List of Publications - Simona Cocco


[87] Optimal regularizations for data generation with probabilistic graphical models

A. Fantôme, F. Rizzato, S. Cocco, R. Monasson

arXiv:2112.01292



[86] Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1

A. Birgy, C. Roussel, H. Kemble, J. Mullaert, K. Panigoni, A. Chapron, J.  Chatel, M. Magnan, H. Jacquier, S. Cocco, R Monasson, O. Tenaillon

bioRXiv 2021.11.29.470435


[85] sgDI-tector: defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs.

A. Di Gioacchino, R. Legendre, Y. Rahou, V. Najburg, P. Charneau, B. D Greenbaum, F. Tangy, S. van der Werf, S. Cocco, A. V Komarova.

 bioRXiv 2021.11.30.470527 


[84] Repeats Mimic Immunostimulatory Viral Features Across a Vast Evolutionary Landscape.

P. Sulc, A. Solovyov,S. Marhou. S. Sun, J  A LaCava, O I Abdel-Wahab, N. Vabret, D. De Carvalho, R. Monasson, S. Cocco, B. D. Greenbaum.

bioRXiv 2021.11.04.467016v1


[83] Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines 

C. Roussel, S. Cocco, R. Monasson 

Phys. Rev. E 104,034109 ( 2021)


[82] Inferring epistasis from genomic data with comparable mutation and outcrossing rate
H-L. Zeng, E. Mauri, V. Dichio, S. Cocco, R. Monasson, E. Aurell
J. Stat. Mech. 083501 (2021) 

[81] A synaptic novelty signal in the dentate gyrus supports switching hippocampal attractor networks from generalization to discrimination
R. Gomez-Ocadiz, M. Trippa, L. Posani, S. Cocco, R. Monasson, C. Schmidt-Hieber
Submitted to publication (2021) 

[80] Probing T-cell response by sequence-based probabilistic modeling
B. Bravi, V.P. Balachandran, B.D. Greenbaum, A.M. Walczak, T. Mora, R. Monasson, S. Cocco
 PLoS Comp Bio 17 (9): e1009297  ( 2021) 

[79] Survival probability and size of lineages in antibody affinity maturation
M. Molari, R. Monasson, S. Cocco
Physical Review E 103, 052413 (2021) 

[78] Improving sequence-based modeling of protein families using secondary structure quality assessment
C. Malbranke, D. Bikard, S. Cocco, R. Monasson
Bioinformatics, btab442 (2021) 

[77] Gaussian Closure Scheme in the Quasi-Linkage Equilibrium Regime of Evolving Genome Populations
E. Mauri, S. Cocco, R. Monasson
Europhysics Letters 132, 56001 (2020) 

[76] The heterogeneous landscape and early evolution of pathogen-associated CpG dinucleotides in SARS-CoV-2
A. Di Gioacchino, P. Sulc, A.V. Komarova, B.D. Greenbaum, R. Monasson, S. Cocco
Molecular Biology and Evolution 38, 2428-2445 (2021) 

[75] RBM-MHC: a semi-supervised machine-learning method for sample-specific prediction of antigen presentation by HLA-I alleles
B. Bravi, J. Tubiana, S. Cocco, R. Monasson, T. Mora, A.M. Walczak
Cell Systems 12, 1-8 (2021) 

[74] An evolution-based model for designing chorismate mutase enzymes
W.P. Russ, M. Figliuzzi, C. Stocker, P. Barrat-Charlaix, M. Socolich, P. Kast, D. Hilvert, R. Monasson, S. Cocco, M. Weigt, R. Ranganathan
Science 369, 440-5 (2020) 

[73] Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers
M. Molari, K. Eyer, J. Baudry, S. Cocco, R. Monasson
eLife 2020 9:e55678 (2020) 

[72] 'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
M. Harsh, J. Tubiana, S. Cocco, R. Monasson
J. Phys. A 53, 174002 (2020)

[71] Parameters and determinants of responses to selection in antibody libraries
S. Schulz, S. Boyer, M. Smerlak, S. Cocco, R. Monasson, C. Nizak, O. Rivoire
PLoS Comp. Biol. 17:e1008751 (2021) 

[70] Inference of compressed Potts graphical models
F. Rizzato, A. Coucke, E. de Leonardis, J.P. Barton, J. Tubiana, R. Monasson, S. Cocco
Physical Review E 101, 012309 (2020) 

[69] Adaptive cluster expansion for Ising spin models
S. Cocco; G. Croce, F. Zamponi
The European Physical Journal B DOI 10.1140/EPJB/E2019-100313-9,2019 

[68] Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
J. Tubiana, S. Cocco, R. Monasson
Neural Computation 31(8), 1671-1717 (2019) 

[67] Learning protein constitutive motifs from sequence data
J. Tubiana, S. Cocco, R. Monasson
eLife 2019;8:e39397 (2019). See also the press release. 

[66] Adaptation of olfactory receptor abundances for efficient coding
T. Tesileanu, S. Cocco, R. Monasson, V. Balasubramanian
eLife 2019;8:e39279 (2019). See also the press release. 

[65] Integration and multiplexing of positional and contextual information by the hippocampal network
L. Posani, S. Cocco, R. Monasson
PLoS Computational Biology 14: e1006320 (2018). 

[64] Statistical Physics and Representations in Real and Artificial Neural Networks
S. Cocco, R. Monasson, L. Posani, S. Rosay, J. Tubiana
Lectures Notes of Fundamental Problems in Statistical Physics XIV, Physica A 504, 45-76 (2018). 

[63] Inverse Statistical Physics of Protein Sequences: A Key Issues Review
S. Cocco, C. Feinauer, M. Figliuzzi, R. Monasson, M. Weigt
Reports on Progress in Physics 81, 032601 (2018). 

[62] Evolutionary constraints on coding sequences at the nucleotidic level: a statistical physics approach
D. Chatenay, S. Cocco, B. Greenbaum, R. Monasson, P. Netter
chapter of "Evolutionary Biology: Self/Nonself Evolution, Species and Complex Traits Evolution, Methods and Concepts", Editor P. Pontarotti (2017). 

[61] Functional Networks from Inverse Modeling of Neural Population Activity
S. Cocco, R. Monasson, L. Posani, G. Tavoni
Current Opinion in Systems Biology 3, 103-110 (2017) 

[60] Functional connectivity models for brain state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings
L. Posani, S. Cocco, K. Jezek, R. Monasson
J. Comp. Neurosci. 43, 17-33 (2017). 

[59] Direct coevolutionary couplings reflect biophysical residue interactions in proteins
A. Coucke, G. Uguzzoni, F. Oteri, S. Cocco, R. Monasson, M. Weigt
J. Chem. Phys. 145, 174102 (2016). 

[58] Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings
G. Tavoni, S. Cocco, R. Monasson
J. Comp. Neurosci. 41, 269-293 (2016). 

57] ACE: adaptive cluster expansion for maximum entropy graphical model inference 

JP Barton, E. De Leonardis, A. Coucke, S. Cocco
Bioinformatics doi: 10.1093/bioinformatics/btw328 (2016) 


[56] Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models
H. Jacquin, A. Gilson, E. Shakhnovich, S. Cocco, R. Monasson
PLoS Comput Biol 12: e1004889 (2016) 

[55] Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity
G. Tavoni, U. Ferrari, F.P. Battaglia, S. Cocco, R. Monasson
Network Neuroscience 1, 275-301 (2017) (supporting information)

[54] On the entropy of protein families
J.P. Barton, A.K. Chakraborty, S. Cocco, H. Jacquin, R. Monasson
Journal of Statistical Physics 162, 1267-1293 (2016) 

[53] Distinguishing the Immunostimulatory Properties of Non-coding RNAs Expressed in Cancer Cells
A. Tanne, L. Muniz, A. Puzio-Kuter, K. Leonova, A. Gudkov, D. Ting, R. Monasson, S. Cocco, A. Levine, N. Bhardwaj, B. Greenbaum
Proc. Natl. Acad. Sci. USA 112, 15154-15159 (2015), doi: 10.1073/pnas.1517584112 (supplementary methods and experiments) 

see also Immunostimulatory noncoding RNAs, in Highlights (Medical Sciences) 

and the commentary Silent pericentromeric repeats speak out by S.T. Younger and J.L. Rinn. 


[52] Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction
E. De Leonardis, S. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt
Nucleic Acid Research, doi: 10.1093/nar/gkv932 (2015) (supplemental text and supplemental figures) 

[51] Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
T. Obuchi, R. Monasson
Journal of Physics Conf. Ser. 638, 012018 (2015) 

[50] Large Pseudo-Counts and L2-Norm Penalties Are Necessary for the Mean-Field Inference of Ising and Potts Models
J.P.Barton, S. Cocco, E. De Leonardis, R. Monasson
Physical Review E 90, 012132 (2014) 

[49] Stochastic Ratchet Mechanisms for Replacement of Proteins Bound to DNA
S. Cocco, J.F. Marko, R. Monasson
Physical Review Letters 112, 238101 (2014) (supplemental material)

[48] A Quantitative Theory of Entropic Forces Acting on Constrained Nucleotide Sequences Applied to Viruses
B. Greenbaum, S. Cocco, A. Levine, R. Monasson
Proc. Natl. Acad. Sci. USA 111, 5054-5059 (2014) 

[47] Reconstruction and identification of DNA sequence landscapes from unzipping experiments at equilibrium
C. Barbieri, S. Cocco, T. Jorg, R. Monasson
Biophysical Journal 106, 430-9 (2014)
      (supporting material)

[46] Trend or Fluctuations? Analysis and design of population dynamics measurements in replicate ecosystems.
D.R. Hekstra, S. Cocco, R. Monasson, S. Leibler
Physical Review E 88, 062714 (2013) (supplementary information)

[45] Hopfield-Potts patterns for covariation in protein families: calculation and statistical error bars
S. Cocco, R. Monasson, M. Weigt
J. Phys. Conference Series 473, 012010 (2013) 

[44] From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction
S. Cocco, R. Monasson, M. Weigt
PLoS Comput Biol 9, E1003176 (2013) (supplementary information)

[43] Ising models for neural activity inferred via Selective Cluster Expansion: structural and coding properties. 

J.P Barton, S. Cocco

Journal of Statistical Mechanics: Theory and Experiment.P03002 (2013) 


[42] Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests
S. Cocco, R. Monasson
J. Stat. Phys. 147, 252 (2012) 


41] High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections.
S. Cocco, R. Monasson, V. Sessak
Physical Review E 83, 051123 (2011) 

[40] On the trajectories and performance of Infotaxis, an information-based greedy search algorithm.
C. Barbieri, S. Cocco, R. Monasson
Europhysics Letters 94, 20005 (2011) 

[39] Adaptive cluster expansion for inferring Boltzmann machines with noisy data.
S. Cocco, R. Monasson
Physical Review Letters 106, 090601 (2011) (supplementary information)

[38] Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings
R. Monasson, S. Cocco
Journal of Computational Neuroscience 31, 199-227 (2011) 

[37] Inference of a random potential from random walk realizations: formalism and application to the one-dimensional Sinai model with a drift
S. Cocco, R. Monasson
Journal of Physics: Conference Series 197, 012005 (2009) 

[36] Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods.
S. Cocco, S. Leibler, R. Monasson
Proc. Natl. Acad. Sci. USA 106, 14058 (2009) (supplementary information)

[35] Dynamical modelling of molecular constructions and setups for DNA unzipping.
C. Barbieri, S. Cocco, R. Monasson, F. Zamponi
Phys. Biol. 6, 025003 (2009) 

[34] Reconstructing a random potential from its random walks.
S. Cocco, R. Monasson.
Europhysics Letters 81, 20002 (2008) 


[33] Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case.
V. Baldazzi, S. Bradde, S. Cocco, E. Marinari, R. Monasson
Phys. Rev. E 75, 011904 (2007). 

[32] The mechanical opening of DNA and the sequence content
S. Cocco, R. Monasson
AIP Conference Proceedings, vol 851, p 50 (2006) 

[31] Inference of DNA sequences from mechanical unzipping experiments: an ideal-case study
V. Baldazzi, S. Cocco, E. Marinari, R. Monasson
Phys. Rev. Lett. 96, 128102 (2006). 

[30] Comment on the paper ”Comparison of the measured phase diagrams in the force- temperature plane for the unzipping of two different natural DNA sequences” by C.H. Lee, C. Danilowicz, V.W. Coljee, and M. Prentiss.
S.Cocco
Eur Phys J E 19, 345 (2006). 

[Edited book] Multiple aspects of DNA and RNA: from biophysics to bioinformatics.
D. Chatenay, S. Cocco, R. Monasson, D. Thieffry, J. Dalibard (eds)
Lecture Notes of Les Houches Summer School, Elsevier (2005) 

[29] Loops in DNA: an overview of experimental and theoretical approaches. 

J.F. Allemand, S. Cocco , N. Douarche N, G. Lia 

Eur Phys J E 19, 293 (2006). 

[28] Protein-mediated DNA loops: effects of protein bridge size and kinks. 

 N. Douarche, S. Cocco 

Phys Rev E 72, 061902 (2005). 

[27] Dynamics of excitatory synaptic components in sustained firing at low rates. 

C. Wyart, S. Cocco, L. Bourdieu, J. F. L ́eger, C. Herr , D. Chatenay 

J Neurophysiology 93, 3370 (2005). 

[27a] Role of calcium and noise in the persistent activity of an isolated neuron 

S.Cocco

arXiv:q-bio/0406010 [q-bio.NC]


[26] Overstretching and force-driven strand separation of double-helix DNA 

S. Cocco, Y. Yan, J-F. L ́eger, D. Chatenay, J.F. Marko, 

Phys Rev E 70, 011910 (2004). 


[25] Analysis of backtracking procedures for random decision problems
S. Cocco, L. Ein-Dor, R. Monasson.
Chapter for "New optimization algorithms in physics" edited by A. Hartmann, H. Rieger, Wiley (2004) 

[24] Approximate analysis of search algorithms with ``physical'' methods.
S. Cocco, R. Monasson, A. Montanari, G. Semerjian.
Chapter for "Phase transitions and Algorithmic complexity" edited by G. Istrate, C. Moore, A. Percus (2004) 


[23] The micromechanics of DNA 

S. Cocco, J.F. Marko 

Physics World 16, 37 (2003). 


[22] Rigorous decimation-based construction of ground pure states for spin glass models on random lattices.
S. Cocco, O. Dubois, J. Mandler, R. Monasson.
Phys. Rev. Lett. 90, 047205 (2003) 

[21] Force-extension behavior of folding polymers.
S. Cocco, J.F. Marko, R. Monasson, A. Sarkar, J. Ya.
Eur. Phys. J. E 10, 249 (2003). 

20] Unzipping dynamics of long DNAs.
S. Cocco, R. Monasson, J.F. Marko.
Phys. Rev. E 66, 051914 (2002). 

19] Theoretical models for single-molecule DNA and RNA experiments: from elasticity to unzipping.
S. Cocco, J.F. Marko, R. Monasson.
C.R. Physique 3, 569-584 (2002) 

[18] Slow nucleic acid unzipping kinetics from sequence-defined barriers.
S. Cocco, R. Monasson, J.F. Marko.
Eur. Phys. J. E 10, 153 (2003). 

[17] Exponentially hard problems are sometimes polynomial, a large deviation analysis of search algorithms for the random Satisfiability problem, and its application to stop-and-restart resolutions.
S. Cocco, R. Monasson.
Phys. Rev. E 66, 037101 (2002) 

[16] Heuristic average-case analysis of the backtrack resolution of random 3-Satisfiability instances.
S. Cocco, R. Monasson.
Theoretical Computer Science A 320, 345 (2004). 

[15] Restarts and exponential acceleration of random 3-SAT instances resolutions: a large deviation analysis of the Davis-Putnam-Loveland-Logemann algorithm.
S. Cocco, R. Monasson.
Annals of Mathematics and Artificial Intelligence 43, 153-172 (2005) 

[14] Phase transitions and Complexity in computer science: An overview of the statistical physics approach to the random satisfiability problem.
G. Biroli, S. Cocco, R. Monasson.
Physica A 306, 381-394 (2002). 

13] A la rescousse de la complexité calculatoire.
S. Cocco, O. Dubois, J. Mandler, R. Monasson.
Pour la Science, Mai 2002, Editions Belin. 

[12] Force and kinetic barriers to initiation of DNA unzipping.
S. Cocco, R. Monasson, J. Marko.
Phys. Rev. E 65, 041907 (2002). 

[11] Force and kinetic barriers in unzipping of DNA.
S. Cocco, R. Monasson, J. Marko.
Proc. Natl. Acad. Sci. USA 98, 8608 (2001). 

[10] Statistical physics analysis of the computational complexity of solving random satisfiability problems using branch and bound search algorithms.
S. Cocco, R. Monasson.
Eur. Phys. J. B 22, 505 (2001). 

[9] Le temps d'un choix : transitions de phase et complexité en informatique.
G. Biroli, S. Cocco, R. Monasson.
Images de la Physique 2001, CNRS Editions. 

[8] Trajectories in phase diagrams, growth processes and computational complexity: how search algorithms solve the 3-Satisfiability problem.
S. Cocco, R. Monasson.
Phys. Rev. Lett. 86, 1654 (2001). 

[7] Theoretical study of collective modes in DNA at ambient temperature.
S. Cocco, R. Monasson.
J. Chem. Phys. 112, 100 (2000) 

[6] Statistical Mechanics of Torque Induced Denaturation of DNA.
S. Cocco, R. Monasson.
Phys. Rev. Lett. 83, 5178 (1999) 

[5] A twist opening model for DNA. 

M. Barbi, S. Cocco, M. Peyrard, S. Ruffo. 

Journal of Biological Physics 24, 97 (1999). 


[4] Helicoidal model for DNA opening.

 M. Barbi, S. Cocco, M. Peyrard.

 Physics Letters A 253, 358 (1999). 


[3] S. Cocco, M. Barbi, M. Peyrard. 

Vector Nonlinear Klein-Gordon Lattices: General Derivation of Small Amplitude Envelope Soliton Solutions.
Physics Letters A 253, 161-167 (1999). 


[2] S. Cocco, R. Monasson, R. Zecchina. 

 The weight space structure of the parity machine with binary weights: analytical and numerical results.
Proceedings of the Varenna School of physics Enrico Fermi course CXXXIV, edited by F. Mallamace and H. E. Stanley (IOS Press, Amsterdam) 738-739 (1997). 


[1] Analytical and numerical study of internal representations in multilayer neural networks with binary weights.
S. Cocco, R. Monasson, R. Zecchina.
Phys. Rev. E 54, 717 (1996).