Author: Mikhail Blinov

24th Annual Computational Cell Biology Workshop

06-28-2023. The  24th Annual Computational Cell Biology (CCB) workshop took place at the Center for Cell Analysis and Modeling (CCAM) on June 26-28th. 11 participants from all over the US (from Maryland to Illinois) came to Farmington to work on developing their modeling projects using VCell and COPASI software tools, designed and maintained at CCAM. The students were helped by a CCAM team consisting of Michael Blinov, Ann Cowan, Leslie Loew, Pedro Mendes, Ion Moraru, Kelvin Peterson, Jim Schaff, Nathan Schaumburger, and Boris Slepchenko.

Drs. Blinov, Moraru and Kuchel publish a research on aging

05-04-2023. The interdisciplinary team consisting of CCAM modelers Drs. Blinov and Moraru and researchers from the Center on Aging Drs. Kuchel and Kositsawat, together with former undergrad students Schaumburger and Pally published a manuscript theoretically explaining the bistability of clinical outcomes: the likelihood of an individual remaining mobile over time either increases to almost 100% or decreases to almost zero

Schaumburger, N., Pally, J., Moraru, I. I., Kositsawat, J., Kuchel, G. A., & Blinov, M. L. (2023). Dynamic model assuming mutually inhibitory biomarkers of frailty suggests bistability with contrasting mobility phenotypes. Frontiers in Network Physiology, 3, 1079070.

https://www.frontiersin.org/articles/10.3389/fnetp.2023.1079070/full

VCell tutorial at ICSB 2022 in Berlin

09-12-22. Michael Blinov and Ion Moraru will give a VCell tutorial at the 21st International Conference on Systems Biology – the premier meeting on systems studies in biology, human evolution disease and planetary health. The meeting will take place in Berlin, Germany on October 8th-12th. The tutorial will be on October 9th at 12:30pm local time (https://www.icsb2022.berlin/satellite-events).

New publication of the Vera-Licona Lab

06-20-22 Congratulations to Dr. Paola Vera-Licona and Lauren Marazzi who recently published a paper in npj Systems Biology and Applications on NETISCE: a network-based tool for cell fate reprogramming. In this paper, they introduce NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using Signal Flow Analysis and Feedback Vertex Set Control, respectively.