"Highlights of the Year 2014" (physics.aps.org/articles/v7/132) by the American Physical Society to "Entropy of leukemia on multidimensional morphological and molecular landscapes" [Phys. Rev. X 4, 21038 (2014)]
Awarded "Best of 2013" by the Biophysical Society to the article "Systems Biophysics of Gene Expression" [Biophys. J. 104, 2574-2585 (2013)]
2012 Werfen-Izasa-Beckman-Coulter prize in Biophysics
Participant in the 2016 expert review panel conducted by The National Cancer Institute (NCI) Division of Cancer Biology (DCB) to assess the impact and innovativeness of research funded by NCI's Physical Sciences - Oncology Centers (PS-OC) Program,
NCI's Integrative Cancer Biology Program (ICBP), and NCI R01 awards.
The traditional experimental approach to the study of the functioning of cells has been remarkably successful at identifying cellular components and their interactions. Current automated technologies have brought the cartoon-like representations of
cellular processes to exponentially growing webs of nodes and links that seem as close to completion as ever. The complexity of the emerging picture, however, makes it clear that all this information by itself is not enough to truly understand processes
such as cancer. In order to piece back together all the genetic, biochemical, molecular, and structural information into a physiologically relevant description of the cell, one needs "constructive" methods. Computational modeling has emerged as a
promising tool for transforming molecular detail into a more integrated form of understanding complex behavior.
We use computational and mathematical modeling to study biological networks that are relevant to cancer. We are interested, not
only in the interactions between cellular components, but also in the resulting cellular behavior and its integration into the physiological context of an organism. We study computationally how mutations affect the molecular properties of the cellular
components; how the mutated components affect different pathways; and how these modified pathways confer cell-growth advantages during tumor progression and metastasis. Having a global view of all these processes and their effects through all relevant
levels of biological organization is crucial to identify and characterize the key control elements of the system.
We are currently working on:
Gene regulation (RXR and other nuclear hormone receptors)
Signal transduction (EGF and TGF-β pathways)
Control of cell growth and death (Bcl-2/Bax in metabolism and apoptosis)
We are also developing new computational approaches to determine, capture, and use the relevant biological information. We are especially interested in stochastic analyses and in multilevel and multiscale methods.
PUBLICATIONS
Press on our research
| |
Open Source Software
J. M. G. Vilar and L. Saiz (2019), Scaling and Regression over Nearest Neighbors for Single-Cell Signaling Prediction in Breast Cancer, https://www.synapse.org/#!Synapse:syn21188145
"CplexA" (released on April 19th, 2010/updated on December 12th, 2012) is a software package (available for Mathematica, Python, and Matlab) to compute probabilities and average properties of macromolecular assembly and its effects in gene regulation.
CplexA and the tutorial are available at https://sourceforge.net/projects/cplexa/
A. Keller, R.C. Gerkin, Y. Guan, A. Dhurandhar, G. Turu, B. Szalai, J.D. Mainland, Y. Ihara, C.W. Yu., R. Wolfinger, C. Vens, L. Schietgat, K. De Grave, R. Norel, DREAM Olfaction Prediction Consortium (*), G. Stolovitzky, G.A. Cecchi, L.B. Vosshall,
P. Meyer (* including J.M.G. Vilar), Predicting
human olfactory perception from chemical features of odor molecules, Science 355, 820-826 (2017).
D.P. Noren, B.L. Long, R. Norel, K. Rrhissorrakrai, K. Hess, C.W. Hu, A.J. Bisberg, A. Schultz, E. Engquist, L. Liu, X. Lin, G.M. Chen, H. Xie, G.A.M. Hunter, P.C. Boutros, O. Stepanov, DREAM 9 AML-OPC Consortium (*), T. Norman, S.H. Friend, G.
Stolovitzky, S. Kornblau, A.A. Qutub (* including J.M.G. Vilar), A
Crowdsourcing Approach to Developing and Assessing Prediction
Algorithms for AML Prognosis, PLoS Comput. Biol. 12: e1004890 (2016).
N. Aghaeepour, G. Finak, The FlowCAP Consortium, The DREAM Consortium (*), H. Hoos, T.R. Mosmann, R. Brinkman, R. Gottardo, and R.H. Scheuermann (* including J. M. G. Vilar), Critical
assessment of automated flow cytometry data analysis techniques, Nature Methods 10, 228-238 (2013). Supplementary Information [pdf].
J. M. G. Vilar and J. M. Rubi, Scaling
of Noise and Constructive Aspects of Fluctuations, in Lecture Notes in Physics: "Stochastic Processes in Physics", J. Freund and T. Poschel, eds., Springer Verlag (Berlin, Heidelberg 2000), Vol. 557: pp. 121-130 (2000).
Books
D. Reguera, J. M. G. Vilar, and J. M. Rubi (eds.), Statistical Mechanics of Biocomplexity, Lecture Notes in Physics (Springer
Verlag, Berlin 1999).
MORE
Selected invited talks
Dynamics-informed characterization of molecular, cellular, and population mechanisms.
SEB Centenary Conference 2023, July 2023, Edinburg (UK).
Inverting convolutional neural networks for super-resolution identification of regime changes in epidemiological time series.
Belgrade
BioInformatics Conference 2023, June 2023, Belgrade (Serbia).
Inference and prediction in biological systems at the molecular, cellular, and cell-population levels,
Basel Computational Biology Seminar Series , October 2019, Basel (Switzerland).
Presentation of the winner solution of the European Union Big Data Technologies Horizon Prize.
European Big Data Value Forum 2018, November 2018, Vienna (Austria).
Multidimensional entropies for diagnosing Acute Myeloid Leukemia from patient samples using flow cytometry data, DREAM-RECOMB
annual meeting, October 2011, Barcelona (Spain).
Signal processing in the TGF-β superfamily ligand-receptor network. Systems Biology and Connective Tissue Disorders Meeting, February 2011, Washington, DC.
From components to systems and vice versa: lessons from gene regulation and synthetic cooperation. Workshop
on
Cellular Decision Making, June 2008, Toronto (Canada).
Stochastic Dynamics of Macromolecular-Assembly Networks. Systems Biology Discussion Group, New York Academy of Sciences, November 2005, New York (New York).
Stochastic Dynamics of Macromolecular-Assembly Networks. ISQBP Gilda Loew Memorial Meeting, October 2005, Staten Island (New York).
Mechanisms of Noise-resistance in Genetic Oscillators. SIAM Conference on the Life Sciences, July 2004, Portland (Oregon).
Mathematical analysis of gene circuits. Conference on Mathematical Modelling of Plant Development and Gene Networks, University of Warwick, May 2004, Coventry (United Kingdom).
Modeling the Networks of the Cell: Molecular, Cellular, and Population Levels. Workshop on Biological Information and Statistical Physics, July 2003, Dresden (Germany).
Modeling Noise, Switches and Clocks. Workshop on Dynamics, Adaptation and Fluctuations in Bio-networks, KITP, University of California, March 2003, Santa Barbara (California).
Noise in the cell: from molecular mechanisms to populations via network design. Annual American Physical Society March Meeting, March 2002, Indianapolis (Indiana).
Networking with noise at the molecular, cellular, and population level. Gordon Research Conference on Bioinformatics: From inference to Predictive Models, August 2001, Tilton (New Hampshire).
Modules of modules: from molecular interactions to cell populations. Workshop on Design and Control of Biochemical Networks, June 2001, Leiden (The Netherlands).
Ordering Periodic Spatial Structures by Noise. Workshop on Fluctuations Far From Equilibrium: Noise Induced Transport, April 1998, Dresden (Germany).