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Aleksandra M. Walczak
Researcher at École Normale Supérieure
Publications - 249
Citations - 8783
Aleksandra M. Walczak is an academic researcher from École Normale Supérieure. The author has contributed to research in topics: Population & Biology. The author has an hindex of 46, co-authored 222 publications receiving 6822 citations. Previous affiliations of Aleksandra M. Walczak include University of California, San Diego & Pierre-and-Marie-Curie University.
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Journal ArticleDOI
Statistical mechanics for natural flocks of birds
William Bialek,Andrea Cavagna,Irene Giardina,Thierry Mora,Edmondo Silvestri,Massimiliano Viale,Aleksandra M. Walczak +6 more
TL;DR: It is shown how a quantitative microscopic theory for directional ordering in a flock can be derived directly from field data, and the minimally structured (maximum entropy) model is constructed consistent with experimental correlations in large flocks of starlings.
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Maximum entropy models for antibody diversity
TL;DR: The results suggest that antibody diversity is not limited by the sequences encoded in the genome and may reflect rapid adaptation to antigenic challenges.
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Statistical inference of the generation probability of T-cell receptors from sequence repertoires.
TL;DR: The probabilistic model predicts the generation probability of any specific CDR3 sequence by the primitive recombination process, allowing the potential diversity of the T-cell repertoire and to understand why some sequences are shared between individuals.
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Self-regulating gene: An exact solution
J. E. M. Hornos,J. E. M. Hornos,Daniel Schultz,Guilherme C. P. Innocentini,Jin Wang,Aleksandra M. Walczak,José N. Onuchic,Peter G. Wolynes +7 more
TL;DR: An exact steady-state solution of the stochastic equations governing the behavior of a gene regulated by a self-generated proteomic atmosphere reveals deviations from the commonly used Ackers et al approximation, allowing anticooperative behavior in the "nonadiabatic" limit of slow binding and unbinding rates.
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High-throughput immune repertoire analysis with IGoR.
TL;DR: A software tool, IGoR, that calculates the likelihoods of potential V(D)J recombination and somatic hypermutation scenarios from raw immune sequence reads and outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization.