Dr. Faiz Muhammad Khan

Developing integrative workflows that combine network structure, high throughput and biomedical data and dynamical models to analyze large-scale biochemical networks in complex diseases.

Research interest

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 vitro by 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.


Research Projects

SASKit: Senescence-Associated Systems diagnostics Kit for cancer and stroke

A European standardization framework for data integration and data-driven in silico models for personalised medicine.



AIR: Atlas of Inflammation Resolution

The AIR is to provide an interactive platform connecting scientific and medical communities.


Academic background

2012 - 2019

PhD in Systems Biology and Bioinformatics,
University of Rostock, Rostock/Germany

2009 - 2012

Master of Science in Computational Engineering,
University of Rostock, Rostock/Germany

2003 - 2008

Bachelor of Science in Computer Systems Engineering,
NWFP University of Engineering and Technology Peshawar, Peshawar/Pakistan


Selected publications

A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression

Salahshouri P, Emadi-Baygi M, Jalili N , Khan F.M, Wolkenhauer O and Salehzadeh-Yazdi A

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

Catherine Bjerre Collin, Tom Gebhardt, Martin Golebiewski, Tugce Karaderi, Maximilian Hillemanns, Faiz Muhammad Khan, Ali Salehzadeh-Yazdi, Marc Kirschner, Sylvia Krobitsch, EU-STANDSPM consortium, Lars Kuepfer

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.


accepted in MDPI Journal of Personalized Medicine

The Atlas of Inflammation-Resolution (AIR)

Serhan CN, Gupta SK, ... , Smita S, Schopohl P, Hoch M, Gjorgevikj D, Khan FM, Brauer D, ... , Wolkenhauer O

A systems appraoch to investigate inflammation resolution by multicomponent medicinal product TR14

Schopohl P, Smita S, Khan F, Gebhardt T, Hoch M, Brauer D, Cesnulevicius D, Schultz M, Wolkenhauer O, Gupta S

A web platform for the network analysis of highthroughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy

Dreyer FS, Cantone M, Eberhardt M, Jaitly T,Walter L, Wittmann J, Gupta SK , Khan FM, Wolkenhauer O, Pützer BM, Jäck HM, Heinzerling L, Vera J

Biochimica et Biophysica Acta;2018 Feb 23. pii: S0925-4439(18)30031-0.

Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures

Khan FM, Marquardt S, Gupta SK,Knoll S, Schmitz U,Spitschak A, Engelmann D, Vera J, Wolkenhauer O & Pützer BM

Nature Communications

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

Sadeghi M, Ranjbar B, Ganjalikhany MR, Khan FM, Schmitz U, Wolkenhauer O, Gupta SK

PLoS One. 2016 Dec 22;11(12):e0168760.

Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic

Khan FM, Schmitz U, Nikolov S, Engelmann D, Pützer BM,Wolkenhauer O, Vera J

Biochim Biophys Acta; 1844 (1 Pt B): 289-98.

Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network

Vera J, Schmitz U, Lai X, Engelmann D, Khan FM, Wolkenhauer O, Pützer BM (2013)

Cancer Research,

Integrative workflow for network analysis

Faiz M. Khan, Shailendra K. Gupta, Olaf Wolkenhauer


A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression.

Khan FM, Sadeghi M, Gupta SK, Wolkenhauer O

Detection of potential drug targets in cancer signaling by mathematical modelling and optimization

Khan FM


Teaching Experience

21-23 Feb 2018

Provided training on logic-based modeling at the EU- and BMBF-funded 3rd OpenMultiMed Training School, Erlangen, Germany


EX: Biosystems Modelling and Simulation (Systems Biology II)


EX: Modeling and Simulation with applicatio to the Life Sciences (Systems Biology I: Nonlinear systems theory with applications to biology)


EX: Biosystems Modelling and Simulation (Systems Biology II)

WS16/17 EX: Introduction to High Performance Computing