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Chronic inflammation is a prolonged, dysregulated immune response that persists for weeks to years when the inciting stimulus is difficult to eradicate or when self‑antigens drive ongoing reactivity. Morphologically, it is defined by mononuclear cell infiltration, progressive tissue destruction, and concurrent attempts at healing via angiogenesis and fibrosis. Compared with acute inflammation, edema is less prominent while cellular infiltration predominates; triggers include persistent...
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NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION.

P T Foteinou1, E Yang, I P Androulakis

  • 1Biomedical Engineering Department, Rutgers University, 599 Taylor Road Piscataway, NJ 08854.

Computers & Chemical Engineering
|February 18, 2010
PubMed
Summary
This summary is machine-generated.

This study addresses critical challenges in computational systems biology by constructing transcription factor networks and identifying functional modules for disease progression, focusing on inflammation. The research aims to uncover key components and interaction rules governing biological system behavior and host responses.

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Area of Science:

  • Computational systems biology
  • Network biology
  • Molecular systems biology

Background:

  • Biological systems are complex networks with emergent behaviors.
  • Identifying critical components and interaction rules is key to understanding biological systems.
  • Inflammation is a crucial physiological response with significant clinical relevance.

Purpose of the Study:

  • To address fundamental problems in constructing transcription factor networks.
  • To identify networks of functional modules that describe disease progression.
  • To focus on inflammation as a model system for host response.

Main Methods:

  • Network construction methodologies for transcription factors.
  • Module identification techniques for disease progression analysis.
  • Systems biology approaches applied to inflammation.

Main Results:

  • Development of methods for building transcription factor regulatory networks.
  • Identification of functional modules associated with disease progression.
  • Insights into the network dynamics of inflammation.

Conclusions:

  • The study provides foundational methods for network construction and module identification in systems biology.
  • Understanding these networks is crucial for deciphering host responses and disease mechanisms.
  • The focus on inflammation highlights the translational importance of network-based approaches.