Abstract
Bacteria are some of the most ubiquitous, simple and fastest evolving life forms in the planet, yet even in their case, evolution is painstakingly difficult to trace in a laboratory setting. However, evolution of microorganisms in controlled and/or accelerated settings is crucial to advance our understanding on how various behavioral patterns emerge, or to engineer new strains with desired proprieties (e.g. resilient strains for recombinant protein or bio-fuels production). We present a microbial evolution simulator, a tool to study and analyze hypotheses regarding microbial evolution dynamics. The simulator employs multi-scale models and data structures that capture a whole ecology of interactions between the environment, populations, organisms, and their respective gene regulatory and biochemical networks. For each time point, the evolutionary "fossil record" is recorded in each run. This dataset (stored in HDF5 format for scalability) includes all environmental and cellular parameters, cellular (division, death) and evolutionary events (mutations, Horizontal Gene Transfer). This leads to the creation of a coherent dataset that could not have been obtained experimentally. To efficiently analyze it, we have developed a novel visualization tool that projects information in multiple levels (population, phylogeny, networks, and phenotypes). Additionally, we present some of the unique insights in microbial evolution that were possible through simulations in TeraGrid, and we describe further steps to address scalability issues for populations beyond 32,000 cells.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the TeraGrid 2011 Conference |
| Subtitle of host publication | Extreme Digital Discovery, TG'11 |
| DOIs | |
| State | Published - Sep 7 2011 |
| Event | TeraGrid 2011 Conference: Extreme Digital Discovery, TG'11 - Salt Lake City, UT, United States Duration: Jul 18 2011 → Jul 21 2011 |
Other
| Other | TeraGrid 2011 Conference: Extreme Digital Discovery, TG'11 |
|---|---|
| Country | United States |
| City | Salt Lake City, UT |
| Period | 7/18/11 → 7/21/11 |
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Keywords
- biological networks
- high performance computing
- microbial evolution
- multi-scale modeling
- simulation
- visualization
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
Cite this
A scalable multi-scale framework for parallel simulation and visualization of microbial evolution. / Mozhayskiy, Vadim; Miller, Bob; Ma, Kwan-Liu; Tagkopoulos, Ilias.
Proceedings of the TeraGrid 2011 Conference: Extreme Digital Discovery, TG'11. 2011.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A scalable multi-scale framework for parallel simulation and visualization of microbial evolution
AU - Mozhayskiy, Vadim
AU - Miller, Bob
AU - Ma, Kwan-Liu
AU - Tagkopoulos, Ilias
PY - 2011/9/7
Y1 - 2011/9/7
N2 - Bacteria are some of the most ubiquitous, simple and fastest evolving life forms in the planet, yet even in their case, evolution is painstakingly difficult to trace in a laboratory setting. However, evolution of microorganisms in controlled and/or accelerated settings is crucial to advance our understanding on how various behavioral patterns emerge, or to engineer new strains with desired proprieties (e.g. resilient strains for recombinant protein or bio-fuels production). We present a microbial evolution simulator, a tool to study and analyze hypotheses regarding microbial evolution dynamics. The simulator employs multi-scale models and data structures that capture a whole ecology of interactions between the environment, populations, organisms, and their respective gene regulatory and biochemical networks. For each time point, the evolutionary "fossil record" is recorded in each run. This dataset (stored in HDF5 format for scalability) includes all environmental and cellular parameters, cellular (division, death) and evolutionary events (mutations, Horizontal Gene Transfer). This leads to the creation of a coherent dataset that could not have been obtained experimentally. To efficiently analyze it, we have developed a novel visualization tool that projects information in multiple levels (population, phylogeny, networks, and phenotypes). Additionally, we present some of the unique insights in microbial evolution that were possible through simulations in TeraGrid, and we describe further steps to address scalability issues for populations beyond 32,000 cells.
AB - Bacteria are some of the most ubiquitous, simple and fastest evolving life forms in the planet, yet even in their case, evolution is painstakingly difficult to trace in a laboratory setting. However, evolution of microorganisms in controlled and/or accelerated settings is crucial to advance our understanding on how various behavioral patterns emerge, or to engineer new strains with desired proprieties (e.g. resilient strains for recombinant protein or bio-fuels production). We present a microbial evolution simulator, a tool to study and analyze hypotheses regarding microbial evolution dynamics. The simulator employs multi-scale models and data structures that capture a whole ecology of interactions between the environment, populations, organisms, and their respective gene regulatory and biochemical networks. For each time point, the evolutionary "fossil record" is recorded in each run. This dataset (stored in HDF5 format for scalability) includes all environmental and cellular parameters, cellular (division, death) and evolutionary events (mutations, Horizontal Gene Transfer). This leads to the creation of a coherent dataset that could not have been obtained experimentally. To efficiently analyze it, we have developed a novel visualization tool that projects information in multiple levels (population, phylogeny, networks, and phenotypes). Additionally, we present some of the unique insights in microbial evolution that were possible through simulations in TeraGrid, and we describe further steps to address scalability issues for populations beyond 32,000 cells.
KW - biological networks
KW - high performance computing
KW - microbial evolution
KW - multi-scale modeling
KW - simulation
KW - visualization
UR - http://www.scopus.com/inward/record.url?scp=80052330079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052330079&partnerID=8YFLogxK
U2 - 10.1145/2016741.2016749
DO - 10.1145/2016741.2016749
M3 - Conference contribution
AN - SCOPUS:80052330079
SN - 9781450308885
BT - Proceedings of the TeraGrid 2011 Conference
ER -