Inverse statistical problems: from the inverse Ising problem to data science

Nguyen, H. Chau; Zecchina, Riccardo; Berg, Johannes

doi:10.1080/00018732.2017.1341604

Cited by 250 publications

(395 citation statements)

References 246 publications

(475 reference statements)

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“…Unlike the SIS model considered before, this distribution cannot be written in closed form since Z(A, β, J , h) cannot be computed exactly, rendering the reconstruction problem intractable. Therefore, instead, we make use of the pseudolikelihood approximation [40], which is very accurate for the purpose at hand [14], where we approximate Eq. 8 as a product of (properly normalized) conditional probabilities for each spin variable s i…”

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confidence: 99%

Network Reconstruction and Community Detection from Dynamics

Peixoto

2019

Phys. Rev. Lett.

122

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We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.P (A, b|D) = P (D|A)P (A|b)P (b) P (D) .(2)

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confidence: 99%

Network Reconstruction and Community Detection from Dynamics

Peixoto

2019

Phys. Rev. Lett.

122

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“…In addition, phylogenetic correlations are often considered 476 deleterious to structure prediction, which is one of the major aims of DCA. Nevertheless, 477 a DCA model is fundamentally a global statistical model that aims to faithfully reproduce 478 the empirical pairwise correlations observed in the data [33,34]. As such, it should also 479 encode phylogenetic correlations, thus rationalizing our result.…”

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confidence: 66%

“…MI includes all types of 32 statistical dependence between the sequences of interacting partners. 33 To what extent do phylogenetic correlations contribute to the prediction of interaction 34 partners from sequences? In the case where only phylogenetic correlations are present, 35 how do DCA-based methods compare to methods based only on sequence similarity and 36 on phylogeny?…”

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confidence: 99%

Phylogenetic correlations can suffice to infer protein partners from sequences

Marmier

Weigt

Bitbol

2019

Preprint

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Determining which proteins interact together is crucial to a systems-level understanding of the cell. Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among the paralogs of ubiquitous prokaryotic proteins families, starting from sequence data alone. Since DCA allows to infer the three-dimensional structure of protein complexes, its success in predicting protein-protein interactions could be mainly based on contacting residues coevolving to remain physicochemically complementary. However, interacting proteins often possess similar evolutionary histories, which also gives rise to correlations among their sequences. What is the role of purely phylogenetic correlations in the performance of DCA-based methods to infer interaction partners? To address this question, we employ controlled synthetic data that only involves phylogeny and no interactions or contacts. We find that DCA accurately identifies the pairs of synthetic sequences that only share evolutionary history. It performs as well as methods explicitly based on sequence similarity, and even slightly better with large and accurate training sets. We further demonstrate the ability of these various methods to correctly predict pairings among actual paralogous proteins with genome proximity but no known direct physical interaction, which illustrates the importance of phylogenetic correlations in real data. However, for actually interacting and strongly coevolving proteins, DCA and mutual information outperform sequence similarity. Author summaryMany biologically important protein-protein interactions are conserved over evolutionary time scales. This leads to two different signals that can be used to computationally predict interactions between protein families and to identify specific interaction partners. First, the shared evolutionary history leads to highly similar phylogenetic relationships between interacting proteins of the two families. Second, the need to keep the interaction surfaces of partner proteins biophysically compatible causes a correlated amino-acid usage of interface residues. Employing simulated data, we show that the shared history alone can be used to detect partner proteins. Similar accuracies are achieved by algorithms comparing phylogenetic relationships and by coevolutionary methods based on Direct Coupling Analysis, which are a priori designed to detect the second type of signal. Using real sequence data, we show that in cases with shared evolutionary but without known 1/28 physical interactions, both methods work with similar accuracy, while for physically interacting systems, methods based on correlated amino-acid usage outperform purely phylogenetic ones. 119 4/28 524 17/28

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“…Often, the observables are taken to be marginals of variables of interest, as well as pairwise of higher-order correlations between them. The resulting models then fall into classes of inverse statistical physics models, such as disordered Ising or Potts models [322].…”

Section: Maximum Entropy Modelsmentioning

confidence: 99%

Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. CONTENTS Dissociation ratesAs discussed below, immunological interactions span the range of very short lived interactions, k off > 10 s −1 or τ off = (k off ) −1 < 0.1 s for antibody binding to its target in the initial phase of an immune response, to extremely long-lived interactions, k off < 10 −4 s −1 or τ off > 3 hours for cytokines interacting with their receptors. These estimates set a huge range of time scales the immune system must deal with, even before taking into consideration delays in cellular responses and how these affect the ligand environment on time scales ranging from hours to days. These considerations form the crux of the matter for quantitative immunology at the cellular scale: while the physical chemistry of ligand-receptor interactions is straightforward, the immune system builds a response of devilish complexity from such elementary interactions. Numbers of receptors per cellIn some situations, such as in T cell antigen recognition, where both the T cell receptor and the antigen exist in a membrane-bound form. All dynamics of interactions must be estimated taking into account the surface concentrations of molecules, with proper adjustment for slower diffusion in the association rates.

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Inverse statistical problems: from the inverse Ising problem to data science

Cited by 250 publications

References 246 publications

Network Reconstruction and Community Detection from Dynamics

Network Reconstruction and Community Detection from Dynamics

Phylogenetic correlations can suffice to infer protein partners from sequences

Quantitative Immunology for Physicists

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