Modeling cellular life: From single molecules to cellular function

August 19, 2014 to August 22, 2014
Location : CECAM-HQ-EPFL, Lausanne, Switzerland
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  • Ed O'Brien (Pennsylvania State University, USA)
  • Arup Chakraborty (Massachusetts Institute of Technology, USA)
  • Ursula Röthlisberger (Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland)








*** Registration for this conference is free. You can register by clicking on the 'APPLY' tab at the top of this document and following the instructions. This conference is limited to 80 participants. Participants are accepted on a first-come first-served basis so those interested are encouraged to apply early. ***


Being able to model and understand the emergence of cellular life from a cell’s components is a formidable challenge that is at the forefront of many research group’s efforts. Addressing this challenge requires that a range of computational tools, from quantum mechanics to systems biology, be brought to bear to answer questions at the different spatial and time scales relevant to cellular life. Developing new computational and theoretical methods and applying existing methods in new ways are all crucial components to making progress in this area.  Success in this field holds the promise of providing fundamental insights into molecular and cellular biology and of opening up new research avenues in the fields of biomedicine, synthetic biology and biotechnology.

The purpose of this conference, titled “Modeling Cellular Life: From single molecules to cellular function”, is to provide a snapshot of the research efforts being made on multiple fronts to provide quantitative insights and predictions about cellular life. Methods that will be highlighted at this conference include quantum mechanical calculations that probe the chemical reactions that drive the cell’s macromolecular machines (1,2); coarse-grained models that allow us to understand the behavior of protein and nucleic acid assemblies (3-8); all-atom simulations that explore structural mechanisms essential to the transport of molecules within the cell (9-11); mathematical models coupled with experimental data sets that make testable predictions about the response of cells to changing environmental conditions (12-16); bioinformatic methods that utilize genomic and proteomic information to gain insight into the dynamics of cellular life (17-19); and systems biology methods that are integrating high-throughput data sets with theory to predict how cells grow and behave (20-22).

This conference is timely because new computing resources and methods are allowing the scientific community to model aspects of the cell that were previously intractable. This conference is needed because it will bring together scientists from sub-disciplines that do not regularly interact with each other. Therefore this conference will provide the opportunity for attendees to hear about new perspectives and methods on topics of common interest, providing synergistic and cross-fertilization opportunities for their research.

Aims of this Conference

The primary aims of this workshop are to highlight the state-of-the-art in computational method development and application to modeling the cell and its critical components and processes; to bring together scientists representing a wide-breadth of computational skill sets interested in modeling cellular life; and to invite to the conference leaders in the community who are generating cutting-edge data sets that are likely to be important in modeling cellular life (23-25). Throughout this conference the methods that are being developed and applied at different levels of molecular and cellular organization will be presented and discussed.


Computational Issues

Computational studies of the cell represent a significant challenge due to the hierarchy of length and time scales involved in this multi-component system. The relevant biological time scales span from picoseconds to hours, and the spatial scales span from angstroms to micrometers. Cutting edge computational strategies are therefore being devised by members of the research community to tackle these complex challenges. For this reason, many of the scientists that will attend this conference are at the forefront of devising such strategies that include the use of coarse grained and all-atom models (4, 9, 26, 27), distributed computing methodologies (28), mathematical modeling (13, 22), and systems biology methods (17, 21).  By focusing this meeting on the theoretical and computational methods that can be brought to bear on this topic this conference will provide insight into where computational methods need to go to answer crucial questions regarding cellular behavior.


Conference Format

We will limit the number of participants of this conference to 80. The conference will be held on the EPFL campus. There will be 24 speakers, each giving a 45-minute presentation including 10 minutes for questions and answers. A poster session will also be held. This 3.5 day conference will be comprised of both morning and afternoon sessions, with the talks being evenly distributed amongst the sessions (a program will be posted by mid-March).


1. Acosta-Silva C, Bertran J, Branchadell V, & Oliva A (2013) Quantum mechanical study on the mechanism of peptide release in the ribosome. The journal of physical chemistry. B 117(13):3503-3515.
2. Wallin G, Kamerlin SC, & Aqvist J (2013) Energetics of activation of GTP hydrolysis on the ribosome. Nature Communications 4:1733.
3. O'Brien EP, Christodoulou J, Vendruscolo M, & Dobson CM (2011) New Scenarios of Protein Folding Can Occur on the Ribosome. J Am Chem Soc 133(3):513-526.
4. Elcock AH (2006) Molecular Simulations of cotranslational protein folding: Fragment stabilities, folding cooperativity, and trapping in the ribosome. Plos Comput Biol 2(7):824-841.
5. Levy Y & Onuchic JN (2006) Mechanisms of protein assembly: Lessons from minimalist models. Accounts Chem Res 39(2):135-142.
6. Lin JC & Thirumalai D (2012) Gene regulation by riboswitches with and without negative feedback loop. Biophys J 103(11):2320-2330.
7. Savelyev A, Materese CK, & Papoian GA (2011) Is DNA's rigidity dominated by electrostatic or nonelectrostatic interactions? J Am Chem Soc 133(48):19290-19293.
8. Imakaev M, et al. (2012) Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods 9(10):999-1003.
9. Gumbart JC, Teo I, Roux B, & Schulten K (2013) Reconciling the roles of kinetic and thermodynamic factors in membrane-protein insertion. J Am Chem Soc 135(6):2291-2297.
10. Collepardo-Guevara R & Schlick T (2012) Crucial role of dynamic linker histone binding and divalent ions for DNA accessibility and gene regulation revealed by mesoscale modeling of oligonucleosomes. Nucleic Acids Res 40(18):8803-8817.
11. Sanbonmatsu KY (2012) Computational studies of molecular machines: the ribosome. Curr Opin Struc Biol 22(2):168-174.
12. Fisher J & Piterman N (2010) The executable pathway to biological networks. Brief Funct Genomics 9(1):79-92.
13. Wensink HH, et al. (2012) Meso-scale turbulence in living fluids. P Natl Acad Sci USA 109(36):14308-14313.
14. Tuller T, et al. (2010) An Evolutionarily Conserved Mechanism for Controlling the Efficiency of Protein Translation. Cell 141(2):344-354.
15. Reuveni S, Meilijson I, Kupiec M, Ruppin E, & Tuller T (2011) Genome-scale analysis of translation elongation with a ribosome flow model. Plos Comput Biol 7(9):e1002127.
16. Boedicker JQ, Garcia HG, & Phillips R (2013) Theoretical and experimental dissection of DNA loop-mediated repression. Phys Rev Lett 110(1):018101.
17. Zabet NR & Adryan B (2012) GRiP: a computational tool to simulate transcription factor binding in prokaryotes. Bioinformatics 28(9):1287-1289.
18. Plotkin JB & Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12(1):32-42.
19. Drummond DA & Wilke CO (2008) Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134(2):341-352.
20. Anonymous (!!! INVALID CITATION !!!)
21. Karr JR, et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150(2):389-401.
22. Roberts E, Stone JE, & Luthey-Schulten Z (2013) Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation. J Comput Chem 34(3):245-255.
23. Ingolia NT, Ghaemmaghami S, Newman JRS, & Weissman JS (2009) Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling. Science 324(5924):218-223.
24. Churchman LS & Weissman JS (2012) Native elongating transcript sequencing (NET-seq). Curr Protoc Mol Biol Chapter 4:Unit 4 14 11-17.
25. Larson DR, Zenklusen D, Wu B, Chao JA, & Singer RH (2011) Real-time observation of transcription initiation and elongation on an endogenous yeast gene. Science 332(6028):475-478.
26. O'Brien EP, Hsu STD, Christodoulou J, Vendruscolo M, & Dobson CM (2010) Transient Tertiary Structure Formation within the Ribosome Exit Port. J Am Chem Soc 132(47):16928-16937.
27. Zhang Z, Sanbonmatsu KY, & Voth GA (2011) Key intermolecular interactions in the E. coli 70S ribosome revealed by coarse-grained analysis. J Am Chem Soc 133(42):16828-16838.
28. Whitford PC, et al. (2010) Accommodation of aminoacyl-tRNA into the ribosome involves reversible excursions along multiple pathways. Rna 16(6):1196-1204.
29. Dill KA, Ghosh K, & Schmit JD (2011) Physical limits of cells and proteomes. P Natl Acad Sci USA 108(44):17876-17882.
30. Chung W, Abel SM, & Chakraborty AK (2012) Protein clusters on the T cell surface may suppress spurious early signaling events. Plos One 7(9):e44444.
31. Sun J, et al. (2007) Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12. Algorithms Mol Biol 2:2.
32. de Pablo JJ (2011) Coarse-grained simulations of macromolecules: from DNA to nanocomposites. Annu Rev Phys Chem 62:555-574.
33. de la Rosa MA, Koslover EF, Mulligan PJ, & Spakowitz AJ (2010) Dynamic strategies for target-site search by DNA-binding proteins. Biophys J 98(12):2943-2953.
34. Zandarashvili L, et al. (2012) Asymmetrical roles of zinc fingers in dynamic DNA-scanning process by the inducible transcription factor Egr-1. P Natl Acad Sci USA 109(26):E1724-1732.
35. Vandecan Y & Blossey R (2012) Stochastic description of single nucleosome repositioning by ACF remodelers. Phys Rev E Stat Nonlin Soft Matter Phys 85(6 Pt 1):061920.
36. Schink S, et al. (2012) Quantitative analysis of the nanopore translocation dynamics of simple structured polynucleotides. Biophys J 102(1):85-95.
37. Jamali T, Jamali Y, Mehrbod M, & Mofrad MR (2011) Nuclear pore complex: biochemistry and biophysics of nucleocytoplasmic transport in health and disease. Int Rev Cell Mol Biol 287:233-286.
38. Cacace F, Paci P, Cusimano V, Germani A, & Farina L (2012) Stochastic modeling of expression kinetics identifies messenger half-lives and reveals sequential waves of co-ordinated transcription and decay. Plos Comput Biol 8(11):e1002772.
39. Sharma AK & Chowdhury D (2011) Stochastic theory of protein synthesis and polysome: ribosome profile on a single mRNA transcript. J Theor Biol 289:36-46.
40. Pineiro A, Bond PJ, & Khalid S (2011) Exploring the conformational dynamics and membrane interactions of PorB from C. glutamicum: a multi-scale molecular dynamics simulation study. Biochim Biophys Acta 1808(6):1746-1752.
41. Sneddon MW, Faeder JR, & Emonet T (2011) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 8(2):177-U112.
42. Scott M & Hwa T (2011) Bacterial growth laws and their applications. Curr Opin Biotechnol 22(4):559-565.
43. Ben-Jacob E, Coffey DS, & Levine H (2012) Bacterial survival strategies suggest rethinking cancer cooperativity. Trends Microbiol 20(9):403-410.