|EMILiO: a fast algorithm for genome-scale strain design|
L Yang, WR Cluett, R Mahadevan
Metabolic engineering 13 (3), 272-281, 2011
|COBRAme: A computational framework for genome-scale models of metabolism and gene expression|
CJ Lloyd, A Ebrahim, L Yang, ZA King, E Catoiu, EJ O’Brien, JK Liu, ...
PLoS computational biology 14 (7), e1006302, 2018
|Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance|
ES Kavvas, E Catoiu, N Mih, JT Yurkovich, Y Seif, N Dillon, D Heckmann, ...
Nature communications 9 (1), 1-9, 2018
|Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow|
E Brunk, KW George, J Alonso-Gutierrez, M Thompson, E Baidoo, ...
Cell systems 2 (5), 335-346, 2016
|Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities|
X Fang, A Sastry, N Mih, D Kim, J Tan, JT Yurkovich, CJ Lloyd, Y Gao, ...
Proceedings of the National Academy of Sciences 114 (38), 10286-10291, 2017
|The Escherichia coli transcriptome mostly consists of independently regulated modules|
AV Sastry, Y Gao, R Szubin, Y Hefner, S Xu, D Kim, KS Choudhary, ...
Nature communications 10 (1), 1-14, 2019
|Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design|
K Zhuang, L Yang, WR Cluett, R Mahadevan
BMC biotechnology 13 (1), 1-15, 2013
|Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation|
K Chen, Y Gao, N Mih, EJ O’Brien, L Yang, BO Palsson
Proceedings of the National Academy of Sciences 114 (43), 11548-11553, 2017
|Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655|
Y Gao, JT Yurkovich, SW Seo, I Kabimoldayev, A Dräger, K Chen, ...
Nucleic acids research 46 (20), 10682-10696, 2018
|solveME: fast and reliable solution of nonlinear ME models|
L Yang, D Ma, A Ebrahim, CJ Lloyd, MA Saunders, BO Palsson
BMC Bioinformatics 17 (1), 391, 2016
|Cellular responses to reactive oxygen species are predicted from molecular mechanisms|
L Yang, N Mih, A Anand, JH Park, J Tan, JT Yurkovich, JM Monk, CJ Lloyd, ...
Proc Natl Acad Sci USA 116 (28), 14368-14373, 2019
|BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data|
JC Lachance, CJ Lloyd, JM Monk, L Yang, AV Sastry, Y Seif, BO Palsson, ...
PLoS computational biology 15 (4), e1006971, 2019
|Modeling the multi-scale mechanisms of macromolecular resource allocation|
L Yang, JT Yurkovich, ZA King, BO Palsson
Current opinion in microbiology 45, 8-15, 2018
|Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data|
L Yang, J Tan, EJ O’Brien, JM Monk, D Kim, HJ Li, P Charusanti, ...
Proceedings of the National Academy of Sciences 112 (34), 10810-10815, 2015
|DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression|
L Yang, A Ebrahim, CJ Lloyd, MA Saunders, BO Palsson
BMC systems biology 13 (1), 1-15, 2019
|Adaptive evolution reveals a tradeoff between growth rate and oxidative stress during naphthoquinone-based aerobic respiration|
A Anand, K Chen, L Yang, AV Sastry, CA Olson, S Poudel, Y Seif, ...
Proceedings of the National Academy of Sciences 116 (50), 25287-25292, 2019
|Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks|
JT Yurkovich, DC Zielinski, L Yang, G Paglia, O Rolfsson, ...
Journal of Biological Chemistry 292 (48), 19556-19564, 2017
|Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression|
D Ma, L Yang, RMT Fleming, I Thiele, BO Palsson, MA Saunders
Scientific Reports 7, 40863, 2017
|Principles of proteome allocation are revealed using proteomic data and genome-scale models|
L Yang, JT Yurkovich, CJ Lloyd, A Ebrahim, MA Saunders, BO Palsson
Scientific reports 6 (1), 1-8, 2016
|A biochemically-interpretable machine learning classifier for microbial GWAS|
ES Kavvas, L Yang, JM Monk, D Heckmann, BO Palsson
Nature communications 11 (1), 1-11, 2020