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Statistical Genomics and Biological Physics

Research

Advances in sequencing and other high-throughput experimental technologies have fueled the genomic revolution over the last 10-15 years, providing an unprecedented amount of large-scale biological data. To extract information from such data, and to reach a deeper understanding of the corresponding biological system, requires the solution of hard inference problems, which are intractable by conventional computational tools.

Our group, newly established in Sept. 2011, draws inspiration from the statistical physics of disordered systems to develop novel algorithmic tools for solving large-scale optimization and inference tasks, to bring such computational methods to the full benefit of biological research. 

Specific questions of our interest are:

- Statistical-physics inspired approaches to large-scale network inference 
- Protein co-evolution and structural inference of proteins, their complexes, and their interaction specificity
- Inference of signal transduction networks from multiple-drug perturbation experiments
- Robust clustering of noisy data


NEW: Direct Coupling Analysis website offersa webservice, information and downloadable software.

Members
Selected Publications
Gueudré T, Baldassi C, Zamparo M, Weigt M, Pagnani A.
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis.
Proc. Natl. Acad. Sci. 113(43), pp.12186-12191 (2016).
Figliuzzi M, Jacquier H, Schug A, Tenaillon O, Weigt M.
Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1.
Mol Biol Evol. 33(1), pp.268-280 (2015).
De Leonardis E, Lutz B, Ratz S, Cocco S, Monasson R, Schug A, Weigt M.
Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction.
Nucleic Acids Res. 43(21), pp.10444-10455 (2015).
Cocco S, Monasson R, Weigt M.
From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
PLoS Comput Biol. 9(8), pp.e1003176 (2013).
Ekeberg M, Lövkvist C, Lan Y, Weigt M, Aurell E.
Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models.
Phys Rev E Stat Nonlin Soft Matter Phys. 87(1), pp.012707 (2013).
Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, Jing X, Kaushik P, He Q, Mills G, Solit DB, Pratilas CA, Weigt M, Braunstein A, Pagnani A, Zecchina R, Sander C.
Perturbation biology: inferring signaling networks in cellular systems.
PLoS Comput Biol. 9(12), pp.e1003290 (2013).
Sułkowska JI, Morcos F, Weigt M, Hwa T, Onuchic JN.
Genomics-aided structure prediction.
Proc Natl Acad Sci U S A. 109(26), pp.10340-5 (2012).
Dago AE, Schug A, Procaccini A, Hoch JA, Weigt M, Szurmant H.
Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis.
Proc Natl Acad Sci U S A. 109(26), pp.E1733-42 (2012).
Morcos F, Pagnani A, Lunt B, Bertolino A, Marks DS, Sander C, Zecchina R, Onuchic JN, Hwa T, Weigt M.
Direct-coupling analysis of residue coevolution captures native contacts across many protein families.
Proceedings of the National Academy of Sciences. 108, pp.E1293-E1301 (2011).
Schug A, Weigt M, Onuchic JN, Hwa T, Szurmant H.
High-resolution protein complexes from integrating genomic information with molecular simulation.
Proc Natl Acad Sci U S A. 106(52), pp.22124-9 (2009).
Weigt M, White RA, Szurmant H, Hoch JA, Hwa T.
Identification of direct residue contacts in protein-protein interaction by message passing.
Proc Natl Acad Sci U S A. 106(1), pp.67-72 (2009).
Software
Jobs & Internships

A postdoctoral position has been opened in the team “Statistical Genomics and Computational Physics” for the duration of one year. We are looking for outstanding candidates with a PhD in physics, bioinformatics, statistics or related fields, and some experience in analysis and statistical modeling of large biological datasets. Applications containing a letter of interest and a CV are to be sent before March 13, 2016 to martin.weigt@upmc.fr. A description (fiche de poste) can be downloaded here.

Open Positions