| Year |
Citation |
Score |
| 2024 |
Zhao J, Chen K, Palsson BO, Yang L. StressME: Unified computing framework of Escherichia coli metabolism, gene expression, and stress responses. Plos Computational Biology. 20: e1011865. PMID 38346086 DOI: 10.1371/journal.pcbi.1011865 |
0.447 |
|
| 2023 |
Rychel K, Tan J, Patel A, Lamoureux C, Hefner Y, Szubin R, Johnsen J, Mohamed ETT, Phaneuf PV, Anand A, Olson CA, Park JH, Sastry AV, Yang L, Feist AM, et al. Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance. Cell Reports. 42: 113105. PMID 37713311 DOI: 10.1016/j.celrep.2023.113105 |
0.564 |
|
| 2023 |
Dahal S, Renz A, Dräger A, Yang L. Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation. Communications Biology. 6: 165. PMID 36765199 DOI: 10.1038/s42003-023-04540-8 |
0.371 |
|
| 2023 |
Park JY, Lee SM, Ebrahim A, Scott-Nevros ZK, Kim J, Yang L, Sastry A, Seo SW, Palsson BO, Kim D. Model-driven experimental design workflow expands understanding of regulatory role of Nac in . Nar Genomics and Bioinformatics. 5: lqad006. PMID 36685725 DOI: 10.1093/nargab/lqad006 |
0.523 |
|
| 2023 |
Yao H, Dahal S, Yang L. Novel context-specific genome-scale modelling explores the potential of triacylglycerol production by Chlamydomonas reinhardtii. Microbial Cell Factories. 22: 13. PMID 36650525 DOI: 10.1186/s12934-022-02004-y |
0.477 |
|
| 2021 |
Lloyd CJ, Monk J, Yang L, Ebrahim A, Palsson BO. Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth. Plos Computational Biology. 17: e1007817. PMID 34161321 DOI: 10.1371/journal.pcbi.1007817 |
0.588 |
|
| 2021 |
Anand A, Olson CA, Sastry AV, Patel A, Szubin R, Yang L, Feist AM, Palsson BO. Restoration of fitness lost due to dysregulation of the pyruvate dehydrogenase complex is triggered by ribosomal binding site modifications. Cell Reports. 35: 108961. PMID 33826886 DOI: 10.1016/j.celrep.2021.108961 |
0.479 |
|
| 2020 |
Dahal S, Yurkovich JT, Xu H, Palsson BO, Yang L. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models. Proteomics. e1900282. PMID 32579720 DOI: 10.1002/Pmic.201900282 |
0.534 |
|
| 2020 |
Kavvas ES, Yang L, Monk JM, Heckmann D, Palsson BO. A biochemically-interpretable machine learning classifier for microbial GWAS. Nature Communications. 11: 2580. PMID 32444610 DOI: 10.1038/S41467-020-16310-9 |
0.45 |
|
| 2020 |
Mih N, Monk JM, Fang X, Catoiu E, Heckmann D, Yang L, Palsson BO. Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes. Bmc Bioinformatics. 21: 162. PMID 32349661 DOI: 10.1186/S12859-020-3505-Y |
0.528 |
|
| 2020 |
Seif Y, Choudhary KS, Hefner Y, Anand A, Yang L, Palsson BO. Metabolic and genetic basis for auxotrophies in Gram-negative species. Proceedings of the National Academy of Sciences of the United States of America. PMID 32132208 DOI: 10.1073/Pnas.1910499117 |
0.588 |
|
| 2020 |
Dahal S, Zhao J, Yang L. Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications Biotechnology and Bioprocess Engineering. 25: 931-943. DOI: 10.1007/s12257-020-0061-2 |
0.384 |
|
| 2019 |
Du B, Yang L, Lloyd CJ, Fang X, Palsson BO. Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli. Plos Computational Biology. 15: e1007525. PMID 31809503 DOI: 10.1371/Journal.Pcbi.1007525 |
0.569 |
|
| 2019 |
Sastry AV, Gao Y, Szubin R, Hefner Y, Xu S, Kim D, Choudhary KS, Yang L, King ZA, Palsson BO. The Escherichia coli transcriptome mostly consists of independently regulated modules. Nature Communications. 10: 5536. PMID 31797920 DOI: 10.1038/S41467-019-13483-W |
0.528 |
|
| 2019 |
Anand A, Chen K, Yang L, Sastry AV, Olson CA, Poudel S, Seif Y, Hefner Y, Phaneuf PV, Xu S, Szubin R, Feist AM, Palsson BO. Adaptive evolution reveals a tradeoff between growth rate and oxidative stress during naphthoquinone-based aerobic respiration. Proceedings of the National Academy of Sciences of the United States of America. PMID 31767748 DOI: 10.1073/Pnas.1909987116 |
0.55 |
|
| 2019 |
Anand A, Chen K, Catoiu E, Sastry AV, Olson CA, Sandberg TE, Seif Y, Xu S, Szubin R, Yang L, Feist AM, Palsson BO. OxyR is a convergent target for mutations acquired during adaptation to oxidative stress-prone metabolic states. Molecular Biology and Evolution. PMID 31651953 DOI: 10.1093/Molbev/Msz251 |
0.556 |
|
| 2019 |
Yang L, Mih N, Anand A, Park JH, Tan J, Yurkovich JT, Monk JM, Lloyd CJ, Sandberg TE, Seo SW, Kim D, Sastry AV, Phaneuf P, Gao Y, Broddrick JT, et al. Cellular responses to reactive oxygen species are predicted from molecular mechanisms. Proceedings of the National Academy of Sciences of the United States of America. PMID 31270234 DOI: 10.1073/Pnas.1905039116 |
0.473 |
|
| 2019 |
Lachance JC, Lloyd CJ, Monk JM, Yang L, Sastry AV, Seif Y, Palsson BO, Rodrigue S, Feist AM, King ZA, Jacques PÉ. BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. Plos Computational Biology. 15: e1006971. PMID 31009451 DOI: 10.1371/Journal.Pcbi.1006971 |
0.617 |
|
| 2019 |
Anand A, Olson CA, Yang L, Sastry AV, Catoiu E, Choudhary KS, Phaneuf PV, Sandberg TE, Xu S, Hefner Y, Szubin R, Feist AM, Palsson BO. Pseudogene repair driven by selection pressure applied in experimental evolution. Nature Microbiology. PMID 30692668 DOI: 10.1038/S41564-018-0340-2 |
0.455 |
|
| 2019 |
Yang L, Ebrahim A, Lloyd CJ, Saunders MA, Palsson BO. DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression. Bmc Systems Biology. 13: 2. PMID 30626386 DOI: 10.1186/S12918-018-0675-6 |
0.616 |
|
| 2018 |
Kavvas ES, Catoiu E, Mih N, Yurkovich JT, Seif Y, Dillon N, Heckmann D, Anand A, Yang L, Nizet V, Monk JM, Palsson BO. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nature Communications. 9: 4306. PMID 30333483 DOI: 10.1038/S41467-018-06634-Y |
0.5 |
|
| 2018 |
Kabimoldayev I, Nguyen AD, Yang L, Park S, Lee EY, Kim D. Basics of genome-scale metabolic modeling and applications on C1-utilization. Fems Microbiology Letters. PMID 30256945 DOI: 10.1093/Femsle/Fny241 |
0.51 |
|
| 2018 |
Gao Y, Yurkovich JT, Seo SW, Kabimoldayev I, Dräger A, Chen K, Sastry AV, Fang X, Mih N, Yang L, Eichner J, Cho BK, Kim D, Palsson BO. Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655. Nucleic Acids Research. PMID 30137486 DOI: 10.1093/Nar/Gky752 |
0.518 |
|
| 2018 |
Lloyd CJ, Ebrahim A, Yang L, King ZA, Catoiu E, O'Brien EJ, Liu JK, Palsson BO. COBRAme: A computational framework for genome-scale models of metabolism and gene expression. Plos Computational Biology. 14: e1006302. PMID 29975681 DOI: 10.1371/Journal.Pcbi.1006302 |
0.597 |
|
| 2018 |
Yang L, Yurkovich JT, King ZA, Palsson BO. Modeling the multi-scale mechanisms of macromolecular resource allocation. Current Opinion in Microbiology. 45: 8-15. PMID 29367175 DOI: 10.1016/J.Mib.2018.01.002 |
0.535 |
|
| 2018 |
Yurkovich JT, Yang L, Palsson BO. Toward a Proteome-Complete Computational Model of the Human Red Blood Cell Blood. 132: 4888-4888. DOI: 10.1182/Blood-2018-99-114969 |
0.575 |
|
| 2017 |
Chen K, Gao Y, Mih N, O'Brien EJ, Yang L, Palsson BO. Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proceedings of the National Academy of Sciences of the United States of America. 114: 11548-11553. PMID 29073085 DOI: 10.1073/Pnas.1705524114 |
0.534 |
|
| 2017 |
Yurkovich JT, Zielinski DC, Yang L, Paglia G, Rolfsson O, Sigurjónsson ÓE, Broddrick JT, Bordbar A, Wichuk K, Brynjólfsson S, Palsson S, Gudmundsson S, Palsson BO. Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks. The Journal of Biological Chemistry. PMID 29030425 DOI: 10.1074/Jbc.M117.804914 |
0.547 |
|
| 2017 |
Fang X, Sastry A, Mih N, Kim D, Tan J, Yurkovich JT, Lloyd CJ, Gao Y, Yang L, Palsson BO. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. Proceedings of the National Academy of Sciences of the United States of America. PMID 28874552 DOI: 10.1073/Pnas.1702581114 |
0.515 |
|
| 2017 |
Yurkovich JT, Yang L, Palsson BO. Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells. Plos Computational Biology. 13: e1005424. PMID 28264007 DOI: 10.1371/Journal.Pcbi.1005424 |
0.533 |
|
| 2017 |
Ma D, Yang L, Fleming RM, Thiele I, Palsson BO, Saunders MA. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. Scientific Reports. 7: 40863. PMID 28098205 DOI: 10.1038/Srep40863 |
0.555 |
|
| 2016 |
Yang L, Yurkovich JT, Lloyd CJ, Ebrahim A, Saunders MA, Palsson BO. Principles of proteome allocation are revealed using proteomic data and genome-scale models. Scientific Reports. 6: 36734. PMID 27857205 DOI: 10.1038/Srep36734 |
0.625 |
|
| 2016 |
Yang L, Ma D, Ebrahim A, Lloyd CJ, Saunders MA, Palsson BO. solveME: fast and reliable solution of nonlinear ME models. Bmc Bioinformatics. 17: 391. PMID 27659412 DOI: 10.1186/S12859-016-1240-1 |
0.583 |
|
| 2016 |
Brunk E, George KW, Alonso-Gutierrez J, Thompson M, Baidoo E, Wang G, Petzold CJ, McCloskey D, Monk J, Yang L, O'Brien EJ, Batth TS, Martin HG, Feist A, Adams PD, et al. Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. Cell Systems. PMID 27211860 DOI: 10.1016/J.Cels.2016.04.004 |
0.584 |
|
| 2015 |
Yang L, Tan J, O'Brien EJ, Monk JM, Kim D, Li HJ, Charusanti P, Ebrahim A, Lloyd CJ, Yurkovich JT, Du B, Dräger A, Thomas A, Sun Y, Saunders MA, et al. Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data. Proceedings of the National Academy of Sciences of the United States of America. PMID 26261351 DOI: 10.1073/Pnas.1501384112 |
0.583 |
|
| 2015 |
Yang L, Srinivasan S, Mahadevan R, Cluett WR. Characterizing metabolic pathway diversification in the context of perturbation size. Metabolic Engineering. 28: 114-22. PMID 25542850 DOI: 10.1016/J.Ymben.2014.11.013 |
0.63 |
|
| 2013 |
Gawand P, Yang L, Cluett WR, Mahadevan R. Metabolic model refinement using phenotypic microarray data. Methods in Molecular Biology (Clifton, N.J.). 985: 47-59. PMID 23417798 DOI: 10.1007/978-1-62703-299-5_3 |
0.707 |
|
| 2013 |
Zhuang K, Yang L, Cluett WR, Mahadevan R. Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. Bmc Biotechnology. 13: 8. PMID 23388063 DOI: 10.1186/1472-6750-13-8 |
0.592 |
|
| 2011 |
Yang L, Cluett WR, Mahadevan R. EMILiO: a fast algorithm for genome-scale strain design. Metabolic Engineering. 13: 272-81. PMID 21414417 DOI: 10.1016/J.Ymben.2011.03.002 |
0.573 |
|
| 2010 |
Garg S, Yang L, Mahadevan R. Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling. Bmc Research Notes. 3: 125. PMID 20444261 DOI: 10.1186/1756-0500-3-125 |
0.642 |
|
| 2010 |
Yang L, Cluett WR, Mahadevan R. Rapid design of system-wide metabolic network modifications using iterative linear programming Ifac Proceedings Volumes (Ifac-Papersonline). 9: 391-396. DOI: 10.3182/20100705-3-Be-2011.00065 |
0.627 |
|
| 2008 |
Yang L, Mahadevan R, Cluett WR. A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks Computers and Chemical Engineering. 32: 2072-2085. DOI: 10.1016/J.Compchemeng.2007.10.015 |
0.66 |
|
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