Systems Biology


Research Mentors

Institution and Department

Research Focus

Anne-Ruxandra Carvunis

Pitt – Computational Biology

My research aims at understanding the molecular mechanisms of change and innovation by examining systems biology in the light of evolution and evolution in the light of systems biology.

Rob Coalson

Pitt – Chemistry, Physics, and the Center for Molecular and Materials Simulations

Computational approaches to study ion channel gating and transport.

Vaughn Cooper

Pitt – Microbiology and Molecular Genetics

 Studying the evolution, ecology, and genome dynamics of experimental and clinical microbial populations.

Lance Davidson

Pitt – Bioengineering

Multidisciplinary (mathematics, developmental biology, biophysics, and bioengineering) approach to understanding how molecular and genetic programs drive the formative tissue mechanics and self-assembly processes that generate living structures in developing embryos.

G. Bard Ermentrout

Pitt – Mathematics and the CMU-Center for the Neural Basis of Cognition

Application of nonlinear dynamics to problems from cell biology and physiology.

Jim Faeder

Pitt – Computational & Systems Biology

Mathematical (rule-based) modeling of intracellular signal transduction pathways.

Robin E.C. Lee

Pitt – Computational Biology

Quantitative imaging, microfluidics, and mathematical models to study how dynamic molecular signals transmit information in single cells.

Timothy Lezon

Pitt – Cell Biology and Physiology

Computational systems biology of signaling pathways.

Bing Liu

Pitt – Computational Biology

Development of mathematical models to study the dynamics of signaling networks; development of formal verification and machine learning based techniques for computational modeling and analysis of biological systems.

Natasa Miskov-Zivanov

Pitt – Bioengineering

Automation of learning big mechanisms in biology. Systems and synthetic biology. Emerging technologies and Internet of Things in medicine

Robert S. Parker

Pitt – Chemical and Petroleum Engineering

Systems medicine – mathematical modeling of disease (cancer, critical care/inflammation/sepsis, diabetes, cystic fibrosis) to support patient-tailored treatment decision-making.

Roni Rosenfeld

CMU – Computer Science, Machine Learning, and The Language Technology Institute

Using growing databases of viral sequences to build descriptive and generative models of viral molecular evolution.  Modeling the evolution of Influenza.

Jonathan Rubin

Pitt – Mathematics

Application of dynamical systems to modeling of inflammation and other aspects of physiology.

Hanna Salman

Pitt – Physics & Astronomy

Understanding the factors that shape phenotypic variability in populations of bacteria and how the populations benefit from such variability.

Jason Shoemaker

Pitt – Chemical and Petroleum Engineering

The Shoemaker Lab focuses on systems immunology. We model our body’s natural, dynamic responses to disease and develop mathematical tools to support these modeling challenges. Our model systems are multiscale with applications in pathogen detection and clearance, cancer immune evasion, and more. We are very interdisciplinary – most projects involve collaborations with UPMC or our international collaborators in Japan and Switzerland.

Matt Smith

Pitt – Ophthalmology and Bioengineering

Computational approaches to modeling neuronal communication and interactions, combined with experimental techniques to measure the activity of populations of neurons.

Ben Van Houten

Pitt – Pharmacology and Chemical Biology

The formation and repair of DNA damage in nuclear and mitochondrial genomes, with particular interest in the structure and function of proteins that mediate nucleotide excision repair and the role of oxidative stress in human disease.

Jianhua Xing

Pitt – Computational Biology

Computational and experimental quantitative biology approaches to study the dynamics and (genetic and epigenetic) regulatory mechanism of cell phenotype changes.

Leming Zhou

Pitt – Health Information Management

Agent-based, equation-based, and statistical modeling of cardiovascular disease; comparative genomics and its applications in personalized medicine.