Associating growth conditions with cellular composition in
Research Office (ARO),
Investigators: C. Wilke, J. E. Barrick, E. M. Marcotte,
P. Ravikumar, M. S. Trent at UT Austin, D. Segre' and Yannis
Paschalidis at Boston Univ., and C. J. Marx at Harvard Univ.
Microbes impact many vital civilian and military activities. Naturally
occurring pathogens, such as Legionella pneumoniae (causative
agent of Legionnaire's disease) or Yersinia pestis (causative
agent of the bubonic plague), can incapacitate entire communities. With
genetic manipulation or growth in the laboratory for deliberate attacks,
microbes can pose national security risks, as in the anthrax mailings in
2001. Microbes can also be engineered for useful purposes: to serve as
vaccines, to produce chemicals, or to break down toxins or other
This project will develop mathematical methods and make systematic
biological measurements to associate the conditions under which bacteria
have grown with the resulting composition of the bacterial cell. This
association has important applications both in bacterial forensics
(e.g., identifying the source of a pathogen used in a deliberate attack)
and in engineering applications. We will complete four specific tasks:
Task I: Develop methods to identify statistical association in
multiple-input--multiple-output (MIMO) data. We will develop linear
and non-linear methods to associate two high-dimensional data sets with each other and to predict values in one set from data points in the other.
Task II: Incorporate domain-specific knowledge into high-dimensional
association models. We will leverage side-information (such as
metabolic pathways) within the statistical framework of Task I. We will
also develop inverse optimization methods as a source of side
Task III: Generate reference data sets of bacterial composition.
We will grow bacteria under carefully controlled conditions and measure
biomass composition, mRNA abundances, protein abundances, lipid
abundances, and metabolic fluxes.
Task IV: Validate models using reference data sets. We will
validate the models developed under Tasks I and II against the reference
data sets. Validation results will feed back into further model
development as well as subsequent experiments.
To pursue this work, we have assembled an outstanding, highly
interdisciplinary team of statisticians, computer scientists,
microbiologists, and biochemists. Our work will be centered at The
University of Texas at Austin, and it will involve experts in microbial
physiology and computational modeling at Boston University and
Harvard. Successful completion of this project will yield novel
mathematical methods to associate bacterial growth conditions with
cellular composition, identification of the types and ranges of growth
conditions that lead to distinguishable cellular composition,
identification of key compositional markers that are diagnostic of
specific bacterial growth conditions, and assessments of model
uncertainty, robustness, and computational cost.