Understanding how biological systems work requires knowledge of their component parts, how those parts are connected together, and how then those connected parts work together as part of a dynamic evolving system. With the advent of large-scale sequencing projects, a catalog of molecular parts has started to be compiled. While the molecular wiring between these parts has been under investigation by biologists for decades, only recently have comprehensive studies of connectivity and dynamics been able to be performed. While modeling and analysis of these systems is still in its infancy, systems approaches have significant potential in helping us to understand both biological function as well as dysfunction.

Our primary research is in the area of computational systems biology, with a focus on networks. We are particularly interested in biological signaling networks; trying to understand their structure, evolution and dynamics. In collaboration with wet lab experimentalists, we are developing computational models, including probabilistic graphical and multivariate methods, along with more traditional engineering approaches such as system identification and control theory, and applying them to the study of disease-relevant systems. With numerous advances in imaging technologies, we have also recently become involved in the development of image analysis tools, with the longer term goal of being able to infer the underlying network that controls the cellular behavior observed in these images.

Cell Signaling & the Kinome

We are actively involved in the study of cell signaling systems. For instance, in collaboration with Gary Johnson (UNC Dept. of Pharmacology), we are investigating the architecture and dynamics of the “kinome” – the network of kinases that drive numerous cell signaling processes. We are particularly interested in dysregulated homeostasis which can drive pathophysiological changes related to tissue remodeling (e.g. cancer). We are developing and applying computational approaches to the analysis and modeling of these networks and their use as clinical diagnostics.

Similarly, with Beverly Errede (Dept. of Biochemistry and Biophysics) and Tim Elston (Dept. of Pharmacology), we are investigating MAPK regulation of cell-fate transitions and decision making processes in yeast. This work focuses on the pheromone-induced transition of budding yeast to either a chemotrophic or mating competent form. Regulation of this decision making process is done by  a single MAPK cascade. We are using a combination of experimental and computational techniques to try and better understand how such cell fate decisions are controlled.

Systems Genetics & Metabolism

While much of our work focuses on studies at the level of cellular pathways and networks, we are very interested in higher-level relationships that link the genotype of an organism with its phenotype. More specifically, it is understood that the presentation of a particular phenotype occurs as an output of a highly complex system, components of which are organized into tightly coordinated networks. This system spans a range of scales from the molecular scale of genes and proteins up to the physiological level of connected tissues and organs. Our hypothesis is that genetic variability alters the architecture and/or dynamics of the components of this system, leading directly, in a proper environmental context, to observed differences in phenotype. We are part of the UNC Computational Genetics Research Group which brings together computational and experimental researchers to address outstanding questions in systems genetics.

We are actively pursuing several computational studies focused on the reconstruction of gene-regulatory and protein interaction networks, network comparison as well as network modeling. Furthermore, in collaboration with Daniel Pomp and Fernando Pardo-Manuel de Villena, we have initiated systems-level studies of metabolism in mice derived from the Collaborative Cross, with particular emphasis on linking genetic variation and diet with metabolic function. In this work, we are developing mechanistic models of metabolism that can be used to help link genetic variation to mechanistic explanations for changes in biological function.

Live Cell Imaging of Signaling Networks & Bioimage Informatics

Highly related to our work in understanding cell signaling systems, we are developing innovative tools for use in quantitative imaging within living cells. In collaboration with experimental collaborators, we are using these tools to quantify the behavior of various signaling molecules and pathways. One major focus is on understanding the spatiotemporal dynamics of focal adhesions which are highly dynamic protein complexes responsible for mediating the cell’s interaction with the outside environment; acting as a point of integration for both mechanical and chemical signaling. High-content, quantitative investigations of FA dynamics will help improve our understanding of such processes as cellular motility, metastasis and wound healing.