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| Status |
Public on Jun 23, 2023 |
| Title |
Inferring gene regulatory networks via transcriptional profiles as associated dynamical attractors |
| Organism |
Candida albicans |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.
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| Overall design |
Screen single knockouts and created double knockouts in Candida albicans white-opaque switch core circuit to verify model prediction.
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| Contributor(s) |
Quail M, Li R |
| Citation(s) |
37607190 |
| |
| Submission date |
Nov 07, 2022 |
| Last update date |
Sep 22, 2023 |
| Contact name |
Ruihao Li |
| E-mail(s) |
rli46@ucmerced.edu
|
| Organization name |
University of California, Merced
|
| Department |
QSB/MCB
|
| Lab |
Aaron Hernday lab
|
| Street address |
5200 NORTH LAKE ROAD
|
| City |
MERCED |
| State/province |
CA |
| ZIP/Postal code |
95340 |
| Country |
USA |
| |
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| Platforms (1) |
| GPL24129 |
Illumina HiSeq 4000 (Candida albicans) |
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| Samples (51)
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| Relations |
| BioProject |
PRJNA899050 |
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