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Jacopo Marchi

Researcher at École Normale Supérieure

Publications -  13
Citations -  116

Jacopo Marchi is an academic researcher from École Normale Supérieure. The author has contributed to research in topics: Mutation (genetic algorithm) & Evolutionary dynamics. The author has an hindex of 4, co-authored 10 publications receiving 68 citations. Previous affiliations of Jacopo Marchi include University of Paris.

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Dissecting the Control Mechanisms for DNA Replication and Cell Division in E. coli.

TL;DR: It is proposed that two concurrent cycles responsible for division and initiation of DNA replication set the time of cell division, which allows us to select a nearly constant added size per origin between subsequent initiations as the most likely mechanism setting initiation of replication.
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Antigenic waves of virus-immune coevolution.

TL;DR: In this article, the authors present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections.
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Size and structure of the sequence space of repeat proteins.

TL;DR: It is shown that the coding space of a given protein family—the total number of sequences in that family—can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences, and that correlations between amino acid usage at different positions significantly impact that diversity.
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Multi-Lineage Evolution in Viral Populations Driven by Host Immune Systems

TL;DR: An evolutionary model for viruses in the presence of immune host systems with finite memory is used to obtain a phase diagram of evolutionary patterns in a two-dimensional antigenic space and finds that, for small effective mutation rates and mutation jump ranges, a single lineage is the only stable solution.
Posted ContentDOI

Size and structure of the sequence space of repeat proteins

TL;DR: It is shown that the coding space of a given protein family —the total number of sequences in that family— can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences.