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.
