Wake Forest University offers outstanding research opportunities to graduate and undergraduate students and postdoctoral fellows in the core areas of bioinformatics, computational systems biology, and computational biophysics. Research areas span the scales from study of molecular and nano-systems, to modeling of signal transduction pathways and cellular biology, to statistical analysis of ecological systems. Complementary computational and experimental efforts are ongoing in most of the research areas and faculty collaborate extensively with researchers at the Wake Forest Medical School and at other universities and national laboratories.
Ongoing research projects include: 
Predicting biological networks that underlie experimental data is a major, unsolved problem in modern biology. Constructing models from time course experimental data is particularly difficult, as the number of time points is usually fewer than the number of measured genes or proteins. We are developing computational algebra and Bayesian approaches to modeling such data. Although the number of modified proteins and measured biological endpoints that respond (i.e., the number of variables) exceeds the number of time points that can be collected (i.e., the number of equations), by considering the network under various conditions and by applying game theoretic methods to multiple discretizations of the data, consensus models can be constructed. These models represent aspects of the underlying biological network, identifying dependencies between protein modifications and biological responses. This collaboration among researchers in the departments of Biochemistry, Computer Science, Mathematics, and Physics at Wake Forest University aims to develop theory, algorithms, computational tools, and research methodologies for the network modeling of time course data.
The long-term goal of this project is to provide a better understanding of the basic cellular and molecular mechanisms driving joint tissue destruction during the development of osteoarthritis (OA). We are utilizing a systems and computational biology approach to map the transcriptional regulatory networks that underlie development of OA in a stage-specific, whole organ, manner. By integrating this transcriptional regulatory network with publicly available information on signaling pathways and protein-protein interaction networks, we are: 1) identifying key genes and proteins that could serve as novel targets for disease modifying therapy, as well as novel stage-specific biomarkers; and 2) identifying pathways that are involved in the disease process, which will enhance our understanding of mechanism. Our approach utilizes a recently developed mouse model of osteoarthritis (destabilization of the medial meniscus). Advantages of this model include: it is biomechanical; damage to the meniscus is a common feature of human OA; it mimics the joint pathology of human OA; and it allows for collection of time course data (early, middle, and late disease stages). Furthermore, the wide availability of transgenic animals permits the future manipulation of identified pathways to test the role of candidate genes and proteins in the network that underlies the development of OA. This project brings together a team of scientists with expertise in computational biology, basic molecular and translational research in OA, surgical models of OA, and the histological evaluation of OA. We aim to provide a comprehensive picture of the OA disease process, thus providing unprecedented insight into the mechanism of that process with the future promise of discovering novel pathways and drug targets responsible for the initiation and progression of the disease.
Dendritic cells (DC) are essential to the development of protective immunity to a number of infectious pathogens. These cells alert the adaptive immune system to the presence of pathogenic invaders and activate these cells to clear infections. To stimulate such activation, however, they must undergo a process termed maturation that increases their potency. DC maturation is a tightly regulated process involving changes in gene expression, intracellular trafficking, cytoskeletal modifications, and mobilization to lymphoid organs. The gene expression network, the dynamic process of interaction among gene expression, regulatory sequences, and trans-acting factors, underlying this process is extremely important for controlling many of the observed changes. Very few studies have examined this process over a comprehensive time course and none have attempted to derive network models of this process. Our long-term goal is to understand, at a systems level, the biology that underlies DC maturation following stimulation by infectious agents. We aim to identify novel, previously undefined components of the DC maturation network and to identify cause-and-effect relationships that explain how DC maturation is controlled upon exposure to various infectious stimuli. In this project, we are assessing the dynamics of DC maturation by identifying and clustering genes that are significantly expressed during DC maturation over a comprehensive time course following treatment of DC with poly I:C as a model of viral infection. We are also identifying relationships between significantly expressed genes, thus beginning to identify networks of interactions. Ultimately, we will demonstrate that we can identify groups genes involved in subnetworks and model the resulting network neighborhoods, thus beginning to establish cause-and-effect versus correlative relationships within the gene expression network. Because DC maturation is such a pivotal event for protective immunity, a broader understanding of the gene expression program and the comprehensive transcriptional regulatory network underlying their maturation is a key to the identification of new targets for the design and development of vaccines and therapies against infectious agents.
Phenylpropanoid biosynthesis is an important component of plant secondary metabolism that has been extremely well characterized at the genetic, biochemical, and molecular levels. Research interest has been spurred by the importance of the endproducts in such diverse functions as flower pigmentation, UV protection, signaling (including regulation of auxin transport), male fertility, and defense against pathogens as well as their anti-oxidant and anti-cancer properties in humans. The pathway also offers a highly tractable genetic system that is characterized by easily-identifiable (i.e., flower, seed, or leaf color), non-lethal mutations that factored into Mendel’s elucidation of heritable traits, McClintock’s work on transposable elements, and the discovery of cosuppression. Extensive molecular, biochemical, and physiological characterization of this pathway and its many branches make it an ideal system in which to begin to address fundamental questions about Arabidopsis systems biology. We are utilizing new methods for producing quantitative genomics, proteomic, and metabolomic data for identification of novel components and developing new tools for defining the relationships among those components. Recent insights into the physiological functions of the metabolic products of this pathway will allow us to place these molecular and biochemical events into a physiological context. This project is unique in attempting to collect time course gene expression, protein expression, and metabolite data and combining these comprehensive data sets to create integrated biological networks to aid in understanding of the relationships between components. The project combines modeling, theory, and experimentation to produce the outcome of systems-level understanding of the phenylpropanoid biosynthetic, transcriptional and regulatory pathways, as exemplary networks, and the biological consequences of hormonal controls of this pathway and will provide a systems level understanding of a metabolic network that synthesizes molecules that are important regulators of plant growth, development, and defense, as well as serving as important antioxidants in human diet. Understanding the controls of this pathway will provide insights into how to engineer the synthetic, signaling and regulatory pathways for both improving plant growth and facilitating production of these important compounds.
The oxidation of macromolecules in biological systems is a hallmark of stress responses, inflammation and toxicity and is widely believed to be an essential causative factor in many diseases, some of which are environmentally-caused. Oxidation is often caused by reactive oxygen species (ROS), commonly oxidizing the cysteine side chain thiol group. ROS have long been known to be involved in diseases, including cancer, atherosclerosis, and neurodegenerative diseases. ROS have also been implicated in many therapies, including cancer therapies. More recently, it has become apparent that ROS are also involved in normal signaling processes and in modifying protein functions. In addition to oxidation, covalent modification of reactive cysteine thiols occurs in response to both exogenous chemicals and endogenous electrophiles associated with oxidative stress and metabolic imbalances. These protein modifications also affect human health. Taken together, the many implications of reactive thiols in normal and disease processes make a general understanding of these processes essential. Recently developed proteomics technologies are uniquely suited to identifying the molecules that are modified by ROS or that contain reactive cysteines. Reagents are targeted at cysteine sulfenic acid specifically using derivatives of the small molecule dimedone or at other reactive cysteines using other reagents and probes. These reagents allow high-throughput characterization and quantification of protein adducts and mapping of modification sites to specific protein residues. Over 1400 cysteines in over 1000 different proteins have been labeled under various conditions; proteomic-scale searches of biological systems to identify proteins that are modified under specific normal and disease conditions are ongoing. We are developing reagents to identify experimentally cysteine sulfenic acid modification sites in proteins. We are using our functional site analysis tools (DASP and PASSS) to analyze the features of these modification sites. Finally, we are developing experimental methods and our own modeling tools to identify how redox modifications affects signal transduction in various systems.
Sequence and structural genomics projects have identified and predicted molecular functions in proteins, yet researchers still cannot determine biological mechanisms of, for example, catalysis or substrate specificity or inhibitor binding, without detailed biochemical and biophysical analysis of a single protein. While structural genomics projects are providing the necessary data, they are not being used to reveal the general principles underlying biological mechanism. We are using sequence, structure, bioinformatics, and biophysical methods to characterize the molecular function sites of protein superfamilies. Our tools include fuzzy functional forms (FFFs), active site profilling (DASP), PASSS, and MEAD for electrostatic analysis. The research program focuses on the following objectives: 1) characterizing the sequence and structure of functional-site features and using the results to develop methods for clustering the peroxiredoxin family; 2) analyzing the electrostatics, including ionizable residue pKas, residues affecting these pKas, and electrostatic potential, at peroxiredoxin functional sites and testing them experimentally; 3) integrating the electrostatic, sequence and structural information to create a robust profiling method that can identify peroxiredoxin subfamilies, then making it available; and 4) using it to create active-site signatures and profiles for a well-studied and important set of protein superfamilies and making these data available. Crossing the gap from molecular function to biological mechanism requires integrating sequence, structure, and physical-chemical data. The detailed functional site analysis of protein superfamilies is yielding insights into biological mechanisms, leading to hypotheses that can be experimentally tested. In the long term, the resulting methods will enable more accurate functional site identification from sequence. The development of general concepts for identifying and classifying molecular functional-site features will advance the design of enzymes with improved, altered, or novel activity, and of inhibitors (or lead compounds), an early step in the pharmaceutical drug-discovery process.
