Research Areas
Cancer Studies
Cancer is a complex disease that results from a combination of genetic and environmental perturbations to biomolecular networks that, in healthy tissues, maintain a homeostatic balance between normal cellular functional states. Computational and statistical techniques for analyzing primary tumors, metastases, or cell culture systems can help us to understand cellular dysfunction underlying cancer onset, progression, and metastasis as well as to develop novel diagnostic and prognostic tools.
Multiscale Modeling and Digital Twins
A hallmark of living systems is their multiscale nature – their structure and behavior, in time and space, functions on multiple scales of biological organization. Furthermore, these scales are interlinked in that system behaviors on one scale influence and constrain behaviors on another scale. Consider that individual cells are themselves complex systems of molecular interactions comprised of networks.
Networks
Complex dynamical biomolecular systems govern virtually all biological processes on developmental and physiological time scales. A paramount problem is to understand how structural and dynamical properties of such systems affect their roles in cellular function and dysfunction. Our group has developed network inference approaches by integrating the information from multiple types of measurement data using a variety of modeling formalisms.
Biological Image Analysis
High-throughput cellular imaging and microfluidic technologies are enabling phenotypic measurements on single-cell and population-wide scales. The extraction of information from such imaging data is necessary for establishing the relationships between the behavior of molecular networks in cells and quantitative phenotypic features of cells and tissues. Image processing and analysis methods can help us detect, count and describe the shapes of subcellular and multicellular structures .
Computational Biology Tools & Methods
Large-scale high-throughput measurement technologies have allowed system-wide modeling and analysis of cells in health and disease. In order to be able to make reliable inferences, each type of measurement data calls for the development of appropriate statistical and computational methods.
Complex Systems
We are studying complex dynamical systems for understanding fundamental principles governing living systems at various scales of organization. Our work has focused on: the relationships between the structure of such systems and their dynamics; the ability to balance robustness with adaptability in an uncertain and variable environment while making decisions in response to information in the environment.
