| Constraint-based models predict metabolic and associated cellular functions A Bordbar, JM Monk, ZA King, BO Palsson Nature Reviews Genetics 15 (2), 107-120, 2014 | 960 | 2014 |
| Using genome-scale models to predict biological capabilities EJ O’Brien, JM Monk, BO Palsson Cell 161 (5), 971-987, 2015 | 758 | 2015 |
| Wdr5 mediates self-renewal and reprogramming via the embryonic stem cell core transcriptional network YS Ang, SY Tsai, DF Lee, J Monk, J Su, K Ratnakumar, J Ding, Y Ge, ... Cell 145 (2), 183-197, 2011 | 671 | 2011 |
| iML1515, a knowledgebase that computes Escherichia coli traits JM Monk, CJ Lloyd, E Brunk, N Mih, A Sastry, Z King, R Takeuchi, ... Nature biotechnology 35 (10), 904-908, 2017 | 551 | 2017 |
| MEMOTE for standardized genome-scale metabolic model testing C Lieven, ME Beber, BG Olivier, FT Bergmann, M Ataman, P Babaei, ... Nature biotechnology 38 (3), 272-276, 2020 | 413 | 2020 |
| Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments JM Monk, P Charusanti, RK Aziz, JA Lerman, N Premyodhin, JD Orth, ... Proceedings of the National Academy of Sciences 110 (50), 20338-20343, 2013 | 339 | 2013 |
| What Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus Leptospira DE Fouts, MA Matthias, H Adhikarla, B Adler, L Amorim-Santos, DE Berg, ... PLoS neglected tropical diseases 10 (2), e0004403, 2016 | 333 | 2016 |
| Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity E Bosi, JM Monk, RK Aziz, M Fondi, V Nizet, BØ Palsson Proceedings of the National Academy of Sciences 113 (26), E3801-E3809, 2016 | 268 | 2016 |
| Optimizing genome-scale network reconstructions J Monk, J Nogales, BO Palsson Nature biotechnology 32 (5), 447-452, 2014 | 243 | 2014 |
| 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), 4306, 2018 | 189 | 2018 |
| High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities J Nogales, J Mueller, S Gudmundsson, FJ Canalejo, E Duque, J Monk, ... Environmental microbiology 22 (1), 255-269, 2020 | 170 | 2020 |
| Multi-omics quantification of species variation of Escherichia coli links molecular features with strain phenotypes JM Monk, A Koza, MA Campodonico, D Machado, JM Seoane, ... Cell systems 3 (3), 238-251. e12, 2016 | 140 | 2016 |
| Genome-scale metabolic reconstructions of multiple Salmonella strains reveal serovar-specific metabolic traits Y Seif, E Kavvas, JC Lachance, JT Yurkovich, SP Nuccio, X Fang, ... Nature communications 9 (1), 3771, 2018 | 135 | 2018 |
| Model-driven discovery of underground metabolic functions in Escherichia coli GI Guzmán, J Utrilla, S Nurk, E Brunk, JM Monk, A Ebrahim, BO Palsson, ... Proceedings of the National Academy of Sciences 112 (3), 929-934, 2015 | 113 | 2015 |
| 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, ... Proceedings of the National Academy of Sciences 116 (28), 14368-14373, 2019 | 109 | 2019 |
| 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 | 102 | 2016 |
| 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 | 97 | 2019 |
| A biochemically-interpretable machine learning classifier for microbial GWAS ES Kavvas, L Yang, JM Monk, D Heckmann, BO Palsson Nature communications 11 (1), 2580, 2020 | 79 | 2020 |
| A workflow for generating multi-strain genome-scale metabolic models of prokaryotes CJ Norsigian, X Fang, Y Seif, JM Monk, BO Palsson Nature protocols 15 (1), 1-14, 2020 | 75 | 2020 |
| Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens JC Hyun, ES Kavvas, JM Monk, BO Palsson PLoS computational biology 16 (3), e1007608, 2020 | 68 | 2020 |