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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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Published on: February 8, 2017

Biological signaling pathways and potential mathematical network representations: biological discovery through

Clara Isaza¹, Juan F Rosas², Enery Lorenzo²

¹Public Health Program, Ponce Health Sciences University, Ponce, 00732-7004, Puerto Rico.

Cancer Medicine

|April 11, 2018

Summary

This study identifies potential cancer signaling pathways using gene expression data and network optimization. Novel methods reveal new lung cancer gene correlations, improving biological understanding.

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siRNA Screening to Identify Ubiquitin and Ubiquitin-like System Regulators of Biological Pathways in Cultured Mammalian Cells

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Published on: May 24, 2014

Area of Science:

Computational Biology
Bioinformatics
Cancer Genomics

Background:

Identifying genes crucial for cancer development is challenging.
Gene expression changes in cancer are often interconnected, suggesting pathway involvement.
Biological signaling pathways offer a more plausible framework for understanding cancer mechanisms.

Purpose of the Study:

To develop a mathematical network approach for identifying cancer signaling pathways from microarray data.
To apply network optimization models to gene expression data for pathway discovery.
To validate the identified pathways through biological evidence and meta-analysis.

Main Methods:

Preselection of candidate genes using a multi-criteria optimization framework.
Application of Traveling Salesperson Problem and Minimum Spanning Tree models for network analysis.
Computation of correlation coefficients from gene expression differences.

Main Results:

Identification of novel, biologically evidenced correlations between gene groups in different lung cancer states.
Effective utilization of network optimization techniques through correlation coefficient computation.
Generation of mathematically optimal correlation structures for gene networks.

Conclusions:

The proposed computational strategy effectively identifies potential cancer signaling pathways.
The methods provide new insights into gene correlations in lung cancer.
This approach enhances the understanding of complex biological networks in disease.