UC Merced

Candida albicans is the most common cause of life-threatening disseminated fungalinfections. It is capable of undergoing phenotypic switching between two distinct cell types, named ‘white’ and ‘opaque’. The white-opaque switch is controlled by a complex genetic regulatory network (GRN) that consists of transcriptional regulators (TRs). The advent of methodologies for profiling mRNA transcript levels and specific protein-DNA interactions at a genome-wide level has greatly expanded our ability to determine the structure and output of genetic regulatory networks, however uncovering the logic of how these networks function remains a challenging endeavor. The field of genetic regulatory network inference aims to meet this challenge by using computational modeling to derive the structure and logic of GRNs based on the experimental data provided by these genome-wide approaches. Boolean, probabilistic, ODE-based, and other models have been developed to infer GRNs. However, most existing models do not incorporate dynamic transcriptional data, since it has historically been less widely available in comparison to “static” transcriptional data. In this work, a novel evolutionary algorithm-based ODE model that integrates kinetic data and considers “static” transcriptional profiles as attractors has been developed to infer GRN structure. The model performed well on both in-silico and real-life datasets, and it was able to predict unknown transcription profiles produced upon genetic perturbations of the Candida albicans white-opaque GRN. Those genetic perturbations can be engineered in vivo and the result can be utilized to either support or further refine the model. Therefore, the model allows for an iterative refinement strategy to decipher GRN and verify its reliability: the model facilitates GRN candidate selection for experimentation and the experimental result in turn provides validation or improvement for the model.