This award is for a provisional allocation of time on the Blue Waters computer system, due to become operational in 2011, and for travel funds to support technical coordination by various collaborators with the Blue Waters project team and vendor technical team.
The project involves studies in bacterial ecology, gene regulatory networks and intracellular biochemical networks with a view to understanding the way in which populations of unicellular organisms evolve and adapt as their environment changes. This involves multi-scale biological systems where processes ranging from gene expression and intracellular biochemistry to ecosystem dynamics are in play. A key aim is to understand how, by considering the genetic and biochemical processes within a cell, unicellular organisms evolve to develop adaptive responses to recurring changes in their environments. The study will look at the influence of parameters such as nutrient concentrations and mutation on adaptation, compare the efficacy of different strategies for survival in static and fluctuating environments, and examine how unicellular organisms internalize the correlation structure of their environment by modifying their internal networks to facilitate such changes. The role of genetic and molecular information transfer processes will also be studied.
The modeling approach has been developed and previously used for scientific research using an implementation on contemporary computing systems. It is inherently multi-scale, including representations of molecular processes within cells and scaling up to ecosystems of unicellular organisms. In prior work, the limitations of computer power have necessitated a number of simplifying assumptions. It is anticipated that the use of Blue Waters will allow some of these to be relaxed, so that more biological processes can be included in the simulations. Results will be tested against the outcomes of in vivo experiments in the UC Davis Genome Center.
The principal investigator is a recent Ph.D. Progress in this research area could find application in bioengineering and biotechnology.
One of the central challenges in computational biology is the development of predictive multi-scale models that can capture the diverse layers of cellular organization. Even more scarce are models that encode biophysical phenomena together with evolutionary forces, in order to provide insight on the effect of adaptation at a systems-level. Over the last five years, our lab has created a multi-scale abstract microbial evolution model that unifies various layers, from diverse molecular species and networks to organism and population-level properties. With the help of Blue Waters and the NCSA team, we are able to scale up to hundreds of thousand cells, an unprecedented scale of simulation that is, however, short of the billions of cells that are present in a bacterial colony. Here, we present our scalability results, the methods that we employed to achieve them and our current work on a data-driven, genome-scale, population-level model for Escherichia coli. Challenges There are a number of challenges that have to be addressed to achieve such feat. First, a model of biological organization that is both biological realistic and computationally feasible is paramount, incorporating the right level of biological abstraction. Second, the spatial and temporal scales of a model that encompasses genes, proteins, networks, cells and populations are very diverse, which create additional hurdles when applying numerical methods to solve them. Third, since evolution is based on random mutations and natural selection, it is inherently hard to predict and can lead to imbalances in the distribution of active cells, and in its extension, computational tasks. Fourth, a typical microbial colony has billions of cells, while current simulations are at most in the thousands, which leads to size-specific artifacts as size does matter. Finally, storing and visualizing the fossil record of an evolutionary trajectory, especially since dozens of them are needed for assessing statistical significance for any hypothesis-testing experiment, is not an easy task, as a simulation can lead to terabytes of complex data for analysis. Accomplishments We have created the Evolution in Variable Environments (EVE) v3.0 synthetic ecology framework with a capacity to scale up to 8,000 MPI processes and 128,000 organisms. To compare, our previous work scaled up to 200 organisms (1). To cope with unforeseen computational load due to the emergence of complex phenotypes, we have developed both static and adaptive load balancers (TG’11 Best Paper Award) that can account for both fixed and non-fixed population sizes (2-3). We developed intuitive visualization tools (4), HDF5 storage solutions, and novel analysis algorithms based on network flows (5) to efficiently project data to accelerate biological discovery. The EVE simulator has since been used to investigate the effect of horizontal gene transfer (6), distribution of fitness effects and the hypothesis of accelerated evolution through guided, step-wise adaptation (7) with interesting results that drive biological experimentation (8-9). Future work includes pushing the limits of microbial simulations to break the million-cell barrier, parallelization of organism-specific, data-driven models that integrate omics layers, starting from our recent work in the model bacterium Escherichia coli (10) and integration with Synthetic Biology computer-aided design tools (11). References 1. I.Tagkopoulos, Y.Liu, S. Tavazoie, "Predictive Behavior Within Microbial Genetic Networks", Science, 320:1313-7, 2008 2. V. Mozhayskiy, I.Tagkopoulos, "In silico Evolution of Multi-scale Microbial Systems in the Presence of Mobile Genetic Elements and Horizontal Gene Transfer", ISBRA2011, Lecture Notes in Bioinformatics, LNBI 6674, pp.262-273, Springer, 2011 3. V. Mozhayskiy, R. Miller, KL. Ma, I.Tagkopoulos, "A Scalable Multi-scale Framework for Parallel Simulation and Visualization of Microbial Evolution", TeraGrid2011; Salt Lake City, Utah, 2011, DOI:10.1145/2016741.2016749 4. R. Miller, V.Mozhayskiy, I.Tagkopoulos, KL. Ma, "EVEVis: A Multi-Scale Visualization System for Dense Evolutionary Data", 1st IEEE Symposium on Biological Data Visualization, pp. 143-150, Rhode Island, 2011 5. A. Pavlogiannis, V. Mozhayskiy, I. Tagkopoulos, "A flood-based information flow analysis and network minimization method for bacterial systems", 14:137 DOI:10.1186/1471-2105-14-137, BMC Bioinformatics, 2013 6. V.Mozhayskiy, I.Tagkopoulos, "Horizontal gene transfer dynamics and distribution of fitness effects during microbial In silico Evolution", doi: 10.1186/1471-2105-13-S10-S13, 13:S13, BMC Bioinformatics, 2012 7. V.Mozhayskiy, I.Tagkopoulos, "Guided evolution of in silico microbial populations in complex environments accelerates evolutionary rates through a step-wise adaptation", 13:S10, BMC Bioinformatics, 2012 8. V. Mozhayskiy, I. Tagkopoulos, "Microbial evolution in vivo and in silico: methods and applications", DOI:10.1039/C2IB20095C, 5(2):262-77, Integrative Biology, 2012 9. M. Dragosits, V. Mozhayskiy, S. Quinones-Soto, J. Park, I.Tagkopoulos, "Evolutionary potential,cross-stress behavior, and the genetic basis of acquired stress resistance in Escherichia coli", doi:10.1038/msb.2012.76, 9:643, Molecular Systems Biology, 2013 10. J. Carrera, R.E. Curado, J. Luo, N. Rai, A. Tsoukalas, I. Tagkopoulos, "An integrative, multi-layer, genome-scale model reveals the phenotypic landscape of Escherichia coli", 10(7):735, Molecular Systems Biology, 2014 11. L. Huynh, A. Tsoukalas, M. Köppe, I.Tagkopoulos, "SBROME: A scalable optimization and module matching framework for automated biosystem design", DOI: 10.1021/sb300095m, pp 263-273, ACS Synthetic Biology, 2013
