Skip to main content

Biological Physics

We are witnessing an unprecedented boom in biological data availability, inevitably leading to a turning point, where theory can be a guiding force behind experimental design and development, similarly to what happened in physics. Molecules in our cells, genes in our genome or individuals in our societies do not serve their functions in isolation, but in concert with other nodes in their networks, as well as with environmental factors. Pairwise interactions and correlations are an important starting point, captured by network models. Yet, a sufficient understanding of cancer and complex diseases, as well as drug combinations or genetic interactions requires to consider interactions of higher order, between multiple nodes and conditions. Hindered by a combinatorial explosion, limited data availability and quality, going beyond second order in a data-driven way is extremely demanding, with only a handful of examples. The same problem arises not only in systems biology, but in neuroscience, as well as in information and infection spreading. In our work we develop novel methods to fight data incompleteness and biases, leading to experimentally testable, large-scale predictions. Besides bio-physical interactions, our approach can reliably predict a broad spectrum of functional associations, including co-complex membership information, disease associations, pathway membership and (higher order) genetic interactions, toxic and synergistic drug combinations as well as organizing rules in the nervous system. 

Related Publications

  • H. S. Ansell and I. A. Kovács (2024) Unveiling universal aspects of the cellular anatomy of the brain, Communications Physics, 7, 184 [arXiv]
  • S. Schäfer, M. Smelik, O. Sysoev, Y. Zhao, D. Eklund, S. Lilja, M. Gustafsson, H. Heyn, A. Julia, I. A. Kovács, J. Loscalzo, S. Marsal, H. Zhang, X. Li, D. Gawel, H. Wang, and M. Benson (2024) scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases, Genome Medicine 16, 42 [bioRxiv]
  • D. L. Barabási, G. Bianconi, E. Bullmore, M. Burgess, S. Y. Chung, T. Eliassi-Rad, D. George, I. A. Kovács, H. Makse, T. E. Nichols, C. Papadimitriou,  O. Sporns, K. Stachenfeld, Z. Toroczkai, E. K. Towlson, A. M. Zador, H. Zeng, A.-L. Barabási, A. Bernard and Gy. Buzsáki (2023) Neuroscience needs network science, The Journal of Neuroscience, 43 (34) 5989-5995 [arXiv]
  • B. Hao and I. A. Kovács (2023) A positive statistical benchmark to assess network agreement, Nat. Commun., 14, 2988 [bioRxiv]
  • H-W. Tang*, K. Spirohn*, Y. Hu, T. Hao, I. A. Kovács, Y. Gao, R. Binari, D. Yang-Zhou, K. H. Wan, J. S. Bader, D. Balcha, W. Bian, B. W. Booth, A. G. Cote, S. de Rouck, A. Desbuleux, K. Y. Goh, D.-K. Kim, J. J. Knapp, W. X. Lee, I. Lemmens, C. Li, M. Li, R. Li, H. J. Lim, Y. Liu, K. Luck, D. Markley, C. Pollis, S. Rangarajan, J. Rodiger, S. Schlabach, Y. Shen, D. Sheykhkarimli, B. TeeKing, F. P. Roth, J. Tavernier, M. A. Calderwood, D. E. Hill, S. E. Celniker, M. Vidal, N. Perrimon, S. E. Mohr (2023) Next-generation large-scale binary protein interaction network for Drosophila melanogaster, Nat. Commun., 14, 2162 [bioRxiv]
  • X.-W. Wang, L. Madeddu, K. Spirohn, L. Martini, A. Fazzone, L. Becchetti, T. P. Wytock, I. A. Kovács, O. M. Balogh, B. Benczik, M. Pétervári, B. Ágg, P. Ferdinandy, L. Vulliard, J. Menche, S. Colonnese, M. Petti, G. Scarano, F. Cuomo, T. Hao, F. Laval, L. Willems, J.-C. Twizere, M. A. Calderwood, E. Petrillo, A.-L. Barabási, E. K. Silverman, J. Loscalzo, P. Velardi and Y.-Y. Liu (2023) Assessment of community efforts to advance network-based prediction of protein-protein interactions, Nat. Commun., 14, 1582 [bioRxiv]
  • M. R. Harris, T. P. Wytock and I. A. Kovács (2022) Computational inference of synaptic polarities in neuronal networks, Advanced Science 2104906, Rising Star Series
  • A. Meignié, C. Combredet, M. Santolini, I. A. Kovács, T. Douche, Q. Gianetto, H. Eun, M. Matondo, Y. Jacob, R. Grailhe, F. Tangy and A. V. Komarova (2021) Proteomic Analysis uncovers Measles Virus Protein C interaction with p65/iASPP/p53 protein complex, Molecular & Cellular Proteomics, doi: https://doi.org/10.1016/j.mcpro.2021.100049
  • I. A. Kovács, D.-L. Barabási and A.-L. Barabási (2020) Uncovering the genetic blueprint of the C. elegans nervous system, PNAS 10.1073/pnas.2009093117 [bioRxiv]
  • I. A. Kovács and R. Juhász (2020) Emergence of disconnected clusters in heterogeneous complex systems, Scientific Reports 10, 21874 [arXiv]
  • P. Maróti, I. A. Kovács, M. Kis, J. L. Smart and F. Iglói (2020) Correlated clusters of closed reaction centers during induction of intact cells of photosynthetic bacteria, Scientific Reports 10, 14012 [arXiv]
  • R. Juhász and I. A. Kovács (2020) Scaling of local persistence in the disordered contact process, Phys. Rev. E 102, 012108 [arXiv]
  • K. Luck*, D.-K. Kim*, L. Lambourne*, K. Spirohn*, B. E. Begg, W. Bian, R. Brignall, T. Cafarelli, F. J. Campos-Laborie, B. Charloteaux, D. Choi, A. G. Cote, M. Daley, S. Deimling, A. Desbuleux, A. Dricot, M. Gebbia, M. F. Hardy, N. Kishore, J. J. Knapp, I. A. Kovács, I. Lemmens, M. W. Mee, J. C. Mellor, C. Pollis, C. Pons, A. D. Richardson, S. Schlabach, B. Teeking, A. Yadav, M. Babor, D. Balcha, O. Basha, S.-F. Chin, S. G. Choi, C. Colabella, G. Coppin, C. D’Amata, D. De Ridder, S. De Rouck, M. Duran-Frigola, H. Ennajdaoui, F. Goebels, A. Gopal, G. Haddad, M. Helmy, Y. Jacob, Y. Kassa, R. Li, N. van Lieshout, A. MacWilliams, D. Markey, J. N. Paulson, S. Rangarajan, J. Rasla, A. Rayhan, T. Rolland, A. San Miguel, Y. Shen, D. Sheykhkarimli, G. M. Sheynkman, E. Simonovsky, M. Taşan, A. Tejeda , J.-C. Twizere, Y. Wang, R. Weatheritt, J. Weile, Y. Xia, X. Yang, E. Yeger-Lotem, Q. Zhong, P. Aloy, G. D. Bader, J. De L. Rivas, S. Gaudet, T. Hao, J. Rak, J. Tavernier, V. Tropepe, D. E. Hill*, M. Vidal*, F. P. Roth*, and M. A. Calderwood* (2020) A reference map of the human binary protein interactome Nature 580, 402-408 [biorxiv]
  • R. Juhász and I. A. Kovács (2020) Population boundary across an environmental gradient: Effects of quenched disorder, Physical Review Research 2 023123 [arXiv]
  • B. Ágg, A. Császár, M. Szalay-Bekő, D. V. Veres, R. Mizsei, P. Ferdinandy, P. Csermely and I. A. Kovács (2019) The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein-protein interaction and signaling networks, Bioinformatics btz257
  • I. A. Kovács, K. Luck, K. Spirohn, Y. Wang, C. Pollis, S. Schlabach, W. Bian, D-K. Kim, N. Kishore, T. Hao, M. A. Calderwood, M. Vidal and A.-L. Barabási (2019) Network-based prediction of protein interactions, Nature Communications 10, 1240 [biorxiv]
  • F. Cheng*, I. A. Kovács*, A-L. Barabási (2019) Network-based prediction of drug combinations, Nature Communications 10, 1197
  • N. Sahni, S. Yi, M. Taipale, J. I. Fuxman Bass, J. Coulombe-Huntington, F. Yang, J. Peng, J. Weile, G. I. Karras, Y. Wang, I. A. Kovács, A. Kamburov, I. Krykbaeva, M. H. Lam, G. Tucker, V. Khurana, A. Sharma, Y.-Y. Liu, N. Yachie, Q. Zhong, Y. Shen, A. Palagi, A. San-Miguel, C. Fan, D. Balcha, A. Dricot, D. M. Jordan, J. M. Walsh, A. A. Shah, X. Yang, A. Stoyanova, A. Leighton, M. A. Calderwood, Y. Jacob, M. E. Cusick, K. Salehi-Ashtiani, L. J. Whitesell, S. Sunyaev, B. Berger, A.-L. Barabási, B. Charloteaux, D. E. Hill, T. Hao, F. P. Roth, Y. Xia, A. J. M. Walhout, S. Lindquist and M. Vidal (2015) Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders, Cell, Vol. 161, Issue 3, p647–660.
  • R. Juhász, I. A. Kovács and F. Iglói (2015) Long-range epidemic spreading in a random environment, Phys. Rev. E 91 032815 [arXiv]
  • R. Juhász and I. A. Kovács (2013) Infinite randomness critical behavior of the contact process on networks with long-ranged connections, J. Stat. Mech. P06003 [arXiv]
  • A. Mihalik, A. S. Kaposi, I. A. Kovacs, T. Nanasi, R. Palotai, A. Rak, M. S. Szalay-Beko and P. Csermely. Edited by: B. Vedres, M. Scotti (2012) How creative elements help the recovery of networks after crisis: lessons from biology. Cambridge University Press, Networks in Social Policy Problems p. 179-188
  • I. J. Farkas, T. Korcsmáros, I.A. Kovács, Á. Mihalik, R. Palotai, G.I. Simkó, K.Z. Szalay, M. Szalay-Bekő, T. Vellai, S. Wang, P. Csermely (2011) Network-based tools for the identification of novel drug targets Science Signal. 4, pt3
  • P. Csermely, I. A. Kovács, Á. Mihalik, T. Nánási, R. Palotai, Á. Rák és M. Szalay (2009) Hogyan küzdik le a válságokat a biológiai hálózatok, és mit tanulhatunk el tőlük? Magyar Tudomány 170, 1381-1390 (in hungarian)
  • P. Csermely, T. Korcsmáros, I. A. Kovács, M. S. Szalay and C. Sőti (2008) Systems biology of molecular chaperone networks. In: The biology of extracellular molecular chaperones. Novartis Foundation Symposium Series Vol. 291, Wiley, pp. 45-58
  • Z. Spiro, I. A. Kovács and P. Csermely (2008) Drug-therapy networks and the predictions of novel drug targets J. Biol. 7, 20 [arXiv]
  • C. Böde, I. A. Kovács, M. Szalay, R. Palotai, T. Korcsmáros and P. Csermely (2007) Network analysis of protein dynamics FEBS Lett. 581, 2776-2782 [arXiv]
  • M. Szalay, I. A. Kovács, T. Korcsmáros, C. Böde and P. Csermely (2007) Stress-induced rearrangements of cellular networks: consequences for protection and drug design FEBS Lett. 581, 3675-3680 [arXiv]
  • T. Korcsmáros, M. Szalay, C. Böde, I. A. Kovács and P. Csermely (2007) How to design multi-target drugs: Target-search options in cellular networks Expert Op. Drug Discov. 2:1-10 [arXiv]
  • T. Korcsmáros, I. A. Kovács, M. S. Szalay and P. Csermely (2007) Molecular chaperones: the modular evolution of cellular networks J. Biosci. 32, 441-446 [arXiv]
  • I. A. Kovács, M. S. Szalay and P. Csermely (2005) Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins FEBS Lett. 579, 2254-2260 [arXiv]