Multi-Timescale Complex Adaptation

Abstract

The overall goal of the project was to develop structured approaches to modeling complex gene regulation dynamics underlying cellular adaptation in mammalian systems. This involves integration of two erstwhile disjoint aspects: mathematical modeling and bioinformatics of high-throughput biological data. As part of a structured approach to tackle this problem, we have developed methods and software tools for identification of 1) robust patterns of gene expression using a meta-clustering approach, 2) network structures from these patterns, and 3) a continuous-time regulatory network model based on temporally discrete gene expression data and predicted network structures. A web-based, graphical user interface was developed for the network structure prediction software, PAINT, and has been released as a DARPA BioSPICE module. We have successfully employed our structured approach in the study of various gene regulatory networks from in silico model systems, yeast cell cycle, neuronal differentiation and adaptation, circadian rhythms, and cellular response to pathogens.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA456466

Entities

People

  • James Schwaber
  • Praveen Chakravarthula
  • Raj Vadigepalli

Organizations

  • Thomas Jefferson University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cell Physiological Processes
  • Cells
  • Computational Biology
  • Computational Science
  • Data Mining
  • Fungi
  • Graphical User Interface
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Proteins
  • Rna Stability
  • Sequence Analysis
  • Systems Biology
  • Systems Engineering

Fields of Study

  • Biology

Readers

  • Computational Fluid Dynamics (CFD)
  • Database Systems and Applications
  • Molecular Genetics