Eran Agmon is a postdoc in the Department of Bioengineering, where he is part of the Covert lab’s team developing a whole-cell computational model of Escherichia coli. His research interests include multi-scale modeling frameworks for cell biology, models of lipid membranes and transmembrane transport, the spatial organization of cells, and bacterial chemotaxis.

Professional Education

  • Master of Science, Portland State University, Systems Science (2011)
  • Postdoc, Columbia University, Biological Sciences (2017)
  • Visiting Scholar, Institute for Advanced Study, Princeton, Interdisciplinary Studies (2017)
  • Bachelor of Science, University of California San Diego (2009)
  • Doctor of Philosophy, Indiana University (2016)

Stanford Advisors

Lab Affiliations

All Publications

  • A Multi-Scale Approach to Modeling E. coli Chemotaxis ENTROPY Agmon, E., Spangler, R. K. 2020; 22 (10)

    View details for DOI 10.3390/e22101101

    View details for Web of Science ID 000585421100001

  • 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)


    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

  • Deriving the bodily grounding of living beings with molecular autopoiesis ADAPTIVE BEHAVIOR Agmon, E. 2020; 28 (1): 35–36