementations23 Nov 2020 Pierre Barrat-Charlaix, Anna Paola Muntoni, Kai Shimagaki, Martin Weigt, Francesco Zamponi

For example, pairwise Potts models (PM), which are instances of the BM class, provide accurate statistical models of families of evolutionarily related protein sequences.

V-, U-, L-, or W-shaped economic recovery after COVID: Insights from an Agent Based Model

1 code implementation15 Jun 2020 Dhruv Sharma, Jean-Philippe Bouchaud, Stanislao Gualdi, Marco Tarzia, Francesco Zamponi

We discuss the impact of a Covid-19--like shock on a simple model economy, described by the previously developed Mark-0 Agent-Based Model.

General Economics Physics and Society Economics

Good speciation and endogenous business cycles in a constraint satisfaction macroeconomic model

no code implementations24 May 2020 Dhruv Sharma, Jean-Philippe Bouchaud, Marco Tarzia, Francesco Zamponi

More generally, our model shows that constraints at the individual scale can generate highly complex patterns at the aggregate level.

Aligning biological sequences by exploiting residue conservation and coevolution

no code implementations18 May 2020 Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi

Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information.

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