Belinda Akpa
Belinda S Akpa is a Senior Staff Scientist in Quantitative Systems Biology at ORNL and Joint Associate Professor in Chemical and Biomolecular Engineering at the University of Tennessee. She holds a BA, MEng, and doctorate in Chemical Engineering from the University of Cambridge (UK). A highly interdisciplinary researcher, her current interest is in developing mathematical frameworks that integrate scarce and heterogeneous data to connect molecular phenomena to dynamic physiological outcomes. Akpa is broadly interested in computational biology, but more specifically in how mechanistic mathematical models can be used to inform targeted experimental strategies. To date, her work has touched the fields of pharmacology/toxicology, membrane biophysics, plant physiology, and forensic anthropology.
Eric Deeds
Research in my lab is focused on using a combination of computational and experimental techniques to understand the dynamics of complex molecular networks within cells. One major area of interest to us is involves cell signaling and gene regulatory networks. In particular, we make dynamic models of these networks and compare the predictions of these models to data obtained by other groups from single-cell experimental techniques. We have recently begun to focus on single-cell RNA sequencing (scRNA-seq) data, since there is a wealth of experimental data currently emerging based on that technique. One issue with this type of data is its dimensionality; scRNA-seq can measure the expression level of thousands of genes in thousands to millions of single cells. Dimensionality reduction using approaches like PCA or t-SNE is thus a critical component of most scRNA-seq data analysis pipelines. We recently found that dimensionality reduction approaches currently employed in the field end up loosing a significant amount of biologically-relevant information about cell state, and so we are working to develop new dimensionality reduction methods that do not have this problem. Our long-term goal is to improve analyses like cell-type clustering and pseudotime analysis in order to facilitate the comparison between scRNA-seq data and the predictions of computational models of cell signaling and gene regulation.
Another major area of research in my lab involves understanding the assembly of the proteasome, a large molecular machine that is responsible for much of the protein degradation that occurs within cells. We make computational models of this process in my lab, and test predictions from those models using in vitro experimental techniques. This work involves significant experimental biochemistry, biophysics and structural biology.
Andrew Mugler
The Mugler Group investigates cell behavior using theoretical physics. We rely on a wide range of tools including statistical physics, stochastic modeling, and information theory. We tackle problems that range from the molecular to the multicellular scale, often in collaboration with experimental groups. Current projects include:
Collective sensing. Cells sense chemicals in their environment and also communicate, but the impact of communication on sensing is poorly understood. We are using tools from statistical physics to develop a unified theory of collective sensing.
Metastatic invasion. Cancer metastasis begins when tumor cells invade the surrounding tissue. We are investigating metastatic invasion using theory, simulation, and microfluidic experiments with collaborators.
Long-range signaling. Cellular communities transmit signals over long distances, but noise or defects can cause these signals to die out. We have discovered that these systems are well described by percolation theory, a branch of statistical physics that describes coffee filtering and crack formation.
Criticality in biology. The molecular networks that process information in cells share many properties with critical points from statistical physics, but the implications for cell behavior are poorly understood. We are investigating critical behavior in biochemical networks and comparing our findings to experiments in immune cells.
Elizabeth Read
We use computational approaches from engineering and the physical sciences to study cell-biological processes relevant to human health.
We develop mathematical models, computer simulation tools, and statistical inference techniques. We apply these methods to a variety of areas, including gene networks, epigenetic regulation, and immune cell activation. The themes that link all of our projects are stochastic processes in cell biology and dynamics of biomolecular networks.
We are part of the Department of Chemical and Biomolecular Engineering, the Center for Complex Biological Systems, and the NSF-Simons Center for Multiscale Cell Fate at UC Irvine.
