Over the past 20 years, researchers in biology and medicine have created Boolean network models to simulate complex systems and find solutions, including new treatment methods for colorectal cancer.
Boolean network models assume that each gene in a regulatory network can have one of two states: on or off.“
– Claus Kadelka, System Biologist and Associate Professor of Mathematics at Iowa State University
Kadelka and student researchers recently published a study unraveling the common design principles in these mathematical models for gene regulatory networks. He states that representing the features that have evolved over millions of years can „guide the process of accurate model building“ for mathematicians, computer scientists, and synthetic biologists.
Gene regulatory networks determine what happens in an organism and where it happens. They can also explain why certain actions occur in specific cells, for example, why cells in your stomach lining stimulate acid production but not in your eyes, even though all cells in your body contain the same DNA.
Kadelka discusses the complexity of gene regulatory networks, using the example of a simple hypothetical gene regulatory network. He also highlights various design principles and emphasizes the importance of „canalization“ as one of the most common principles in these networks.
Accessible Data Supported by Undergraduate Research
Kadelka emphasizes that the project, which involved scanning 30 million biomedical journal articles to identify Boolean biological network models, would have been challenging without the First-Year Mentor Program that brings together student researchers from the Iowa State Honors Program.
Undergraduate students assisted Kadelka in developing an algorithm to scan biomedical journal articles and developed an online database for the project, enhancing data accessibility and usability of the analysis tools.
Kadelka also plans to maintain and update the website and continue investigating why evolution selects specific design principles in gene regulatory networks. Meanwhile, the student researcher, Addison Schmidt, appreciated Kadelka’s mentorship, learning to expand his expertise in programming language Python while working as a freshman researcher.
Kadelka, C., et al. (2024). A meta-analysis of Boolean network models reveals design principles of gene regulatory networks. Scientific Advances. doi.org/10.1126/sciadv.adj0822.