Author: Mikhail Blinov

Dr. Tasnif Rahman Joins Agmon Lab

October 10, 2024. The Agmon Lab is thrilled to welcome Dr. Tasnif Rahman, our newest postdoctoral researcher, who brings a blend of expertise in tissue engineering, computational biomechanics, and microbiology. Tasnif’s research will focus on developing multi-scale computational models to study the interactions between bacterial communities and host tissues, a vital area in understanding microbial dynamics within human health.

Tasnif completed his PhD at Rensselaer Polytechnic Institute, where his thesis explored how multicellular mechanical models can explain symmetry breaking in tissue and organ morphogenesis. Now, he’s eager to build on his computational skillset and delve back into bacteriology, an area of study he first pursued during his undergraduate training in microbiology. Outside of research, Tasnif enjoys unwinding with video games, basketball, and dance music. His multidisciplinary background and enthusiasm for computational modeling make him a fantastic addition to the lab.

Cell Signaling: Principles and Methods (2nd ed) by Dr. Mayer

October 2, 2024. The second edition of Cell Signaling: Principles and Methods is coming out this month. CCAM faculty member Bruce Mayer is one of the authors of the popular cell signaling textbook, which was originally published in 2014. The second edition is has been thoroughly updated and includes two new chapters.

 

Cell Signaling: Principles and Methods (2nd Edition) by Wendell A. Lim, Bruce J. Mayer

ISBN 9780367279370

 

DARPA Funds Whole-Cell Discovery Workflow for E. coli

September 27, 2024. Dr. Eran Agmon, PI at UConn Health, has received a DARPA grant to develop an innovative workflow for discovering unknown gene functions in whole-cell models of E. coli. The project, part of DARPA’s Discovering Unknown Function (DUF) initiative, will harness UConn’s HPC resources to simulate thousands of model variants, enhancing our understanding of E. coli gene functionality. This scalable workflow will leverage modular simulation, probabilistic data analysis, and advanced inference techniques to systematically address gaps in gene function knowledge.