Integrative Biological Modeling
 

I have recently left MSKCC to take up a senior position at the University of the Basque Country in Bilbao. Here is my new contact information:


Updates to my old web page below coming soon!

Integrative Biological Modeling @ MSKCC (2004-2008)
 

Contact (outdated)

 Jose M. G. Vilar, PhD

Assistant Member & Lab Head, Computational Biology Program, Sloan-Kettering Institute
Assistant Professor of Physiology, Biophysics, and Systems Biology, Weill Graduate School of Medical Sciences of Cornell University

Lab Address: Memorial Sloan-Kettering Cancer Center 415 East 68th Street - 11th Floor New York, NY 10065 Office: Z-1143

Mailing Address: Memorial Sloan-Kettering Cancer Center Computational Biology Program 1275 York Avenue, Box # 460 New York, NY 10065

Tel: 646-888-2603 | Fax: 646-422-0717 | E-mail: vilar@cbio.mskcc.org

Research

The traditional experimental approach to understand the cellular function 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 even the simplest biological systems. In order to piece back together all the genetic, biochemical, molecular, and structural information into a physiologically relevant description of the cell, one needs methods to transform molecular detail into a more integrated form of understanding complex behavior.

The long-term goal of our laboratory is to develop and use the computational and mathematical tools needed to accurately determine the cellular behavior in terms of the physical properties of the molecular interactions. Achieving this goal is a prerequisite for the rational identification of therapeutic molecular targets and for eventually bridging prediction of clinical outcomes with molecular properties.

Specifically, our current research focuses on the study of biological networks at multiple levels of organization, ranging from the molecular interactions to cellular processes to the collective behavior of cell populations. We are using this multilevel approach to i) predict computationally how mutations at the molecular level propagate to the collective behavior of cell populations; ii) identify the key elements that determine and control cell behavior; and iii) infer in vivo molecular properties from the observed cellular physiology.

At the molecular level, we have developed a combined computational-experimental method to infer in vivo free energies with unprecedented ±0.15 kcal/mol accuracy (Vilar & Leibler, 2003; Vilar & Saiz, 2005) and used it to obtain for the first time the length-dependent free energy of DNA looping inside a living cell (Vilar & Saiz, 2005; Saiz, Rubi & Vilar, 2005). For short loops, we found that, in addition to the typical intrinsic periodicity of the DNA double helix, there is an oscillatory component with about half the helical period. Through a novel multilevel analysis, we uncovered that such unexpected behavior of the free energy results from the presence of two representative looped conformations of the DNA-protein complex and that DNA architectural proteins stabilize just one of these two conformations (Saiz & Vilar, 2007).

At the molecular-cellular interface, we have developed the methodology to study macromolecular assembly in the cellular context and applied it to comprehensively characterize the properties of protein-DNA complexes and their effects in gene regulation (Saiz & Vilar, 2006). We use thermodynamic and structural information to both estimate reaction rates and incorporate the resulting assembly dynamics into the stochastic kinetics of cellular processes. This new stochastic approach is extremely efficient, to the extent that we can easily perform single-processor computer simulations of networks with thousands of different molecular species that cover simultaneously scales ranging from milliseconds to hours and days, and processes as diverse as dimerization, binding to DNA, transcription, translation, and degradation. We are now generalizing this approach to consider macromolecular assembly of signaling complexes at membranes and scaffolds.

At the cellular level, we are using coarse-grain kinetic approaches that collapse complex phenomena into sets of single rate processes to uncover how the structured intracellular organization affects and controls the cellular behavior. We have used this methodology to develop the first computational model of the TGF-β pathway, which is a pathway that transduces a wide range of extracellular signals into transcriptional responses that affect many cellular processes, including cell growth, apoptosis, differentiation, homeostasis, and morphogenesis (Vilar et al., 2006). Our model uncovered that coupling of signaling with intracellular receptor trafficking results in an extremely versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands.

At the cellular-cell population interface, we are developing computational tools to couple the stochastic dynamics of cellular process with the collective growth and death of cell populations. The goal is to study how changes in cell behavior affect the collective dynamics of cell populations and how the collective properties affect cellular processes. Along these lines, we have been able to use measured cellular properties, such as nutrient uptake and death rates, to predict, with experimental verification, precise boundaries for conditions that separate death from survival in the establishment of cooperation in engineered yeast populations (Shou, Ram & Vilar, 2007).

Our research so far has not only shown an excellent agreement with previously available (Saiz & Vilar, Nucleic Acids Research, in press), simultaneously generated (Shou, Ram & Vilar, 2007), and subsequently obtained (Lau et al., Cell 129, 123-134, 2007) experimental data, but has also provided an avenue to uncover how molecular details are intertwined with physical and functional constrains at multiple levels of biological organization. Among the key results, we have found that the molecular complexity so widely present in typical cellular processes, such as DNA looping or intracellular trafficking, impacts the cellular and cell population behavior in both quantitative and qualitative terms. In the case of DNA-protein complexes, we have shown that looping can increase specificity and affinity simultaneously and, at the same time, buffer molecular variability to produce phenotypically homogeneous cell populations, decrease transcriptional noise, and enable cooperative interactions to take place on demand, as required by the cellular context (Vilar & Leibler, 2003; Vilar & Saiz, 2005; Saiz & Vilar, 2006). Thus, our results indicate that key design principles, such as robustness or noise resistance, that have been shown to play important roles in shaping the structure of intracellular networks are also operating at the molecular level.

Publications

Press commentaries on our research:

Articles in journals

• L. Saiz and J. M. G. Vilar, Protein-protein/DNA interaction networks: versatile macromolecular structures for the control of gene expression, IET Systems Biology, to appear (2008).
• J. M. G. Vilar and J. M. Rubi, Failure of the Work-Hamiltonian Connection for Free-Energy Calculations, Phys. Rev. Lett. 100, 020601 (2008).
• L. Saiz and J. M. G. Vilar, Ab initio thermodynamic modeling of distal multisite transcription regulation, Nucleic Acids Research, 30, 726-731 (2008).
• M. H. Vainstein, J. M. Rubi and J. M. G. Vilar, Stochastic population dynamics in turbulent fields, Eur. Phys. J. ST 146, 177-187 (2007).
• L. Saiz and J. M. G. Vilar, Multilevel deconstruction of the in vivo behavior of looped DNA-protein complexes, PLoS One, 2(4): e355. doi:10.1371/journal.pone.0000355 (2007).
• W.Y. Shou, S. Ram, and J. M. G. Vilar, Synthetic cooperation in engineered yeast populations, Proc. Natl. Acad. Sci. USA 104, 1877-1882 (2007).
• J. M. G. Vilar and L. Saiz, Multiprotein DNA looping, Phys. Rev. Lett. 96, 238103 (2006).
• L. Saiz and J. M. G. Vilar, DNA looping: the consequences and its control, Curr. Opin. Struct. Biol. 16, 344-350 (2006).
• L. Saiz and J. M. G. Vilar, Stochastic dynamics of macromolecular-assembly networks, Nature/EMBO Molecular Systems Biology 2, 2006.0024, doi:10.1038/msb4100061 (2006).
• J. M. G. Vilar, Modularizing gene regulation, Nature/EMBO Molecular Systems Biology 2, 2006.0016, doi:10.1038/msb4100058 (2006).
• J. M. G. Vilar, R. Jansen, and C. Sander, Signal processing in the TGF-β superfamily ligand-receptor network, PLoS Comput. Biol. 2(1): e3 (2006).
• L. Saiz, J. M. Rubi, and J. M. G. Vilar, Inferring the in vivo looping properties of DNA, Proc. Natl. Acad. Sci. USA 102, 17642-17645 (2005).
• D. Reguera, J. M. Rubi, and J. M. G. Vilar, The Mesoscopic Dynamics of Thermodynamic Systems, J. Phys. Chem. B 109, 21502-21515 (2005).
• J. M. G. Vilar and L. Saiz, DNA looping in gene regulation: From the assembly of macromolecular complexes to the control of transcriptional noise, Curr. Opin. Genet. Dev. 15, 136-144 (2005).
• E. Korobkova, T. Emonet, J. M. G. Vilar, T. S. Shimizu, P. Cluzel, From molecular noise to behavioural variability in a single bacterium, Nature 428, 574-578 (2004). 
• J. M. G. Vilar and S. Leibler, DNA looping and physical constrains on transcription regulation, J. Mol. Biol. 331, 981-989 (2003).
•  J. M. G. Vilar, C. C. Guet, and S. Leibler, Modeling network dynamics: the lac operon, a case study, J. Cell Biol. 161, 471-476 (2003).
• J. M. G. Vilar, R. V. Solé, and J. M. Rubí, On the origin of plankton patchiness, Physica A 317, 239-246 (2002).
• José M. G. Vilar, Hao Yuan Kueh, Naama Barkai, and Stanislas Leibler, Mechanisms of noise-resistance in genetic oscillators, Proc. Natl. Acad. Sci. USA 99, 5988-15992 (2002). 
• J. M. G. Vilar and J. M. Rubí, Thermodynamics "beyond" local equilibrium,  Proc. Natl. Acad. Sci. USA 98, 11081-11084 (2001). 
• J. M. G. Vilar and J. M. Rubí, Noise suppression by noise, Phys. Rev. Lett. 86, 950-953 (2001). 
• L. Saiz, J. M. G. Vilar, and J. M. Rubí, Field-induced force-suppression in ferromagnetic colloids, Physica A 293, 51-58 (2001).
• J. M. G. Vilar and J. M. Rubí, Ordering periodic spatial structures by non-equilibrium fluctuations, Physica A 277, 327-334 (2000).
• J. M. Rubí and J. M. G. Vilar, The Rheology of Field-Responsive Suspensions, J. Phys.: Cond. Matter  12, A75-A84 (2000).
• A. Pérez-Madrid, T. Alarcón, J.M.G. Vilar, and J. M. Rubí, Mesoscopic Approach to the "Negative'' Viscosity Effect in Ferrofluids, Physica A 270, 403-412 (1999).
• J. M. G. Vilar, R. V. Solé, and J. M. Rubí, Noise and Periodic Modulations in Neural Excitable Media, Phys. Rev. E  59, 5920-5928 (1999).
• J. M. G. Vilar and J. M. Rubí, Scaling Concepts in Periodically Modulated Noisy Systems, Physica A 264, 1-14 (1999).
• J. M. G. Vilar, G. Gomila and J. M. Rubí, Stochastic Resonance in Noisy Nondynamical Systems, Phys. Rev. Lett. 81, 14-17 (1998).
• J. M. G. Vilar and R. V. Solé, Effects of Noise in Symmetric Two-Species Competition, Phys. Rev. Lett. 80, 4099-4102 (1998).
• J. M. G. Vilar and J. M. Rubí, Effect of the Output of the System in Signal Detection, Phys. Rev. E 56, 32R-35R (1997).
• J. M. G. Vilar and J. M. Rubí, Stochastic Multiresonance, Phys. Rev. Lett. 78, 2882-2885 (1997). 
• J. M. G. Vilar and J. M. Rubí, Spatiotemporal Stochastic Resonance in the Swift-Hohenberg Equation, Phys. Rev. Lett. 78, 2886-2889 (1997).
• José M. Gómez-Vilar and Ricard V. Solé, On cellular automata models for quantum systems, J. Phys. A 29, 8169 -8171 (1996).
• J. M. G. Vilar, A. Pérez-Madrid, and J. M. Rubí, Stochastic Resonance in a Dipole, Phys. Rev. E 54, 6929-6932 (1996).
• J. M. G. Vilar and J. M. Rubí, Divergent Signal-to-Noise Ratio and Stochastic Resonance in Monostable Systems, Phys. Rev. Lett. 77, 2863-2866 (1996).

Book chapters

• J. M. G. Vilar and J. M. Rubí, Scaling of Noise and Constructive Aspects of Fluctuations, in Lecture Notes in Physics: "Stochastic Processes in Physics", J. Freund and T. Pöschel, eds., Springer Verlag (Berlin, Heidelberg 2000), Vol. 557: pp. 121-130 (2000).

Books

D. Reguera, J. M. G. Vilar, and J. M. Rubí (eds.), Statistical Mechanics of Biocomplexity, Lecture Notes in Physics (Springer Verlag, Berlin 1999).

Presentations

Upcoming invited talks

• TBD. Workshop on Cellular Decision Making, June 2008, Toronto (Canada).
• Signal processing in the TGF-β superfamily ligand-receptor network. Conference on Systems Biology of Mammalian Cells, May 2008, Dresden (Germany).

Selected invited talks

• Stochastic dynamics of protein-DNA complexes. Workshop on Deconstructing Biochemical Networks, September 2007, Montreal (Canada).
• Stochastic dynamics of protein-DNA complexes. Information Processing in Cellular Signaling and Gene Regulation, August 2007, Santa Fe (New Mexico).
• Inferring the in vivo looping properties of DNA. Third international conference on Multiscale Materials Modeling, September 2006, Freiburg (Germany).
• Computational Modeling of the TGF-β Superfamily Ligand-Receptor Network. British Society for Developmental Biology Autumn Meeting: Signal Transduction and Integration in Embryonic Development, Sept 2006, Dundee (UK).
• Stochastic dynamics of protein-DNA complexes. Workshop on Nanomechanics of Biomolecules, August 2006, Ascona (Switzerland).
• Macromolecular assembly on looped DNA. ICAM workshop on "Chromatin Dynamics, Gene Regulation and Silencing", August 2006, Snowmass (Colorado).
• Stochastic Dynamics of Macromolecular-Assembly Networks. XX Sitges Conference on Statistical Mechanics. Physical Biology: from Molecular Interactions to Cellular Behavior, June 2006, Sitges, Barcelona (Spain). 
• 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).

People

Current

Jose Vilar (Assistant Member & Lab Head)

Ricky Chachra (Rotation Student, PBSB program)

Igor Segota (Rotation Student, PBSB program)

Richard Osafo (Administrative Assistant; Tel: 646-888-2808)

Want to join? See opportunities below! Two postdocs just moved West to start their own labs.

Former

Leonor Saiz (Postdoc, 2004-2007)

Wenying Shou (Postdoc, 2006-2007)

Anamika Sarkar (Postdoc, 2004-2005)

Rotation Students

Cameron Wellock (Tri-i CBM program, 2005)
Ori Weitz (Tri-i CBM program, 2006)
Pawel Kocieniewski (Tri-i CBM program, 2007)
Fan Xia (PBSB program, 2007)

Opportunities

There are openings for two Postdoctoral Research Associates. To apply send a CV, description of research interests, and contact information of three references to vilar@cbio.mskcc.org.

Prospective graduate students should apply through the affiliated programs at Memorial Sloan-Kettering Cancer Center and Weill Graduate School of Medical Sciences of Cornell University:

• Physiology, Biophysics, and Systems Biology Program
• Tri-Institutional Computational Biology and Medicine Program
• Tri-Institutional MD-PhD Program
• Gerstner Sloan-Kettering Graduate School