Systems Biology (Methods in Molecular Biology)

Chapter 12

A Network-Based Integrative Workflow to Unravel

Mechanisms Underlying Disease Progression

Faiz M. Khan, Mehdi Sadeghi, Shailendra K. Gupta, and Olaf Wolkenhauer

Abstract

Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches.
The key challenges for the development of mathematical models and computational tool are (1) the size of
molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the
structure of interaction networks. We here propose an integrative workflow that combines structural
analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow,
we use prostate cancer as a case study with the aim of identifying key functional components associated with
primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and
PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state,
STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are
validated using patient survival analysis. The workflow presented here allows experimentalists to use
heterogeneous data sources for the identification of diagnostic and prognostic signatures.

KeywordsIntegrative workflow, Network-based analysis, Large-scale networks, Disease signatures,

Mathematical models

1 Introduction

To understand various processes associated with the progression of

complex diseases, systems biology-based methods usually begin

with the gathering of information from the literature and databases,

summarizing components and their interactions relevant for the

process under consideration. The information gathered is summar-

ized in interaction maps, which serve as a knowledge-base and

being machine readable is amenable to computational analysis.

Tumor is one of the complex diseases where mutated and epigenet-

ically modified genes are highly patient and tumor type dependent,

and more importantly these genes are integrated in a small set of

regulatory pathways [1–3].

Mariano Bizzarri (ed.),Systems Biology, Methods in Molecular Biology, vol. 1702,
https://doi.org/10.1007/978-1-4939-7456-6_12,©Springer Science+Business Media LLC 2018

Faiz M. Khan, Mehdi Sadeghi and Shailendra K. Gupta contributed equally to this work.

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