CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.03 - May-June

Subscribe

Issue No.03 - May-June (2012 vol.9)

pp: 911-923

Paolo Ballarini , Ecole Centrale de Paris, Chatenay-Malabry, France

Tommaso Mazza , IRCCS Casa Sollievo della Sofferenza, Rome

Davide Prandi , Laboratory of Computational Oncology, Mattarello

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.27

ABSTRACT

Important achievements in traditional biology have deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for cell's regulation. Such an expanding knowledge also introduces new issues. For example, the increasing comprehension of the interdependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every "piece” of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations, high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here, we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.

INDEX TERMS

Stochastic simulation, parallel computing, graphs and network, systems biology.

CITATION

Paolo Ballarini, Tommaso Mazza, Davide Prandi, "The Relevance of Topology in Parallel Simulation of Biological Networks",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.9, no. 3, pp. 911-923, May-June 2012, doi:10.1109/TCBB.2012.27REFERENCES

- [1] G. Carter, "Inferring Network Interactions within a Cell,"
Briefing in Bioinformatics, vol. 6, pp. 380-389, 2005.- [2] M.S. Samoilov and A.P. Arkin, "Deviant Effects in Molecular Reaction Pathways,"
Nature Biotechnology, vol. 24, no. 10, pp. 1235-1240, 2006.- [3] A. Raj and A. van Oudenaarden, "Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences,"
Cell, vol. 135, no. 2, pp. 216-226, 2008.- [4] V. Shahrezaei and P.S. Swain, "The Stochastic Nature of Biochemical Networks,"
Current Opinion in Biotechnology, vol. 19, no. 4, pp. 369-374, 2008.- [5] D. Gillespie, "Exact Stochastic Simulation of Coupled Chemical Reactions,"
J. Physical Chemistry, vol. 81, no. 25, pp. 2340-2361, 1977.- [6] M. Gibson and J. Bruck, "Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels,"
J. Physical Chemistry A, vol. 104, pp. 1876-1889, 2000.- [7] Y. Cao, H. Li, and L. Petzold, "Efficient Formulation of the Stochastic Simulation Algorithm for Chemically Reacting Systems,"
J. Physical Chemistry, vol. 121, no. 9, pp. 4059-4067, 2004.- [8] J.M. McCollum, G.D. Peterson, C.D. Cox, M.L. Simpson, and N.F. Samatova, "The Sorting Direct Method for Stochastic Simulation of Biochemical Systems with Varying Reaction Execution Behavior,"
Computational Biology and Chemistry, vol. 30, no. 1, pp. 39-49, 2006.- [9] L. Lok and R. Brent, "Automatic Generation of Cellular Reaction Networks with Moleculizer 1.0,"
Nature Biotechnology, vol. 23, no. 1, pp. 131-136, 2005.- [10] D. Gillespie, "Approximate Accelerated Stochastic Simulation of Chemically Reacting Systems,"
The J. Chemical Physics, vol. 115, no. 4, pp. 1716-1733, 2001.- [11] Y. Cao, D. Gillespie, and L. Petzold, "Efficient Step Size Selection for the Tau-Leaping Simulation Method,"
The J. Chemical Physics, vol. 123, p. 044109, 2006.- [12] T. Tian and K. Burrage, "Binomial Leap Methods for Simulating Stochastic Chemical Kinetics,"
The J. Chemical Physics, vol. 121, pp. 10356-10364, 2004.- [13] J. Chatterjee, D. Vlachos, and M. Katsoulakis, "Binomial Distribution Based Tau-Leap Accelerated Stochastic Simulation,"
The J. Chemical Physics, vol. 122, p. 024112, 2005.- [14] M. Rathinam et al., "Stiffness in Stochastic Chemically Reacting Systems: The Implicit Tau-Leaping Method,"
The J. Chemical Physics, vol. 119, pp. 12784-12794, 2003.- [15] C. Rao and A. Arkin, "Stochastic Chemical Kinetics and the Quasi-Steady-State Assumption: Application to the Gillespie Algorithm,"
The J. Chemical Physics, vol. 118, pp. 4999-5010, 2003.- [16] Y. Cao, D. Gillespie, and L.R. Petzold, "The Slow-Scale Stochastic Simulation Algorithm,"
J. Chemical Physics, vol. 122, no. 1,014116 2005.- [17] L. Harris and P. Clancy, "A 'Partitioned Leaping' Approach for Multiscale Modeling of Chemical Reaction Dynamics,"
The J. Chemical Physics, vol. 125, p. 144107, 2006.- [18] J. Meredith, S. Alam, and J. Vetter, "Analysis of a Computational Biology Simulation Technique on Emerging Processing Architectures,"
Proc. IEEE Parallel and Distributed Processing Symp. and IEEE Int'l Workshop High Performance Computational Biology (HiCOMB), pp. 1-8, 2007.- [19] H. Li and L. Petzold, "Efficient Parallelization of the Stochastic Simulation Algorithm for Chemically Reacting Systems on the Graphics Processing Unit,"
The Int'l J. High Performance Computing Applications, vol. 24, pp. 107-116, 2009.- [20] C. Dittamo and D. Cangelosi, "Optimized Parallel Implementation of Gillespie's First Reaction Method on Graphics Processing Units,"
Proc. Int'l Conf. Computer Modeling and Simulation, pp. 156-161, 2009.- [21] K. Burrage et al., "A Grid Implementation of Chemical Kinetic Simulation Methods in Genetic Regulation,"
Proc. Conf. Advanced Computing, Grid Applications and e-Research (APAC '03), pp. 1-3, 2003.- [22] J. Niu et al., "A Distributed-Based Stochastic Simulation Algorithm for Large Biochemical Reaction Networks,"
Proc. First Int'l Conf. Bioinformatics and Biomedical Eng., pp. 502-505, 2007.- [23] L. Salwinski and D. Eisenberg, "In Silico Simulation of Biological Network Dynamics,"
Nature Biotechnology, vol. 22, no. 8, pp. 1017-1019, 2004.- [24] P. Ballarini, R. Guido, T. Mazza, and D. Prandi, "Taming the Complexity of Biological Pathways through Parallel Computing,"
Briefings in Bioinformatics, vol. 10, no. 3, pp. 1278-288, 2009.- [25] S.D. Mieszko Lis, M.N. Artyomov, and A.K. Chakraborty, "Efficient Stochastic Simulation of Reaction-Diffusion Processes via Direct Compilation,"
Bioinformatics, vol. 25, no. 17, pp. 2289-91, 2009.- [26] A. Gupta et al.,
Introduction to Parallel Computing: Design and Analysis of Algorithms. Addison Wesley, 2003.- [27] C. Schaefer, "Pathway Databases,"
Annals of New York Academy of Sciences, vol. 1020, no. 1, pp. 77-91, 2004.- [28] H. Chang et al., "Transcriptome-Wide Noise Controls Lineage Choice in Mammalian Progenitor Cells,"
Nature, vol. 453, no. 7194, pp. 544-547, 2008.- [29] M. Elowitz, A.J. Levine, E.D. Siggia, and P.S. Swain, "Stochastic Gene Expression in a Single Cell,"
Science, vol. 297, no. 5584, p. 1183, 2002.- [30] D. Gillespie, "A Rigorous Derivation of the Chemical Master Equation,"
Physica A: Statistical Mechanics and Its Applications, vol. 188, nos. 1-3, pp. 404-425, 1992.- [31] D. Köhn and N. Le Novère, "The Kinetic Simulation Algorithm Ontology," http://www.ebi.ac.uk/compneur-srvkisao/, 2012.
- [32] M.S. Samoilov, S. Plyasunov, and A. Arkin, "Stochastic Amplification and Signaling in Enzymatic Futile Cycles through Noise-Induced Bistability with Oscillations,"
Proc. Nat'l Academy of Sciences of USA, vol. 102, no. 7, pp. 2310-2315, 2005.- [33] K. Burrage, T. Tian, and P. Burrage, "A Multi-Scaled Approach for Simulating Chemical Reaction Systems,"
Progress in Biophysics and Molecular Biology, vols. 2/3, no. 85, pp. 217-234, 2004.- [34] L. Dematté and T. Mazza, "On Parallel Stochastic Simulation of Diffusive Systems,"
Proc. Sixth Int'l Conf. Computational Methods in Systems Biology (CMSB '08), M. Heiner and A.M. Uhrmacher, eds., pp. 191-210, 2008.- [35] T. Mazza and R. Guido, "Guidelines for Parallel Simulation of Biological Reactive Systems,"
Proc.. Bioinformatics Methods for Biomedical Complex System Applications vol. 2, pp. 83-85, 2008.- [36] T. Mazza, A. Romanel, and F. Jordán, "Estimating the Divisibility of Complex Biological Networks by Sparseness Indices,"
Briefings in Bioinformatics, vol. 11, no. 3, pp. 364-374, 2010.- [37] K. Schloegel, G. Karypis, and V. Kumar, "Graph Partitioning for High-Performance Scientific Simulations,"
Sourcebook of Parallel Computing, J. Dongarra, I. Foster, G. Fox, K. Kennedy, A. White, and M. Kaufmann, eds., pp. 491-541, Morgan Kaufmann Publishers, Inc., 2000.- [38] D.R. Jefferson, "Virtual Time,"
ACM Trans. Programming Languages and Systems, vol. 7, no. 3, pp. 404-425, 1985.- [39] J. Woodger,
Biological Principles: A Critical Study. Harcourt, 1929.- [40] P. Holme, M. Huss, and H. Jeon, "Subnetwork Hierarchies of Biochemical Pathways,"
Bioinformatics, vol. 19, no. 4, pp. 532-538, 2003.- [41] D. Watts and S. Strogatz, "Collective Dynamics of 'Small-World" Networks,'
Nature, vol. 393, no. 6684, pp. 440-442, 1998.- [42] H. Jeong, B. Tombor, R. Albert, Z. Oltvai, and A.-L. Barabási, "The Large-Scale Organization of Metabolic Networks,"
Nature, vol. 407, pp. 651-654, 2000.- [43] L.A.N. Amaral, A. Scala, M. Barthélémy, and H.E. Stanley, "Classes of Small-World Networks,"
Proc. Nat'l Academy of Sciences USA, vol. 97, no. 21, pp. 11149-11152, 2000.- [44] A. Barabási and R. Albert, "Emergence of Scaling in Random Networks,"
Science, vol. 286, no. 5439, pp. 509-512, 1999.- [45] L.-M. G and J. van Helden, "The Powerful Law of the Power Law and Other Myths in Network Biology,"
Molecular Biosystems, vol. 5, no. 12, pp. 1482-1493, 2009.- [46] S. Wuchty, "Scale-Free Behaviour in Protein Domain Networks,"
Molecular Biology and Evolution, vol. 18, pp. 1694-1702, 2001.- [47] M. Nacher, T. Yamada, S. Goto, M. Kanehisa, and T. Akutsu, "Two Complementary Representations of Scale-Free Networks,"
Physica A, vol. 349, pp. 349-363, 2004.- [48] R. Tanaka, "Scale-Rich Metabolic Networks,"
Physical Rev. Letters, vol. 94, no. 16, p. 168101, 2005.- [49] H.-W. Ma and A.-P. Zeng, "The Connectivity Structure, Giant Strong Component and Centrality of Metabolic Networks,"
Bioinformatics, vol. 19, no. 11, pp. 1423-1430, 2003.- [50] R. Cohen, "Scale-Free Networks Are Ultrasmall,"
Physical Review Letters, vol. 90, p. 58701, 2003.- [51] D. Lane, "p53, Guardian of the Genome,"
Nature, vol. 358, no. 6381, pp. 15-16, 1992.- [52] R. Albert, H. Jeong, and A.-L. Barabási, "Error and Attack Tolerance of Complex Networks,"
Nature, vol. 406, pp. 378-382, 2000.- [53] H. Jeong, S. Mason, A.-L. Barabási, and Z. Oltvai, "Lethality and Centrality in Protein Networks,"
Nature, vol. 411, pp. 41-42, 2001.- [54] A. Barabási and Z. Oltvai, "Network Biology: Understanding the Cells Functional Organization,"
Nature Reviews Genetics, vol. 5, pp. 101-113, 2004.- [55] G. Sabidussi, "The Centrality Index of a Graph,"
Psychometrika, vol. 31, pp. 581-603, 1966.- [56] M. Newman,
The Mathematics of Networks, second ed., S.N. Durlauf and L.E. Blume, eds. Palgrave Macmillan Pubblisher, 2008.- [57] M. Girvan and M. Newman, "Community Structure in Social and Biological Networks,"
Proc. Nat'l Academy of Sciences USA, vol. 99, pp. 7821-26, 2002.- [58] Y. Ho et al., "Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry,"
Nature, vol. 415, no. 6868, pp. 180-183, 2002.- [59] A.M. Edwards, B. Kus, R. Jansen, D. Greenbaum, J. Greenblatt, and M. Gerstein, "Bridging Structural Biology and Genomics: Assessing Protein Interaction Data with Known Complexes,"
Trends in Genetics, vol. 18, pp. 529-536, 2002.- [60] E. Estrada, "Virtual Identification of Essential Proteins within the Protein Interaction Network of Yeast,"
Proteomics, vol. 6, no. 1, pp. 35-40, 2005.- [61] G. Bader and C. Hogue, "An Automated Method for Finding Molecular Complexes in Large Protein Interaction Networks,"
BMC Bioinformatics, vol. 4, no. 2, pp. 380-389, 2003.- [62] F. Jordán and I. Scheuring, "Searching for Keystones in Ecological Networks,"
Oikos, vol. 99, pp. 607-612, 2002.- [63] M. Scotti, J. Podáni, and F. Jordán, "Weighting, Scale Dependence and Indirect Effects in Ecological Networks: A Comparative Study,"
Ecological Complexity, vol. 4, pp. 148-149, 2007.- [64] F. Jordán, W.-c. Liu, and J. Davis, "Topological Keystone Species: Measures of Positional Importance in Food Webs,"
Oikos, vol. 112, pp. 535-546, 2006.- [65] J. Elf, A. Doncic, and M. Ehrenberg, "Mesoscopic Reaction-Diffusion in Intracellular Signaling,"
Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems, S.M. Bezrukov, H. Frauenfelder, and F. Moss, eds., pp. 114-124, SPIE, http://dx.doi.org/10.111712.497009, July 2003.- [66] D. Bernstein, "Simulating Mesoscopic Reaction-Diffusion Systems Using the Gillespie Algorithm,"
Physical Review E, vol. 71, no. 4, p. 041103, Apr. 2005.- [67] J.V. Rodríguez, J.A. Kaandorp, M. Dobrzynski, and J.G. Blom, "Spatial Stochastic Modelling of the Phosphoenolpyruvate-Dependent Phosphotransferase (pts) Pathway in Escherichia Coli,"
Bioinformatics, vol. 22, no. 15, pp. 1895-1901, 2006.- [68] S. Lampoudi, D.T. Gillespie, and L.R. Petzold, "The Multinomial Simulation Algorithm for Discrete Stochastic Simulation of Reaction-Diffusion Systems,"
The J. Chemical Physics, vol. 130, no. 9, pp. 094104-094116, http://dx.doi.org/10.10631.3074302, Mar. 2009.- [69] M. Vigelius, A. Lane, and B. Meyer, "Accelerating Reaction-Diffusion Simulations with General-Purpose Graphics Processing Units,"
Bioinformatics, vol. 27, pp. 288-290, 2011.- [70] V. Volterra, "Variazioni e Fluttuazioni del Numero di Individui in Specie Animali Conviventi,"
Memorie dell'Acadmeia Regia Nazionale dei Lincei, vol. 2, pp. 31-113, 1926.- [71] A. Lotka,
Elements of Physical Biology. Williams & Wilkins Company, 1925.- [72] R. Battiti and A. Bertossi, "Differential Greedy for the 0-1 Equicut Problem,"
Proc. DIMACS Workshop Network Design: Connectivity and Facilities Location, pp. 3-22, 1997.- [73] R.B. Schinazi, "Predator-Prey and Host-Parasite Spatial Stochastic Models,"
The Annals of Applied Probability, vol. 7, no. 1, pp. 1-9, 1997.- [74] M. Forlin, T. Mazza, and D. Prandi, "Predicting the Effects of Parameters Changes in Stochastic Models through Parallel Synthetic Experiments and Multivariate Analysis,"
Proc. Ninth Int'l Workshop Parallel and Distributed Methods in Verification and Second Int'l Workshop High Performance Computational Systems Biology, pp. 105-115, 2010.- [75] C. Li, M. Donizelli, N. Rodriguez, H. Dharuri, L. Endler, V. Chelliah, L. Li, E. He, A. Henry, M.I. Stefan, J.L. Snoep, M. Hucka, N. Le Novère, and C. Laibe, "BioModels Database: An Enhanced, Curated and Annotated Resource for Published Quantitative Kinetic Models,"
BMC Systems Biology, vol. 4, article 92, June 2010.- [76] W. Marwan, "Theory of Time-Resolved Somatic Complementation and Its Use to Explore the Sporulation Control Network in Physarum Polycephalum,"
Genetics, vol. 164, no. 1, pp. 105-15, 2003.- [77] T. Lopes, T. Luganskaja, M.V. Spasic, M. Hentze, M. Muckenthaler, K. Schumann, and J. Reich, "Systems Analysis of Iron Metabolism: The Network of Iron Pools and Fluxes,"
BMC Systems Biology, vol. 4, no. 1, article 112, 2010. |