Ilias Tagkopoulos

2019

Ameen Eetemadi and Ilias Tagkopoulos (2019): Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships, Bioinformatics, Oxford University Press, Vol 35, Num 13, pp2226-2234
Navneet Rai, Linh Huynh, Minseung Kim, and Ilias Tagkopoulos (2019): Population collapse and adaptive rescue during long-term chemostat fermentation, Biotechnology and Bioengineering, John Wiley & Sons, Vol 116, Num 3, pp693-703

2017

Minseung Kim, Ameen Eetemadi, and Ilias Tagkopoulos (2017): DeepPep: Deep Proteome Inference from Peptide Profiles, PLOS Computational Biology, Public Library of Science (PLoS), Vol 13, Num 9, ppe1005661

2016

Minseung Kim, Navneet Rai, Violeta Zorraquino, and Ilias Tagkopoulos (2016): Multi-Omics Integration Accurately Predicts Cellular State in Unexplored Conditions for Escherichia Coli, Nature Communications, Springer Nature, Vol 7, pp13090

2015

Minseung Kim, Violeta Zorraquino, and Ilias Tagkopoulos (2015): Microbial Forensics: Predicting Phenotypic Characteristics and Environmental Conditions from Large-Scale Gene Expression Profiles, PLoS Computational Biology, PLoS, Vol 11, Num 3, ppe1004127

2013

Andreas Pavlogiannis, Vadim Mozhayskiy, and Ilias Tagkopoulos (2013): A Flood-Based Information Flow Analysis and Network Minimization Method for Gene Regulatory Networks, BMC Bioinformatics, Springer Nature, Vol 14, Num 1, pp137
Vadim Mozhayskiy and Ilias Tagkopoulos (2013): Microbial Evolution in Vivo and in Silico: Methods and Applications, Integrative Biology, Royal Society of Chemistry, Vol 5, Num 2, pp262-277

2012

Vadim Mozhayskiy, and Ilias Tagkopoulos (2012): Horizontal Gene Transfer Dynamics and Distribution of Fitness Effects During Microbial in Silico Evolution, BMC Bioinformatics, Springer Nature, Vol 13, Num Suppl 10, ppS13
Vadim Mozhayskiy and Ilias Tagkopoulos (2012): Guided Evolution of in Silico Microbial Populations in Complex Environments Accelerates Evolutionary Rates Through a Step-Wise Adaptation, BMC Bioinformatics, Springer Nature, Vol 13, Num Suppl 10, ppS10

2011

Vadim Mozhayskiy and Ilias Tagkopoulos (2011): In Silico Evolution of Multi-Scale Microbial Systems in the Presence of Mobile Genetic Elements and Horizontal Gene Transfer, Springer Science + Business Media, Bioinformatics Research and Applications, Vol 7th International Symposium on Bioinformatics Research and Applications (ISBRA 2011), pp262-273, Changsha, China
Vadim Mozhayskiy, Bob Miller, Kwan-Liu Ma, and Ilias Tagkopoulos (2011): A Scalable Multi-Scale Framework for Parallel Simulation and Visualization of Microbial Evolution, Association for Computing Machinery, Proceedings of the 2011 TeraGrid Conference on Extreme Digital Discovery (TG '11), pp7:1-7:8, Salt Lake City, Utah, U.S.A.

2018

2017

Ilias Tagkopoulos (2017): A Crystal Ball of Bacterial Behavior: From Data to Prediction Using Genome-Scale Models, 2017 Blue Waters Annual Report, pp246-247

2016

Ilias Tagkopoulos (2016): Big Data on Small Organisms: Genome-Scale Modeling and Phenotypic Prediction of Escherichia Coli, 2016 Blue Waters Annual Report, pp244-245

Blue Waters research integrates data to predict E. coli behavior


Nov 9, 2016

Before you savor the first bite of that delicious snack you have tucked in your bag to get you through the mid-afternoon slump, take a moment to thank Ilias Tagkopoulos—it's researchers like him that help keep your food safe from nasty bacteria. A computer scientist by trade, Tagkopoulos runs an experimental microbiology lab at University of California-Davis. He has been using the Blue Waters supercomputer to predict the cellular behavior of E. coli. The findings were recently published in Nature Communications. "The level of integration in advanced machine learning, high performance computing and experimental microbiology is something rare," he says. "It's really an interdisciplinary project that could not be done by just one discipline."


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Supercomputers create E. coli ‘crystal ball’


Oct 28, 2016

Predicting the reactions of living cells—huge numbers of genes, proteins, and enzymes, embedded in complex pathways and feedback loops—is a challenging task. Yet researchers are attempting just that, by building a computer model that predicts the behavior of a single cell of the bacterium Escherichia coli. The new simulation is the largest of its kind yet, says Ilias Tagkopoulos, professor of computer science at the University of California, Davis. “The number of layers, and the amount of data involved are unprecedented.”


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PRAC winners represent a range of disciplines


Apr 21, 2010

Petascale Computing Resource Allocations (PRAC awards) from the National Science Foundation allow research teams to work closely with the Blue Waters project team in preparing their codes. The codes and projects address key challenges faced by our society and explore fundamental scientific and engineering problems. These multiyear collaborations include help porting and re-engineering existing applications. In some cases, the teams will build entirely new applications based on new programming models. Current projects—18 representing about 30 institutions—represent a wide range of scientific disciplines. They will drive scientific discovery for years to come.


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How will Blue Waters benefit science?


Jan 13, 2010

The Blue Waters supercomputer is one of the most powerful supercomputers in the world for open scientific research when it comes online at Illinois in 2011. How will scientists and engineers across the country use this tremendous resource? How will their research be advanced by a supercomputer that can do 1 quadrillion calculations every second? Many scientists are working now with the Blue Waters team so they are ready to use the massive sustained-petaflop supercomputer when it comes online in 2011. These teams will use Blue Waters to improve our understanding of everything from the Earth's climate to earthquakes.


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