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Updated: May 27, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Integer programming-based method for completing signaling pathways and its application to analysis of colorectal
Takeyuki Tamura1, Yoshihiro Yamanishi, Mao Tanabe
1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. tamura@kuicr.kyoto-u.ac.jp.
Abstract:
Signaling pathways are often represented by networks where each node corresponds to a protein and each edge corresponds to a relationship between nodes such as activation, inhibition and binding. However, such signaling pathways in a cell may be affected by genetic and epigenetic alteration. Some edges may be deleted and some edges may be newly added. The current knowledge about known signaling pathways is available on some public databases, but most of the signaling pathways including changes upon the cell state alterations remain largely unknown. In this paper, we develop an integer programming-based method for inferring such changes by using gene expression data. We test our method on its ability to reconstruct the pathway of colorectal cancer in the KEGG database.
Insights
This study introduces a new computational method to map changes in cellular signaling pathways. The approach uses gene expression data to identify altered protein interactions in diseases like cancer.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Cellular signaling pathways are crucial for biological functions and are often modeled as networks of protein interactions.
- Genetic and epigenetic alterations can modify these pathways, leading to disease states.
- Existing databases contain known pathways, but dynamic changes in response to cellular alterations are largely uncharacterized.
Purpose of the Study:
- To develop a novel computational method for inferring alterations in signaling pathways.
- To identify changes in protein-protein interactions and regulatory relationships within cellular networks.
- To apply the method to understand pathway modifications in disease contexts.
Main Methods:
- Development of an integer programming-based algorithm.
- Utilizing gene expression data as input for pathway reconstruction.
- Testing the method's efficacy on a known cancer pathway from the KEGG database.
Main Results:
- Successfully inferred changes in signaling pathways using gene expression data.
- Demonstrated the method's capability to reconstruct known pathways, such as that of colorectal cancer.
- Provided a framework for identifying dynamic pathway alterations.
Conclusions:
- The developed integer programming method is effective for inferring signaling pathway changes.
- This approach can help uncover disease-specific pathway modifications.
- The method offers a valuable tool for systems biology and disease research.
