Computational Modeling and Analysis of Hepatitis C Pathway using Static Analysis Approach to Identify Critical Biomarkers and Drug Targets for Effective Diagnosis

Abstract

Hepatitis C Virus is the vital etiological agent of one of the fatal diseases Hepatitis, leading towards hepatic infiltration with fats known as steatosis followed by fibrosis, cirrhosis, HCC hepatocellular carcinoma and ultimately functional collapsing of the liver. Hepatitis C Virus infection is one of the world-wide indispensable health burdens primarily, in the developing countries like Pakistan where 10 million cases account for HCV. Based on nucleotide transpositions, HCV genome tends to have high mutation rate, resulting in its classification to no less than six genotypes and further subtypes. This categorization impacts the clinical profiles of the patients along with intensity of the liver disease and treatment response to interferon alpha- ribavirin therapy. In Pakistan, genotype 3a is the most prevalent. The past decade has witnessed tremendous breakthrough in comprehending HCV biology and its allied disease hepatitis. Computational methodologies have greatly reduced both the escalating research and development (R&D) costs and raising development times, by sighting up to date research models. Here in this research by deploying the tractability of Process hitting over large complex biological system of Hepatitis C Virus infection pathway, we have implemented the software Pint that works under the parasol of Process Hitting framework. Numerous findings have been enlightened by this phenomenal computational technique, including an elaborated list of novel Bio-markers specifically inclusive of AKT with the onset of enhanced proliferation along with steatosis that marks down the presence of HCV infection. It also acquainted us with the proof that how applicability of cut set on AKT enables restoring the homeostatic balance of apoptosis in the diseased state. The significant drug targets are having been identified inclusive of PDPK1, PIP3, LXR-alpha, PA28-gamma and PI3K along with others that ameliorates the basis for therapeutic strategies to combat HCV disease. This technique has promising attributes of trend setting in the field of computational systems biology by undertaking the colossal biological automata networks and enabling xi interpretable models.