Our projects often involve rounds of iterations between computational and experimental investigations.

Our research group consists of an exciting mixture of mathematicians, engineers, and biologists who are developing knowledge in three main, but intersecting, directions.

We aim to tackle fundamental questions to uncover the basic design principles of biological signaling.

  • Inter­- and intra­cellular communication, and processing of single and multicellular systems
  • Evolutionary rewiring of conserved pathways across multiple species
  • Population heterogeneity in cellular decision­making

Select Paper: Venturelli OS, Zuleta IA, Murray RM, El-Samad H. Population diversification in a metabolic program promotes anticipation of environmental shifts. PLoS Biology, in press.

To obtain higher resolution on the molecular mechanisms underlying cellular signaling, we are developing and applying quantitative perturbative and measurement tools.

  • Cellular perturbations: in silico feedback control of gene expression circuits and cellular signaling pathways. Optogenetic tools for controlling cellular localization
  • Cellular measurements: Development of high­throughput automated flow cytometry

Select Paper: Milias-Argeitis A, Summers S, Stewart-Ornstein J, Zuleta I, Pincus D, El-Samad H, Khammash M, Lygeros J. In silico feedback for in vivo regulation of a gene expression circuit. Nat 22057053.

To further lend intuition on complicated biological processes, we develop and employ mathematical modeling tools to inform testable hypotheses and uncover predictable structure in high dimensional data.

  • Deterministic and stochastic modeling of dynamic biological processes
  • Statistical and machine learning methods for analysis of Next­Generation sequencing data

Select Paper: Lipinski-Kruszka J, Stewart-Ornstein J, Chevalier MW, El-Samad H. Using Dynamic Noise Propagation to Infer Causal Regulatory Relationships in Biochemical Networks. ACS Synth Biol. 2014 Jul 11. [Epub ahead of print] PubMed PMID: 24967515.