Colorectal cancer: New model aims to identify critical risks in human diet

A 3D rendered image of cells. One of the cells has dark tubules protruding from it as compared to the other clear cells.

A new federally funded project intends to develop a gut-microbial investigation model that can identify critical dietary risk factors that cause colorectal cancer. Researchers at Texas A&M University aim to develop and apply innovative signal processing techniques to uncover the complex interactions among microbes, human cells and their metabolic products in the gut.

To fund the project, the interdisciplinary team of researchers received a Division of Computing and Communication Foundations grant from the National Science Foundation. The three-year, $350,000 project is a direct outcome of the Texas A&M Engineering Experiment Station’s Interdisciplinary Seed Grants for Strategic Initiatives, which provided initial funding to establish the collaborative research effort.

The project will produce innovative methods for estimation and control of processes that consist of the complex interactions of many switching elements, such as “presence” and “absence” of a particular microbial species in the gut, which are only indirectly observed through noisy biomedical assays.

“Our goal is to develop a signal processing framework that formalizes the interactions of the complex ecosystem observed in the human gut such as the microbial communities and their interactions with the gut epithelial cells,” said Ulisses M. Braga-Neto, associate professor in the Department of Electrical and Computer Engineering at Texas A&M College of Engineering as well as the principal investigator of the project. “This framework will allow us to study the effects of nutritional supplementation on this complex ecosystem in terms of changes in the microbial diversity and human gut gene expression in cell-signaling pathways.”

The project will provide life scientists with computational tools for biochemical pathway discovery as well as rational intervention design, as in optimal drug scheduling and diet modifications to treat human disease.

More at the Texas A&M Engineering Experiment Station

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