Full Time Faculty
The research activities in this laboratory concern the development of novel methods for structural and functional imaging of tissues and cells (based on Optical Coherence Tomography and Optical Elastography techniques).
The research in the Blood Microfluidics laboratory (Prof. Sergey Shevkoplyas) is focused on development and clinical translation of high-throughput microfluidic devices and single-cell analysis tools in the field of blood storage and transfusion medicine. Our goal is to develop technology for eliminating mediators of toxicity from stored blood, and for separating whole blood into components for transfusion in resource-limited settings. A significant additional thrust of our research efforts is the development of low-cost point-of-care diagnostics (e.g., for sickle cell disease).
Ongoing Research Projects in the Mohan Laboratory include:
- Identifying novel biomarkers for lupus, arthritis and related rheumatic autoimmune diseases
- Identifying key cellular and molecular players in the pathogenesis of lupus and arthritis, and targeting these pathways therapeutically
- Developing novel technologies for non-invasive monitoring of lupus nephritis, including the monitoring of markers in body fluids, as well as diverse imaging modalities
- Identifying the cellular, molecular and genetic players in immune-mediated nephritis
- Exploring the use of mesenchymal stem cells and nanoparticles as vehicles for drug delivery to inflamed end-organs
- Exploring the impact and mechanism of action of nutritional agents and alternative medicines in autoimmunity and chronic diseases
Research interests of our group include: Development of multi-scale models and simulation of biological pathways and systems; use of simulation-based models of host-pathogen interactions to understand molecular mechanisms of pathogenesis and disease; development of integrated quantitative/empirical platforms to enable predictive modeling and simulation of host-pathogen and multicellular interactions by enabling acquisition of high-resolution kinetic, whole-cell data; the use and application of information theory, coding theory, and signal processing to the analysis of genetic regulatory mechanisms; algorithm development for computational biosensors for detection and classification of polymorphisms, microbial identification and strain classification
My research in image science is devoted to medical imaging, primarily emphasizing the development, assessment, and optimization of imaging systems for detecting cancer. One branch of the work is concerned with devising reliable models for predicting the diagnostic utility of new clinical imaging technology. The second and more expansive branch of work is directed at actually applying these predictive models to design and optimize diagnostic imaging systems. Current areas of interest include gamma-ray imaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and x-ray digital tomosynthesis (DT).
The activities in my lab include a variety of basic and translational research in the rapidly growing area of neural engineering and biomedical signal processing. Areas of special interest are: neural decoding for neuroprosthetics; machine learning for neuromarker discovery in cognitive and movement disorders; development of embedded wearable wireless sensors and their integration to intelligent systems for healthcare and assisted living. In particular, we develop novel algorithms and machine learning techniques to explore neural activity recorded in clinical setting. My lab focuses on research that contributes not only to algorithm development but also to the discovery of new methods for diagnosis and therapy that can be applied in clinical practice. In this scheme, our group works closely with clinicians and researchers from diverse fields such as neuroscience, neurosurgery and neurolog
My laboratory has the following research focus: (1) Design and fabrication of novel detection systems for disease diagnostics, this includes protein separation systems, protein chips, nanomaterial-based fluorescent/NIR probes, and surface chemistry for protein binding. The goal is to tackle the existing technological challenges in effective detection of low-abundant proteins and post-translational modified proteins in complex biological samples, especially when these proteins are critical in the pathogenesis of chronic diseases. The development of these novel technologies will aid in high-throughput discovery of early biomarkers, non-invasive biomarkers and therapeutic targets for chronic diseases. In addition, the development of polymer nanofiber based biosensors for biomarker detection has also become of our research interest. (2) Development of versatile and biocompatible nanomaterials for drug delivery to improve bioavailability, effective targeting and controlled release of drugs for chronic diseases. This includes prodrugs and combinatory medicine---the combination of thermotherapy/drug/gene therapy. The goal is to tackle the problems of drug resistance and side-effects commonly seen in today’s medicine for chronic diseases.
Joint Appointment Faculty
Research interests of our group consist of several theoretical, statistical, and analytical topics within the broad area of evolutionary bioinformatics, genomics, and computational biology. In particular, we are working towards the elucidation of the relative roles of mutation and drift versus purifying selection in determining the pattern of nucleotide substitution. A major part of this study involves studies of the methodology of inferring selection from comparative genomic data. We are also working towards producing dynamic and static descriptions of the compositional features of genomes. Other projects include the study of compositionally homogeneous regions within genomes, the assessment of the efficiency and efficacy limits of alignment and phylogenetic reconstruction methods, assessing of the relative contributions of different molecular mechanisms in generating gaps (deletions and insertions) in DNA evolution, and the genomics of mobile elements.