Honors & Awards


  • Kwanjeong Educational Foundation Scholarship, Kwanjeong Educational Foundation (2017 - 2021)

Education & Certifications


  • Master of Science, Stanford University, BIOE-MS (2020)
  • B.S., Seoul National University, Chemical and Biological Engineering, Biological Sciences, Computer Science and Engineering (2017)

Stanford Advisors


Lab Affiliations


All Publications


  • Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science (New York, N.Y.) Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I., Lane, K., Spangler, R. K., Gillies, T. E., Paull, M. L., Akhter, S., Bray, S. R., Weaver, D. S., Keseler, I. M., Karp, P. D., Morrison, J. H., Covert, M. W. 2020; 369 (6502)

    Abstract

    The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

    View details for DOI 10.1126/science.aav3751

    View details for PubMedID 32703847

  • BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals BIOINFORMATICS Kim, M., Sun, G., Lee, D., Kim, B. 2017; 33 (1): 87–94

    Abstract

    Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models.We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets.MATLAB code is available at https://github.com/kms1041/BeReTa (github).byungkim@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btw557

    View details for Web of Science ID 000397091600012

    View details for PubMedID 27605107