A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

Costello, Zak; Martin, Hector Garcia

doi:10.1038/s41540-018-0054-3

Title: A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

Journal Article · Tue May 29 00:00:00 EDT 2018 · npj Systems Biology and Applications

DOI:https://doi.org/10.1038/s41540-018-0054-3· OSTI ID:1510759

Costello, Zak ^[1]; Martin, Hector Garcia ^[2]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Basque Center for Applied Mathematics (BCAM), Bilbao (Spain)

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1510759

Journal Information:: npj Systems Biology and Applications, Vol. 4, Issue 1; ISSN 2056-7189

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 99 works

Citation information provided by
Web of Science

References (83)

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes Heintz-Buschart, Anna; May, Patrick; Laczny, Cédric C. Nature Microbiology, Vol. 2, Issue 1 https://doi.org/10.1038/nmicrobiol.2016.180	journal	October 2016
Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering Alonso-Gutierrez, Jorge; Kim, Eun-Mi; Batth, Tanveer S. Metabolic Engineering, Vol. 28 https://doi.org/10.1016/j.ymben.2014.11.011	journal	March 2015
Ensemble Modeling of Metabolic Networks Tran, Linh M.; Rizk, Matthew L.; Liao, James C. Biophysical Journal, Vol. 95, Issue 12 https://doi.org/10.1529/biophysj.108.135442	journal	December 2008
Clostridium thermocellum ATCC27405 transcriptomic, metabolomic and proteomic profiles after ethanol stress Yang, Shihui; Giannone, Richard J.; Dice, Lezlee BMC Genomics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2164-13-336	journal	January 2012
Genetical Implications of the Structure of Deoxyribonucleic Acid Watson, J. D.; Crick, F. H. C. Nature, Vol. 171, Issue 4361 https://doi.org/10.1038/171964b0	journal	May 1953
Approximative kinetic formats used in metabolic network modeling Heijnen, Joseph J. Biotechnology and Bioengineering, Vol. 91, Issue 5 https://doi.org/10.1002/bit.20558	journal	January 2005
Private traits and attributes are predictable from digital records of human behavior Kosinski, M.; Stillwell, D.; Graepel, T. Proceedings of the National Academy of Sciences, Vol. 110, Issue 15 https://doi.org/10.1073/pnas.1218772110	journal	March 2013
Synthetic biology in mammalian cells: next generation research tools and therapeutics Lienert, Florian; Lohmueller, Jason J.; Garg, Abhishek Nature Reviews Molecular Cell Biology, Vol. 15, Issue 2 https://doi.org/10.1038/nrm3738	journal	January 2014
Metabolic ensemble modeling for strain engineers Tan, Yikun; Liao, James C. Biotechnology Journal, Vol. 7, Issue 3 https://doi.org/10.1002/biot.201100186	journal	October 2011
Mastering the game of Go with deep neural networks and tree search Silver, David; Huang, Aja; Maddison, Chris J. Nature, Vol. 529, Issue 7587 https://doi.org/10.1038/nature16961	journal	January 2016
The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels Daran-Lapujade, P.; Rossell, S.; van Gulik, W. M. Proceedings of the National Academy of Sciences, Vol. 104, Issue 40 https://doi.org/10.1073/pnas.0707476104	journal	September 2007
Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism Doerfler, Hannes; Lyon, David; Nägele, Thomas Metabolomics, Vol. 9, Issue 3 https://doi.org/10.1007/s11306-012-0470-0	journal	October 2012
CircadiOmics: integrating circadian genomics, transcriptomics, proteomics and metabolomics Patel, Vishal R.; Eckel-Mahan, Kristin; Sassone-Corsi, Paolo Nature Methods, Vol. 9, Issue 8 https://doi.org/10.1038/nmeth.2111	journal	July 2012
A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data Khodayari, Ali; Zomorrodi, Ali R.; Liao, James C. Metabolic Engineering, Vol. 25 https://doi.org/10.1016/j.ymben.2014.05.014	journal	September 2014
Systems-level analysis of mechanisms regulating yeast metabolic flux Hackett, S. R.; Zanotelli, V. R. T.; Xu, W. Science, Vol. 354, Issue 6311 https://doi.org/10.1126/science.aaf2786	journal	October 2016
A Whole-Cell Computational Model Predicts Phenotype from Genotype Karr, Jonathan R.; Sanghvi, Jayodita C.; Macklin, Derek N. Cell, Vol. 150, Issue 2 https://doi.org/10.1016/j.cell.2012.05.044	journal	July 2012
Life, simulated de Souza, Natalie Nature Methods, Vol. 9, Issue 9 https://doi.org/10.1038/nmeth.2165	journal	August 2012
Systems metabolic engineering of microorganisms for natural and non-natural chemicals Lee, Jeong Wook; Na, Dokyun; Park, Jong Myoung Nature Chemical Biology, Vol. 8, Issue 6 https://doi.org/10.1038/nchembio.970	journal	May 2012
Real-Time Digitization of Metabolomics Patterns from a Living System Using Mass Spectrometry Heinemann, Joshua; Noon, Brigit; Mohigmi, Mohammad J. Journal of The American Society for Mass Spectrometry, Vol. 25, Issue 10 https://doi.org/10.1007/s13361-014-0922-z	journal	July 2014
Toward robotic cars Thrun, Sebastian Communications of the ACM, Vol. 53, Issue 4 https://doi.org/10.1145/1721654.1721679	journal	April 2010
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning Alipanahi, Babak; Delong, Andrew; Weirauch, Matthew T. Nature Biotechnology, Vol. 33, Issue 8 https://doi.org/10.1038/nbt.3300	journal	July 2015
Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage Muller, Emilie E. L.; Pinel, Nicolás; Laczny, Cédric C. Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms6603	journal	November 2014
Improving Fatty Acid Availability for Bio-Hydrocarbon Production in Escherichia coli by Metabolic Engineering Lin, Fengming; Chen, Yu; Levine, Robert PLoS ONE, Vol. 8, Issue 10 https://doi.org/10.1371/journal.pone.0078595	journal	October 2013
A microfluidics-based in vitro model of the gastrointestinal human–microbe interface Shah, Pranjul; Fritz, Joëlle V.; Glaab, Enrico Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms11535	journal	May 2016
Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow Brunk, Elizabeth; George, Kevin W.; Alonso-Gutierrez, Jorge Cell Systems, Vol. 2, Issue 5 https://doi.org/10.1016/j.cels.2016.04.004	journal	May 2016
Acyl-CoA synthetases: fatty acid uptake and metabolic channeling Digel, Margarete; Ehehalt, Robert; Stremmel, Wolfgang Molecular and Cellular Biochemistry, Vol. 326, Issue 1-2 https://doi.org/10.1007/s11010-008-0003-3	journal	December 2008
Metabolic engineering of Saccharomyces cerevisiae for the production of triacetic acid lactone Cardenas, Javier; Da Silva, Nancy A. Metabolic Engineering, Vol. 25 https://doi.org/10.1016/j.ymben.2014.07.008	journal	September 2014
Integrated omics for the identification of key functionalities in biological wastewater treatment microbial communities: Integrated omics to discover microbial functions Narayanasamy, Shaman; Muller, Emilie E. L.; Sheik, Abdul R. Microbial Biotechnology, Vol. 8, Issue 3 https://doi.org/10.1111/1751-7915.12255	journal	February 2015
Synthetic biology devices for in vitro and in vivo diagnostics Slomovic, Shimyn; Pardee, Keith; Collins, James J. Proceedings of the National Academy of Sciences, Vol. 112, Issue 47 https://doi.org/10.1073/pnas.1508521112	journal	November 2015
Synthetic biology: from hype to impact Gardner, Timothy S. Trends in Biotechnology, Vol. 31, Issue 3 https://doi.org/10.1016/j.tibtech.2013.01.018	journal	March 2013
Biofuel alternatives to ethanol: pumping the microbial well Fortman, J. L.; Chhabra, Swapnil; Mukhopadhyay, Aindrila Trends in Biotechnology, Vol. 26, Issue 7, p. 375-381 https://doi.org/10.1016/j.tibtech.2008.03.008	journal	July 2008
Erratum: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks Esteva, Andre; Kuprel, Brett; Novoa, Roberto A. Nature, Vol. 546, Issue 7660 https://doi.org/10.1038/nature22985	journal	June 2017
Thermodynamics-Based Metabolic Flux Analysis Henry, Christopher S.; Broadbelt, Linda J.; Hatzimanikatis, Vassily Biophysical Journal, Vol. 92, Issue 5 https://doi.org/10.1529/biophysj.106.093138	journal	March 2007
Pathway Thermodynamics Highlights Kinetic Obstacles in Central Metabolism Noor, Elad; Bar-Even, Arren; Flamholz, Avi PLoS Computational Biology, Vol. 10, Issue 2 https://doi.org/10.1371/journal.pcbi.1003483	journal	February 2014
Ensemble Modeling for Aromatic Production in Escherichia coli Rizk, Matthew L.; Liao, James C.; Langowski, Jörg PLoS ONE, Vol. 4, Issue 9, Article No. e6903 https://doi.org/10.1371/journal.pone.0006903	journal	September 2009
Real-time metabolome profiling of the metabolic switch between starvation and growth Link, Hannes; Fuhrer, Tobias; Gerosa, Luca Nature Methods, Vol. 12, Issue 11 https://doi.org/10.1038/nmeth.3584	journal	September 2015
Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA Xu, Peng; Ranganathan, Sridhar; Fowler, Zachary L. Metabolic Engineering, Vol. 13, Issue 5 https://doi.org/10.1016/j.ymben.2011.06.008	journal	September 2011
Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction O'Brien, Edward J.; Lerman, Joshua A.; Chang, Roger L. Molecular Systems Biology, Vol. 9, Issue 1 https://doi.org/10.1038/msb.2013.52	journal	January 2013
Multiple High-Throughput Analyses Monitor the Response of E. coli to Perturbations Ishii, N.; Nakahigashi, K.; Baba, T. Science, Vol. 316, Issue 5824 https://doi.org/10.1126/science.1132067	journal	April 2007
A Method to Constrain Genome-Scale Models with 13C Labeling Data García Martín, Héctor; Kumar, Vinay Satish; Weaver, Daniel PLOS Computational Biology, Vol. 11, Issue 9 https://doi.org/10.1371/journal.pcbi.1004363	journal	September 2015
The dynamic microbiome Gerber, Georg K. FEBS Letters, Vol. 588, Issue 22 https://doi.org/10.1016/j.febslet.2014.02.037	journal	February 2014
A Survey on Transfer Learning Pan, Sinno Jialin; Yang, Qiang IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10 https://doi.org/10.1109/TKDE.2009.191	journal	October 2010
High-throughput discovery metabolomics Fuhrer, Tobias; Zamboni, Nicola Current Opinion in Biotechnology, Vol. 31 https://doi.org/10.1016/j.copbio.2014.08.006	journal	February 2015
A method for analysis and design of metabolism using metabolomics data and kinetic models: Application on lipidomics using a novel kinetic model of sphingolipid metabolism Savoglidis, Georgios; da Silveira dos Santos, Aline Xavier; Riezman, Isabelle Metabolic Engineering, Vol. 37 https://doi.org/10.1016/j.ymben.2016.04.002	journal	September 2016
Reverse engineering and identification in systems biology: strategies, perspectives and challenges Villaverde, Alejandro F.; Banga, Julio R. Journal of The Royal Society Interface, Vol. 11, Issue 91 https://doi.org/10.1098/rsif.2013.0505	journal	February 2014
Synthetic Biology Moving into the Clinic Ruder, W. C.; Lu, T.; Collins, J. J. Science, Vol. 333, Issue 6047 https://doi.org/10.1126/science.1206843	journal	September 2011
Ensemble modeling for strain development of l-lysine-producing Escherichia coli Contador, Carolina A.; Rizk, Matthew L.; Asenjo, Juan A. Metabolic Engineering, Vol. 11, Issue 4-5, p. 221-233 https://doi.org/10.1016/j.ymben.2009.04.002	journal	July 2009
Dermatologist-level classification of skin cancer with deep neural networks Esteva, Andre; Kuprel, Brett; Novoa, Roberto A. Nature, Vol. 542, Issue 7639 https://doi.org/10.1038/nature21056	journal	January 2017
The new frontier of genome engineering with CRISPR-Cas9 Doudna, Jennifer A.; Charpentier, Emmanuelle Science, Vol. 346, Issue 6213 https://doi.org/10.1126/science.1258096	journal	November 2014
Circadian Metabolomics in Time and Space Dyar, Kenneth A. Frontiers in Neuroscience, Vol. 11 https://doi.org/10.3389/fnins.2017.00369	journal	January 2017
Natural products as biofuels and bio-based chemicals: fatty acids and isoprenoids Beller, Harry R.; Lee, Taek Soon; Katz, Leonard Natural Product Reports, Vol. 32, Issue 10 https://doi.org/10.1039/C5NP00068H	journal	January 2015
A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains Khodayari, Ali; Maranas, Costas D. Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms13806	journal	December 2016
Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints Chakrabarti, Anirikh; Miskovic, Ljubisa; Soh, Keng Cher Biotechnology Journal, Vol. 8, Issue 9 https://doi.org/10.1002/biot.201300091	journal	August 2013
A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins Batth, Tanveer S.; Singh, Pragya; Ramakrishnan, Vikram R. Metabolic Engineering, Vol. 26 https://doi.org/10.1016/j.ymben.2014.08.004	journal	November 2014
Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations Costa, Rafael S.; Machado, Daniel; Rocha, Isabel Biosystems, Vol. 100, Issue 2 https://doi.org/10.1016/j.biosystems.2010.03.001	journal	May 2010
From the first drop to the first truckload: commercialization of microbial processes for renewable chemicals Van Dien, Stephen Current Opinion in Biotechnology, Vol. 24, Issue 6 https://doi.org/10.1016/j.copbio.2013.03.002	journal	December 2013
Ready or Not, Here We Go Dyster, Timothy; Sheth, Sameer A.; McKhann, Guy M. Neurosurgery, Vol. 78, Issue 6 https://doi.org/10.1227/01.neu.0000484053.82181.f6	journal	June 2016
13C Metabolic Flux Analysis Wiechert, Wolfgang Metabolic Engineering, Vol. 3, Issue 3 https://doi.org/10.1006/mben.2001.0187	journal	July 2001
Erratum: Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes Heintz-Buschart, Anna; May, Patrick; Laczny, Cédric C. Nature Microbiology, Vol. 2, Issue 1 https://doi.org/10.1038/nmicrobiol.2016.227	journal	October 2016
Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data Gerosa, Luca; Haverkorn van Rijsewijk, Bart R. B.; Christodoulou, Dimitris Cell Systems, Vol. 1, Issue 4 https://doi.org/10.1016/j.cels.2015.09.008	journal	October 2015
Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Savitzky, Abraham.; Golay, M. J. E. Analytical Chemistry, Vol. 36, Issue 8 https://doi.org/10.1021/ac60214a047	journal	July 1964
Channeling in native microbial pathways: Implications and challenges for metabolic engineering Abernathy, Mary H.; He, Lian; Tang, Yinjie J. Biotechnology Advances, Vol. 35, Issue 6 https://doi.org/10.1016/j.biotechadv.2017.06.004	journal	November 2017
Automatic discrimination of human hematopoietic tumor cell lines using a combination of imaging flow cytometry and convolutional neural network Matsuoka, Yoshikazu; Nakatsuka, Ryusuke; Fujioka, Tatsuya Human Cell, Vol. 34, Issue 3 https://doi.org/10.1007/s13577-021-00506-2	journal	February 2021
A wellness study of 108 individuals using personal, dense, dynamic data clouds Price, Nathan D.; Magis, Andrew T.; Earls, John C. Nature Biotechnology, Vol. 35, Issue 8 https://doi.org/10.1038/nbt.3870	journal	July 2017
Ensemble Modeling of Hepatic Fatty Acid Metabolism with a Synthetic Glyoxylate Shunt Dean, Jason T.; Rizk, Matthew L.; Tan, Yikun Biophysical Journal, Vol. 98, Issue 8 https://doi.org/10.1016/j.bpj.2009.12.4308	journal	April 2010
Special Report: The birth of biotechnology Russo, Eugene Nature, Vol. 421, Issue 6921 https://doi.org/10.1038/nj6921-456a	journal	January 2003
Deep learning for regulatory genomics Park, Yongjin; Kellis, Manolis Nature Biotechnology, Vol. 33, Issue 8 https://doi.org/10.1038/nbt.3313	journal	August 2015
The Regulation of Cellular Systems Heinrich, Reinhart; Schuster, Stefan https://doi.org/10.1007/978-1-4613-1161-4	book	January 1996
Dynamical effects of overparametrization in nonlinear models Aguirre, Luis Antonio; Billings, S. A. Physica D: Nonlinear Phenomena, Vol. 80, Issue 1-2 https://doi.org/10.1016/0167-2789(95)90053-5	journal	January 1995
Correlation analysis of targeted proteins and metabolites to assess and engineer microbial isopentenol production: Targeted Proteomics-Based Correlation Analysis George, Kevin W.; Chen, Amy; Jain, Aakriti Biotechnology and Bioengineering, Vol. 111, Issue 8 https://doi.org/10.1002/bit.25226	journal	May 2014
Promise of personalized omics to precision medicine Chen, Rui; Snyder, Michael WIREs Systems Biology and Medicine, Vol. 5, Issue 1 https://doi.org/10.1002/wsbm.1198	journal	November 2012
Big Data: Astronomical or Genomical? Stephens, Zachary D.; Lee, Skylar Y.; Faghri, Faraz PLOS Biology, Vol. 13, Issue 7 https://doi.org/10.1371/journal.pbio.1002195	journal	July 2015
Effects of spatiotemporal variations on metabolic control: Approximate analysis using (log)linear kinetic models Hatzimanikatis, Vassily; Bailey, James E. Biotechnology and Bioengineering, Vol. 54, Issue 2 https://doi.org/10.1002/(SICI)1097-0290(19970420)54:2<91::AID-BIT1>3.0.CO;2-Q	journal	April 1997
The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization Morrell, William C.; Birkel, Garrett W.; Forrer, Mark ACS Synthetic Biology, Vol. 6, Issue 12 https://doi.org/10.1021/acssynbio.7b00204	journal	September 2017
Integrated Proteomic and Metabolomic Analysis of an Artificial Microbial Community for Two-Step Production of Vitamin C Ma, Qian; Zhou, Jian; Zhang, Weiwen PLoS ONE, Vol. 6, Issue 10 https://doi.org/10.1371/journal.pone.0026108	journal	October 2011
General mass action kinetics Horn, F.; Jackson, R. Archive for Rational Mechanics and Analysis, Vol. 47, Issue 2 https://doi.org/10.1007/BF00251225	journal	January 1972
Synthetic and systems biology for microbial production of commodity chemicals Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J. npj Systems Biology and Applications, Vol. 2, Issue 1 https://doi.org/10.1038/npjsba.2016.9	journal	April 2016
Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods Lewis, Nathan E.; Nagarajan, Harish; Palsson, Bernhard O. Nature Reviews Microbiology, Vol. 10, Issue 4 https://doi.org/10.1038/nrmicro2737	journal	February 2012
Metabolic networks in motion: ¹³ C‐based flux analysis Sauer, Uwe Molecular Systems Biology, Vol. 2, Issue 1 https://doi.org/10.1038/msb4100109	journal	January 2006
13C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids Ghosh, Amit; Ando, David; Gin, Jennifer Frontiers in Bioengineering and Biotechnology, Vol. 4 https://doi.org/10.3389/fbioe.2016.00076	journal	October 2016
Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model Khodayari, Ali; Chowdhury, Anupam; Maranas, Costas D. Frontiers in Bioengineering and Biotechnology, Vol. 2 https://doi.org/10.3389/fbioe.2014.00076	journal	January 2015
Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism Matsuoka, Yu; Shimizu, Kazuyuki Bioresources and Bioprocessing, Vol. 2, Issue 1 https://doi.org/10.1186/s40643-014-0031-7	journal	February 2015
Metabolic engineering for the high-yield production of isoprenoid-based C5 alcohols in E. coli George, Kevin W.; Thompson, Mitchell G.; Kang, Aram Scientific Reports, Vol. 5, Issue 1 https://doi.org/10.1038/srep11128	journal	June 2015

Cited By (17)

The Application of Omics Technologies to Study Axon Regeneration and CNS Repair Tedeschi, Andrea; Popovich, Phillip G. F1000Research, Vol. 8 https://doi.org/10.12688/f1000research.17084.1	journal	January 2019
Machine Learning Applications for Mass Spectrometry-Based Metabolomics Liebal, Ulf W.; Phan, An N. T.; Sudhakar, Malvika Metabolites, Vol. 10, Issue 6 https://doi.org/10.3390/metabo10060243	journal	June 2020
Multiscale Modeling Meets Machine Learning: What Can We Learn? Peng, Grace C. Y.; Alber, Mark; Buganza Tepole, Adrian Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-020-09405-5	journal	February 2020
Volatomic pattern of breast cancer and cancer-free tissues as a powerful strategy to identify potential biomarkers Silva, Catarina; Perestrelo, Rosa; Silva, Pedro The Analyst, Vol. 144, Issue 14 https://doi.org/10.1039/c9an00263d	journal	January 2019
A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering Kim, Osvaldo D.; Rocha, Miguel; Maia, Paulo Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.01690	journal	July 2018
XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data Berg, Jordan A.; Belyeu, Jonathan R.; Morgan, Jeffrey T. PLOS Computational Biology, Vol. 16, Issue 1 https://doi.org/10.1371/journal.pcbi.1007625	journal	January 2020
Technical Advances to Accelerate Modular Type I Polyketide Synthase Engineering towards a Retro-biosynthetic Platform Pang, Bo; Valencia, Luis E.; Wang, Jessica Biotechnology and Bioprocess Engineering, Vol. 24, Issue 3 https://doi.org/10.1007/s12257-019-0083-9	journal	June 2019
Multiscale modeling meets machine learning: What can we learn? Peng, Grace C. Y.; Alber, Mark; Tepole, Adrian Buganza arXiv https://doi.org/10.48550/arxiv.1911.11958	preprint	January 2019
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences Alber, Mark; Buganza Tepole, Adrian; Cannon, William R. npj Digital Medicine, Vol. 2, Issue 1 https://doi.org/10.1038/s41746-019-0193-y	journal	November 2019
Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties Miskovic, Ljubisa; Béal, Jonas; Moret, Michael ETH Zurich https://doi.org/10.3929/ethz-b-000372256	text	January 2019
Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences Alber, Mark; Tepole, Adrian Buganza; Cannon, William arXiv https://doi.org/10.48550/arxiv.1910.01258	text	January 2019
Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties Miskovic, Ljubisa; Béal, Jonas; Moret, Michael PLOS Computational Biology, Vol. 15, Issue 8 https://doi.org/10.1371/journal.pcbi.1007242	journal	August 2019
Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches Bardsley, E. N.; Paterson, D. J. The Journal of Physiology, Vol. 598, Issue 14 https://doi.org/10.1113/jp276962	journal	November 2018
Approaches to Computational Strain Design in the Multiomics Era St. John, Peter C.; Bomble, Yannick J. Frontiers in Microbiology, Vol. 10 https://doi.org/10.3389/fmicb.2019.00597	journal	April 2019
Machine Learning Applications for Mass Spectrometry-Based Metabolomics Liebal, Ulf Winfried; Phan, An N. T.; Sudhakar, Malvika RWTH Aachen University https://doi.org/10.18154/rwth-2020-06402	text	January 2020
Common principles and best practices for engineering microbiomes Lawson, Christopher E.; Harcombe, William R.; Hatzenpichler, Roland Nature Reviews Microbiology, Vol. 17, Issue 12 https://doi.org/10.1038/s41579-019-0255-9	journal	September 2019
A rapid methods development workflow for high-throughput quantitative proteomic applications Chen, Yan; Vu, Jonathan; Thompson, Mitchell G. PLOS ONE, Vol. 14, Issue 2 https://doi.org/10.1371/journal.pone.0211582	journal	February 2019

Similar Records

Kinetic Learning vo.1

Software · Thu May 17 00:00:00 EDT 2018 · OSTI ID:1510759

Martin, Hector Garcia; Costello, Zachary

Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering

Journal Article · Tue Feb 09 00:00:00 EST 2021 · Frontiers in Bioengineering and Biotechnology · OSTI ID:1510759

Roy, Somtirtha; Radivojevic, Tijana; Forrer, Mark; +8 more

Genome-Scale ¹³C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae.

Book · Tue Nov 13 00:00:00 EST 2018 · OSTI ID:1510759

Ando, David; García Martín, Héctor

Related Subjects

59 BASIC BIOLOGICAL SCIENCES

Title: A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

Citation Formats

References (83)

Cited By (17)

Similar Records

Related Subjects