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Sungchul Ji

Sungchul Ji

Associate professor, Department of Pharmacology and Toxicology

Title: Mathematical models of RNA expression profiles and their potential applications to drug discovery research.

Biography

Sungchul Ji received a Ph. D. degree in physical organic chemistry in 1970 from the State University of New York at Albany and carried out postdoctoral researches in enzymology, biophysics, systems physiology, and toxicology at the University of Wisconsin (Madison), University of Pennsylvania School of Medicine, Max Planck Institute of Systems Physiology at Dortmund (Germany), and the University of North Carolina School of Medicine, before joining Rutgers University School of Pharmacy in 1982 as an associate professor. His decades-long research has resulted in a book entitled Molecular Theory of the Living Cell: Concepts, Molecular Mechanisms, and Biomedical Applications, published in 2012.

Abstract

During the past five years, we have identified two mathematical equations that quantitatively account for the genome-wide RNA level data measured from (i) budding yeast undergoing nutritional stress and (ii) the breast cancer tissues of 20 patients before and after treating with anticancer drug, doxorubicin [1, 2, 3]. The Poisson distribution formula was found to fit the probability of observing either the beneficial or the harmful RNA expression patterns exhibited by 988 genes in a given number of breast cancer patients, from which a quantitative measure of the efficacy of doxorubicin, referred to as the “micro-therapeutic index (mTI)”, could be calculated, i.e., mTI = 2.5. The same RNA level data set was found to fit another mathematical equation called BRE (blackbody radiation-like equation) that was derived from the blackbody radiation formula discovered by M. Planck in 1900 : y = (a/(Ax + B)^5)/(e^(b/(Ax + B)) -1 ), where y = the probability of observing RNA levels within a given range, x = the ranges of RNA levels. Using the Solver program, we determined the numerical values of the parameters, a, b, A and B, that best fit BRE to the RNA level data. These parameter values were found to different between the RNA data measured before (BE) and after (AF) the drug treatment. This finding supports the notion that BRE can be used as a quantitative method to characterize the effects of drugs on genome-wide RNA metabolism in both normal and diseased cells, thus providing a novel strategy for drug discovery.

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