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.
Postdoc, Columbia University, Biological Sciences (2017)
Visiting Scholar, Institute for Advanced Study, Princeton, Interdisciplinary Studies (2017)
Doctor of Philosophy, Indiana University (2016)
Master of Science, Portland State University, Systems Science (2011)
Bachelor of Science, University of California San Diego (2009)
Markus Covert, Postdoctoral Faculty Sponsor
A forecast for large-scale, predictive biology: Lessons from meteorology.
2021; 12 (6): 488-496
Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.
View details for DOI 10.1016/j.cels.2021.05.014
View details for PubMedID 34139161
- A Multi-Scale Approach to Modeling E. coli Chemotaxis ENTROPY 2020; 22 (10)
Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation.
Science (New York, N.Y.)
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 2020; 28 (1): 35–36
A Multi-Scale Approach to Modeling E. coli Chemotaxis.
Entropy (Basel, Switzerland)
2020; 22 (10)
The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium-an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium's approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.
View details for DOI 10.3390/e22101101
View details for PubMedID 33286869