Developing algorithms in Computational Biology, Bioinformatics, and Medicine
The general focus of Dr. Valafar’s current research is the application and transfer of engineering techniques to biological systems. In addition, this transfer of information can be reversed to implement efficient optimization and security (immunity) techniques inspired by biological models. His specific research is divided into three main categories: Computational Biology, Bio/Health Informatics and Computational Medicine.
Our specific interest in the area of Computational Biology is the problem of protein folding. Current approaches to protein folding are segregated into either experimental or computational means of structure determination. While traditional methods of structure determination remain to be the most reliable approaches to characterization of structure and dynamics of biomolecules, they have imposed certain tangible limitations on the whole process of rapid and accurate structure determination. On the other hand, the recently emerging approaches to computational structure determination offer a very cost effective means of structure determination while suffering from unconfirmed results. Our approach to protein folding is a hybrid approach that uses AI/machine learning techniques along with advanced optimization and is capable of interfacing with traditional experimental data.
Funding for our current projects come from NSF.
Since the advent of the Human Genome Project, the availablity and use of sequencing technologies have skyrocketed. Online services like 23andMe allow an average person to obtain partial sequencing of their DNA to gather explore ancestry along with genetic markers. As a result, large amounts of genetic data are being collected and becoming available every day. One focus of our lab is develop methods to analyze this data and, in collaboration with researchers around the world, report unique genetic insights with potentially profound consequences. In the course of the last several years we have developed methods in differential gene expression, transcriptome reconstruction and annotation, as well as whole genome studies.
Funding for our current projects come from NSF and NIH.
Current investigation of the molecular basis for a number of diseases have been unsuccessful. This lack of success has steered the future investigation in the study of disparate data in order to understand the relationship between the environment and the genome. In our view a very important aspect of this interaction is neglected. That is the temporal component of the interaction. Examples borrowed from controls in the design of stable systems stress the importance of temporal component in the modeling of any electrical circuit. The same argument and therefore engineering techniques should be transferable to the study of stable biological systems. Our long term goals of study is therefore a temporal effects of external stimulants on the behavior of cells with given genome. Our short term goal is to develop a physiological state response of individual patients. Our studies include response of Sickle Cell Anemia patients to Hydroxyurea and study of differential expression profiling in patients suffering from cervical cancer. Our lab has also delved into other projects that aim to incorporate technology (especially smartwatch and mobile apps) and computer science concepts into the medical field. In general, researchers who wish to evaluate medical systems and intervention techniques are forced to resort to more primitive ways of evaluation (hand written surveys, interviews, etc.) It is our goal to create specialized systems that will help researchers tackle huge medical questions with ease.
Funding for our current projects come from NSF, NIH and AHA.
