Computational and Systems Biology

Research includes:

  • Systems Biology
  • Bioinformatics
  • Computational Biology
  • Computational Chemistry
  • Quantitative Biology
  • Synthetic Biology
  • Genomics
  • Quantitative Genetics and Proteomics

Training Group Mission:

The mission of the Computational and Systems Biology (CSB) group is to train a new generation of highly skilled and interdisciplinary graduate students who are competent in multiple disciplines in biology and the quantitative sciences. The objective is that students will be able to navigate between disciplines and be highly proficient scientists operating at the interface of the life sciences and quantitative sciences.

Faculty Membership

Research Area
Information processing in neural circuits, primarily in the auditory system, in normal and pathologic conditions.

Chemical Immunology: Cell specific chemical perturbation of immune microenvironments in cancer, neurological and immunological disorders

Sensorimotor integration and neuroplasticity; neural prostheses
Chemical and systems biology as applied to drug discovery; design, synthesis, and evaluation of small molecule modulators of protein interactions; development and application of high content cell analysis screening platforms.
Genetic and genomic investigation of naturally-occurring canine diseases and traits
Environmental and molecular toxicology (developmental toxicology, developmental neurotoxicology, neurotoxicology), Developmental origin of health and disease, Genome and epigenome alterations, Molecular cytogenetics, Neuroendocrine dysfunction, Neurodegenerative diseases, Toxicogenomics, Zebrafish model system
Structural basis for RNA function
Macromolecular sequences and the evolution, structure and function of molecules; databases and computational tools for functional genomics
Systems biology investigation of eukaryotic N-terminal methylation. Metabolomics and chemogenomics analysis of Candida albicans gastrointestinal colonization. Chemogenomics of Candida and Saccharomyces.

Lipid metabolism at a biochemical level and in vivo, in the context of metabolic disease, exercise, and responses to diet.

method developments and applications of cryo-EM

Our lab focuses on acquiring and utilizing high throughput sequencing data (e.g. RNA-seq, ChIP-seq, ATAC-seq) to develop new computational models and biological assays to study genome regulation and human diseases, in particular immune related disorders and cancer. We are now working on the discovery and modeling of the regulatory circuitry of the non-coding genome which is essential for maintaining normal cellular physiology.

We develop computational methods for analyzing and modeling protein structures, functions, drug molecules, cryo-EM and other biomedical image data.

We use translationally relevant preclinical animal models of substance use disorder to dissect how the brain responds to acute and chronic drug use. We pair behavioral models with whole-brain imaging of protein signaling and computationally based neural network analysis. These approaches can help us to better understand addiction and develop better treatment options.

Bionanotechnology and biosensors
Systems biology of host-pathogen interactions; dengue virus; malaria parasites; protein-protein interactions
Drug discovery for retinal degeneration Retinal degeneration is a group of inherited eye diseases including retinitis pigmentosa and age-related macular degeneration that impair our vision. Although much has been learned about the molecular basis of these diseases, they are still incurable. To expedite discovery of new drugs for these diseases, our group at Purdue University studies zebrafish retinal-degeneration models. We screen drug libraries on these models using high-throughput visual-behaviour assay. We develop novel statistical and machine-learning tools to analyze the large-scale behavioural data, and to to determine which drugs helped the retinal-degeneration models. We then study how the positive drugs work at molecular, genomics and cell-biology levels.
Computer-assisted drug design
Dietary controls on the gut microbiome, host-microbe and microbe-microbe metabolic exchange, gut inflammation and enteropathogenesis
Coding, modeling, computing, and simulating to develop new, effective, and safe drug products.
System-wide Investigation of protein folding, energetics, and ligand binding
The Paschou lab works at the intersection of Data Science and genomics research. We study human genetic variation around the world aiming to understand the cause of neurological and neuropsychiatric disorders as well as the factors that have shaped human population structure. We have a special focus on neurodevelopmental disorders of childhood and analyze large scale genomic data in order to identify genes that lead to symptom onset. As part of the ENIGMA consortium, we also analyze brain neuroimaging and genetic data, investigating brain structure and function in disease.

Computational systems biology and systems pharmacology focused on within-host dynamics.

Computational chemistry and biological NMR
We study the structure-spectrum relationship in cyanobacterial photosynthesis using mutagenesis and optical spectroscopy.
Neurotoxicity and thyroid toxicity of per- and polyfluoroalkyl substances (PFAS) on Xenopus and leopard frogs.
Computational and experimental analyses of multi-scale growth control: from protein complexes to cells, tissues, and organs.
Defining the Molecular Basis for p68 (Dbp2) in Gene Expression and Cellular Proliferation
Mathematical biology and applied math (applications include pattern formation and self-organization, and methods include modeling, computation, and topological techniques)
Dr. Wedow's main research interest is in social science and behavioral genetics, which lies at the intersection of sociology, psychology, demography, and statistical & computational genetics. He is interested in large-scale genomics analyses, and also in how social forces and environments interact with genetics. He uses recent advances in genetic data collection, massive biobank-scale data, and methodological developments in statistical genetics to carry out his research.
Evolution of eukaryotic chemodiversity using genomics, network biology, and phylogenetics

Specialization: pharmacogenomics, ion channels, electrophysiology, induced pluripotent stem cells (iPSCs), neurological diseases (e.g., chronic pain, epilepsy, and autism)

Cancer genetics and genomics, Developmental Biology.
Bioinformatics and Biologically Related Disciplines (genomics, nutrition, proteomics, statistical genetics)

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