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Updated: Jun 12, 2026
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Reverse engineering dynamic temporal models of biological processes and their relationships.
Naren Ramakrishnan1, Satish Tadepalli, Layne T Watson
1Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.
Summary
This study introduces GOALIE, a framework to model cellular processes from gene expression data. It identifies critical time points to understand biological regulation, evolution, and disease.
Area of Science:
- Systems Biology
- Computational Biology
- Genomics
Background:
- Biological processes require synchronized events for cell function.
- Identifying critical time points is crucial for understanding biological regulation and dynamics.
- Disruptions in temporal regulation can lead to disease.
Purpose of the Study:
- To present a general framework (GOALIE) for reconstructing temporal models of cellular processes.
- To analyze time-course gene expression data for identifying critical time points and process interplays.
- To formulate hypotheses on mechanistic regulation, evolutionary variation, and disease-related dysregulation.
Main Methods:
- Developed a mathematical framework (GOALIE) for optimal dataset segmentation.
- Defined "informative" windows to capture concerted gene action and significant restructuring points.
- Applied the framework to time-course gene expression data from yeast.
Main Results:
- GOALIE successfully reconstructs temporal models of cellular processes.
- Identified interplay between yeast cell cycle and metabolic cycle processes.
- Demonstrated the framework's ability to infer temporal dynamics from gene expression data.
Conclusions:
- GOALIE provides a robust method for building temporal phenomenological representations of biological processes.
- The framework aids in understanding biological regulation, evolutionary dynamics, and disease mechanisms.
- This approach is valuable for hypothesis generation in systems biology.
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