Brian Munsky receives NIH early-career award to advance computational biology

Assistant Professor Brian Munsky, chemical and biological engineering and biomedical engineering, received a Maximizing Investigators’ Research Award (MIRA) for Early Stage Investigators from the NIH National Institute of General Medical Sciences (NIGMS). The five-year project, “Using cellular fluctuations and computational analyses to probe biological mechanisms,” leverages Munsky’s expertise in computational biology to probe the information content in single-cell fluctuations.

Munsky’s research will create novel computational analyses to understand, predict, and control the dynamics of cell signaling, transcription, and translation. When integrated with modern single-cell and single-molecule experiments in collaborators’ laboratories — Gregor Neuert at Vanderbilt University, Tim Stasevich at CSU, and several others — Munsky’s computational tools will help discover predictive knowledge about the basic nature and behavior of living systems.

Credit: Neuert Laboratory

Videos reveal incredible insight into the complex and inherently random dynamics of single-cell regulation. For example, this video from the Neuert Laboratory at Vanderbilt University quantifies the spatial, temporal, and stochastic (i.e. random) dynamics of cell signaling (red) and gene activation (green).

Keck Foundation Grant

Munsky also received a recent $1.2 million medical research grant from the W.M. Keck Foundation, with fellow CSU Assistant Professor Tim Stasevich, biochemistry and molecular biology. Their project combines Munsky’s computational expertise with Stasevich’s innovative live-cell, super-resolution microscopy, and aims to observe and model, for the first time, real-time single RNA to protein translation in vivo. The technological advances to be developed in this project will also make it possible to discover and quantify ribosomal frameshifting, a mechanism which allows distinctly different proteins to be translated from the same RNA strand and is exploited by viruses for their replication. These results will help the biomedical community to better understand, control and predict the process of translation as it normally occurs in cells and when cells are infected by viruses.

New single-cell imaging approaches produce an unprecedented amount of data to quantify the fundamental, yet extremely complicated, processes of cell signaling, transcription, and translation. Advanced computational analyses allow researchers to redesign these experiments, untangle the complexity and randomness within these data, and to extract the most valuable insights. With the proper integration of experiments and computation, Munsky and Stasevich will first predict and then change the future of biomedical research.

q-bio Summer School

Munsky is well known for his contributions to guide the emerging field of quantitative biology (q-bio) into maturity. He has been the lead organizer of the internationally-renowned q-bio Summer School since 2010, and is a current member of the q-bio Board of Directors. The q-bio Summer Schools have collectively trained over 500 graduate students and postdocs, many of whom have gone on to faculty positions around the world. Munsky served as the lead editor of an online q-bio textbook that will published by MIT Press in early 2018, with chapters in the textbook contributed by past summer school participants. 

The Munsky Research Group

Prior to joining the faculty in the Walter Scott, Jr. College of Engineering, Munsky earned a doctorate in mechanical engineering from UC Santa Barbara, and held the Richard P. Feynman Distinguished Postdoctoral Fellowship in Theory and Computing at Los Alamos National Laboratory. He is the recipient of the Leon Heller Postdoctoral Publication Prize and the LANL Postdoctoral Distinguished Performance award. Munsky was a joint first author on a 2013 Editor’s Choice Science paper with Vanderbilt University’s Gregor Neuert that proposed the “Goldilocks model”, a data-driven approach that allows researchers to systematically determine when models are too simple, too complex, or just right for predictive purposes.

Munsky’s group creates new tools to explore biological models at multiple interconnected scales, evaluate model uncertainty, and efficiently select among thousands of hypotheses. They apply powerful theories for parameter estimation, experiment design, and model reduction to maximize the accuracy and efficiency of experimental and computational analyses. These integrated approaches enable models to discover new scientific predictions, to optimize genetically engineered systems, and to control biological behavior.

Credit: Brian Munsky

Credit: Brian Munsky

Different molecule or cell types can exhibit unique spatial, temporal, and probabilistic signatures. These simulated cells emit the exact same average fluorescence, but they are easy to differentiate using statistics (compare top and middle) or response dynamics (compare top and bottom). The Munsky Group uses advanced statistical tools to discover, characterize, and re-engineer these “fluctuation fingerprints.”

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Cyberbiosecurity at CSU

Professor of Chemical and Biological Engineering and Abell Chair Jean Peccoud has introduced the emerging field of “cyberbiosecurity”, aimed at understanding the risks emerging at the frontier between cyberspace and biology in order to develop policies to manage them. In a recent article in Trends in Biotechnology, Peccoud and co-workers describe the security risks that come with the increasing digitization of biotechnology workflows, and advocate for a new culture of cybersecurity awareness.