Canalization and Other Design Principles of Gene Regulatory Network Models

About This Grant

All living organisms rely on interacting networks of genes to carry out vital functions like growth, development, and response to the environment. These networks must operate reliably even in the face of unpredictable changes, such as random mutations or environmental shifts. One key to that stability is a biological concept called canalization. This project investigates how gene regulatory networks (GRNs) achieve such stability by studying how their structure shapes their behavior. Using simple but powerful mathematical models, the research will uncover underlying design principles that make these networks robust. To achieve this, the project will analyze hundreds of previously published expert-curated GRN models using new computational tools and theoretical insights. The findings will be validated through biological experiments in a model plant species, Arabidopsis. The broader impacts of this project include the creation of public databases and software tools that will allow scientists to explore how gene networks function. By involving students in all aspects of the research, this project contributes to the interdisciplinary training of the STEM workforce. Moreover, this project has the potential to support efforts in agriculture, biology and medicine by helping scientists better understand how genetic systems function and maintain their resilience. This project seeks to elucidate design principles of GRNs using discrete dynamical systems, specifically Boolean and multistate network models. The central focus is on the biological concept of canalization, which refers to the process of creating stability in a gene regulation program despite genetic and environmental variability. This project will develop a biologically meaningful definition of canalization for multistate functions. A meta-analysis of all published, expert-curated discrete GRN models will be conducted to identify structural and dynamical features that are overrepresented compared to random expectations, including canalization, redundancy, and long-term dynamic behavior. To assess the functional relevance of identified design principles, GRNs with varying properties will be simulated, and dynamical outcomes, such as robustness and phenotypic switching, will be measured. Analysis methods will be implemented in open-source software tools, integrated with existing software libraries such as CANA and BooleanNet. Experimental validation will be performed using Arabidopsis root GRNs, revealing biological insights into correlations between phenotypes, transcription factor expression levels and root GRN robustness. This multi-faceted approach combining theory, computation, and experiment will yield new insights into how GRN topology governs dynamics and how stability is achieved through canalization and other structural features. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.