Faiz Muhammad Khan

I’m analyzing large-scale biological networks to discover and characterize key regulatory mechanisms underlying complex diseases, such as cancer and inflammation.

In order to investigate complex diseases, interdisciplinary collaborations usually begin with the gathering information from literature and databases, summarizing components and their interactions relevant for the processes under consideration. The information gathered is mapped out in large-scale interaction maps, which serve as a knowledge-base and being machine readable are amenable to computational analysis. Studying a large-scale interaction map as a non-linear dynamical system is challenging due to the large number of components which make parameter estimation difficult and generating identifiability problems.

To address this problem, I recently developed an integrative workflow which combines network structure with high throughput and other biomedical data and dynamical theory to analyze large-scale networks for discovery and characterization of regulatory pathways (here we call it core-regulatory network) in complex disease. Using logic-based model, we successfully identified molecular signatures for tumor invasion in bladder and breast cancer which were validated through patient data and with in vitroby our experimental partner from Institute of Cancer Research Uni Rostock.

In another study, published in Cancer Research journal, we used kinetic modeling to understand the mechanism of chemoresistance mediated by transcription factor E2F1. Model explains the dynamics of regulatory pathway, mainly constituted by E2F1-p73/DNp73-miRNA205, in chemoresistance in melanoma cancer.

I’m also interested in developing strategies that combine different modeling formalisms to model large-scale, non-linear biological systems. Towards this I proposed hybrid modeling framework, published in BBA-protein and proteomics, which combines ODE-based modeling with logic-based modeling. Hybrid model provides good compromise between quantitative/qualitative accuracy and scalability when considering large networks.

Computational analysis helps to understand processes in diseases in a mechanistic way, ultimately provides the ability to manipulate and optimize processes towards treatment.