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Published on: July 22, 2020
The use of logic relationships to model colon cancer gene expression networks with mRNA microarray data
Xiaogang Ruan1, Jinlian Wang, Hui Li
1College of Electronic Information and Control Engineering, Beijing University of Technology, Pingleyuan 100#, Chaoyang District 100022, PR China. adrxg@bjut.edu.cn
Abstract:
The ultimate goal of genomics research is to describe the network of molecules and interactions that govern all biological functions and disease processes in cells. Nonlinear interactions among genes in terms of their logic relationships play a key role for deciphering the networks of molecules that underlie cellular function. We present a method based on a graph coloring scheme and information theory to identify the gene expression network with lower and higher order logic interactions of genes. The analysis of oncogenes and suppressor genes from a colon cancer mRNA microarray dataset identifies a gene expression network with directionality and weights that reflects intracellular communication pathways. The success of the proposed method in mining hidden, complicated gene interactions and reliably interpreting experimental results suggests that the proposed method is a useful tool for understanding cancer systems. Extension of this method holds the potential to be fruitful for understanding other complex, nonsymmetric systems.
Insights
This study introduces a novel graph coloring and information theory method to map gene interactions in cellular functions. The approach successfully identifies complex gene networks, aiding in understanding cancer systems.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Understanding molecular networks is crucial for deciphering cellular functions and disease processes.
- Nonlinear gene interactions are key to uncovering these complex networks.
- Existing methods may struggle with identifying higher-order logic interactions.
Purpose of the Study:
- To present a novel method for identifying gene expression networks, including lower and higher-order logic interactions.
- To analyze gene interactions in colon cancer using a microarray dataset.
- To reveal intracellular communication pathways through gene network analysis.
Main Methods:
- Utilized a graph coloring scheme combined with information theory.
- Applied the method to a colon cancer mRNA microarray dataset.
- Focused on oncogenes and suppressor genes to identify network properties.
Main Results:
- Successfully identified a gene expression network with directionality and weights.
- The network reflects intracellular communication pathways.
- The method effectively mined hidden and complicated gene interactions.
Conclusions:
- The proposed method is a valuable tool for understanding complex biological systems, particularly cancer.
- It reliably interprets experimental results by uncovering intricate gene interactions.
- The approach has potential for broader applications in other complex, nonsymmetric systems.
