Allen Discovery Center at Stanford University
Systems Modeling of Infection
Multiscale models that can integrate data from the levels of genes and proteins to a full cell, to collections of cells within a tissue, and ultimately to tissues and organs, is a grand challenge for systems biology. These kinds of models will be capable of predicting how perturbations at one level of scale, such as gene expression, affect important outcomes at other levels of scale, like phenotype and function.
In order to understand the molecular basis for disease—an essential to developing effective, next generation cures—we need these kinds of multiscale models that comprehensively represent whole cells, as well as their dynamic environments and interactions.
The Discovery Center team’s multiscale modeling will focus on the interaction between the pathogen Salmonella and macrophages, part of the first line of the innate immune defense. Studying and modeling this particular system will have specific, immediate impact on a global biomedical challenge of antibiotic-resistant pathogens, as well as generally enhance our understanding of complex diseases.
The modeling approach the team employs will lead to the identification of better, more sophisticated antimicrobial strategies by integrating multiple biological pathways and networks, allowing for heterogeneous cellular phenotypes, including host-pathogen interactions during infection and accounting for the in vivo environment. Combined, these inputs will yield powerful, predictive and highly relevant models.
The goals of the Allen Discovery Center at Stanford include major advances in several fields. In addition to improving whole-cell modeling of both host cells and infectious bacteria, the team will advance the modeling of interacting cells, improve computational power to boost simulation run time, create new visualization tools and employ deep learning for data analysis, and describe computational measurements of observations of cellular processes and dynamics.
Ultimately, the team’s models will suggest experiments with the highest likelihood of generating new knowledge, shortening the path to breakthroughs, and be able to predict or diagnose complex, multi-network phenotypes, both within individual cells and as a result of cell-to-cell interactions and heterogeneity.
Markus Covert, Ph.D.
Markus Covert is currently an Associate Professor at Stanford University. He received a B.S. in Chemical Engineering from Brigham Young University, followed by M.S. and Ph.D. degrees in Bioengineering and Bioinformatics from UCSD, and postdoctoral studies at Caltech. Leveraging his computational and experimental training, Covert's research focuses on integrating cutting-edge computational modeling methods together with experimental techniques to better understand complex cellular behaviors. He is best known for constructing the first "whole-cell" computational model, which explicitly represents all of the known gene functions and molecules in Mycoplasma genitalium. Reported in The New York Times, BBC World News, Scientific American and hundreds of other media outlets worldwide, this work was also recently cited by Cell as one of the most exciting developments reported during the 40-year history of that journal. In addition, Dr. Covert's lab has generated several new exciting experimental techniques to measure and analyze the behaviors of individual cells. Dr. Covert's work has received several awards, including an NIH Pathway to Independence Award (2007), NIH Director's Pioneer Award (2009), and the Paul G. Allen Family Foundation Distinguished Investigator Award (2013).