Systems Biology (Methods in Molecular Biology)

(Tina Sui) #1
Analyzing the structure of biochemical disease networks pro-
vides useful information including network hubs, regulatory
motifs, and possibly global features like small world organization
of the system. Regulatory motifs, feedback, and feedforward loops
are a source for nonlinear regulatory behavior [4, 5], which not
only challenges human intuition but also limits the application of
conventional data analysis tools [6–8]. Moreover, for large-scale
biochemical networks a dynamical analysis is particularly difficult
with mechanistic (e.g., ODE-based) approaches from the theory of
dynamical systems. In order to exploit the advantages of large-scale
biochemical networks, in combination with mechanistic modeling,
we need integrative approaches and computational workflows to
identify disease-specific small regulatory/function modules that
can be subjected to a more detailed analysis, followed by the
prediction of molecular signatures. Exploring large-scale nonlinear
dynamical networks will remain an art form. What we are aiming for
here is a rational approach to what is effectively guesswork, forced
upon us by the wonderful complexity found in living systems.
In this chapter, we highlight and discuss an integrative work-
flow (Fig.1) to study large-scale biochemical disease networks by
combining techniques from bioinformatics and systems biology.
Integrating experimental and clinical data with the workflow,
process-specific hypotheses can be generated and validated. In
particular, we present here a flexible and extendible workflow that
combines network structural properties with high-throughput and
other biomedical data to identify smaller modules/molecular sig-
natures for tumor-specific disease phenotypes. A mathematical
model of the identified smaller modules/molecular signatures can
be constructed to give mechanistic understanding of the disease
and propose new hypotheses, which are subject to experimental
validation. For the illustration of the workflow, we used prostate
cancer as a case study with the aim of identifying key functional
components involved in regulation and progression of primary to
metastasis transitions [9]. We construct a network for each of the
clinical states of prostate cancer based on differentially expressed

Structural
analysis

Mathematical
modelling
constructionNetwork Validation


  • Literature mining

  • Databases

  • Domain knowledge

    • Centrality measures

    • Network motifs

    • Gene prioritization

      • Network analysis

      • Biomedical properites

      • Gene expression data






Regulatory
network
elements


  • Regulatory elements
    for prostrate cancer

    • ODE based model

    • Logic-based model

    • Hybrid model



  • Predictions of disease
    molecular signatures

  • Identification of potential
    drug targes

  • Clinical data validation

  • In vitro validation
    Multi-objective
    function


Graph theory

Interactions map - Validated molecular
signatures


  • Validated drug targets


Fig. 1An integrative workflow to analyze large-scale biochemical disease networks


248 Faiz M. Khan et al.

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