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Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response

ABSTRACT: A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response This model is described in the article: A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response. Diedrichs DR, Gomez JA, Huang CS, Rutkowski DT, Curtu R. Mol. Biol. Cell 2018 Apr; : mbcE17090565 Abstract: The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2? phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feed-forward circuit within the PERK/CHOP axis, and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to ER stress yet also to be rapidly deactivated by improved protein folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR. This model is hosted on BioModels Database and identified by: MODEL1803300000. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Danilo R. Diedrichs

PROVIDER: BIOMD0000000703 | BioModels | 2018-03-06

REPOSITORIES: BioModels

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A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response.

Diedrichs Danilo R DR Gomez Javier A JA Huang Chun-Sing CS Rutkowski D Thomas DT Curtu Rodica R

Molecular biology of the cell 20180418 12

The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unrep ...[more]

PMID: 29668363

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Project description: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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Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response

Publications

A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response.

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