Over the last decade, our research team has investigated the dynamic responses and global properties of living cells using systems biology approaches. More specifically, we have developed computational models and statistical techniques to interpret instructive cell signaling and high-throughput transcriptome-wide behaviors of immune, cancer, and embryonic development cells.
Advances in basic and preclinical science continue to fuel the drug discovery pipeline, however only a small fraction of compounds meet criteria for approval by the FDA.
Computational modeling allows biologists to create formal models of cellular phenomena that can be simulated, analyzed and compared to experimental data. Biologists today have at their disposal a wide range of software tools for their modeling efforts. The wealth of resources is a boon to researchers, but it also presents interoperability problems.
With the explosion of data in different dimensions of drug discovery, biomedicine and healthcare, a key challenge is the ability to connect the disparate data sources, discover the right analytics tools for a specific analysis and navigate through inter-operable analytics to provide executable insights.
The identification of disease-causing genes provides information about the pathogenesis of heritable eye diseases at the most basic level. Finding the causative gene of a disease helps the patient move beyond the unknown into the world of knowing what they have, what the future might hold, recurrence risk assessment, identification of at risk family members, contact with appropriate support groups, knowledge of what else to look for, and appropriate surveillance screening, and most importantly, the new possibility of gene based treatment.
The genome found in every cell of our body contains over 20 thousand genes and over 3 billion letters of DNA that sustains life, shapes who we are and determines our risks of having a disease. CRISPR/Cas (clustered regularly interspaced palindromic repeats) is a recently discovered antiviral defence system in bacteria that has become the favorite set of tools to edit and correct any diseased genome and change any sequence of DNA in precisely chosen genomic location performed not in a test tube but within the nucleus of our living cell.
Shipworms are marine bivalves that live and feed on wood. These bivalves, like most xylophagous and herbivorous animals, rely on bacterial symbionts to digest the recalcitrant lignocellulose component of plants. What’s unusual about shipworms is that bacterial symbionts are housed intracellularly in the specialized cells in the gills, therefore are not in direct contact with the ingested food particles.
Aberrant TGFβ signaling pathway may alter the expression of down-stream targets and promotes ovarian carcinogenesis. However, the mechanism of this impairment is not fully understood. By ChIP-chip and expression microarray, we have previously identified several SMAD4 targets in an immortalized ovarian surface epithelial cells. Bioinformatic analyses identified several SMAD transcriptional modules that predict expression changes after TGF-β activation. Knockdown of SMAD4 in CP70 ovarian cancer cells showed an increase in promoter methylation in some of those SMAD4 targets as demonstrated by sequencing-based analysis.
Metastasis claims 90% of all cancer-related deaths and remains clinically insuperable. The hallmarks of metastases are processes known as Epithelial to Mesenchymal Transition (EMT) and its reverse Mesenchymal to Epithelial Transition (MET) that enable primary carcinoma cells to migrate and start new tumors at distant organs. I will present an integrated theoretical and experimental approach that elucidates how cancer cells undergo EMT and MET, and how these transitions affect their ability to initiate new tumors.
Bioinformatics can play an important role in infectious disease surveillance and research from epidemiological data processing with geographic and temporal visualization to comparing genome phylogenies and structural modelling of mutations. As a classical example, interest in new influenza outbreaks as well as regular surveillance of circulating seasonal strains produce a constant flow of influenza genome sequences that need to be analysed and interpreted for epidemiological and phenotypic features.