title: Improving landscape inference by integrating heterogeneous data in the inverse Ising problem.
abstract: The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are inferred using a sample of equilibrium configurations drawn from the Boltzmann distribution. However, in the context of biological applications, quantitative information for a limited number of microscopic spins configurations has recently become available. In this paper, we extend the usual setting of the inverse Ising model by developing an integrative approach combining the equilibrium sample with (possibly noisy) measurements of the energy performed for a number of arbitrary configurations. Using simulated data, we show that our integrative approach outperforms standard inference based only on the equilibrium sample or the energy measurements, including error correction of noisy energy measurements. As a biological proof-of-concept application, we show that mutational fitness landscapes in proteins can be better described when combining evolutionary sequence data with complementary structural information about mutant sequences.
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, CNRS, Biologie computationnelle et quantitative - Institut de Biologie Paris Seine, 75005 Paris, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Barrat-Charlaix
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, CNRS, Biologie computationnelle et quantitative - Institut de Biologie Paris Seine, 75005 Paris, France.|Sorbonne Universités, UPMC Univ Paris 06, Institut de Calcul et de la Simulation, 75005 Paris, France.
authors.forename: Matteo
authors.initials: M
authors.lastname: Figliuzzi
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, CNRS, Biologie computationnelle et quantitative - Institut de Biologie Paris Seine, 75005 Paris, France.
authors.forename: Martin
authors.initials: M
authors.lastname: Weigt
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical