Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages
| Autor: | Alberto J. M. Martin, Daniel Garrido, Rodrigo Santibáñez |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
AcademicSubjects/SCI01060 Computer science In silico Cell Gene regulatory network Complex system Computational biology Biochemistry Genome 03 medical and health sciences 0302 clinical medicine Gene expression medicine Molecular Biology Gene Transcription factor Gene knockout 030304 developmental biology 0303 health sciences biology Atlas (topology) Systems Biology Promoter Rule-based system Metabolism biology.organism_classification Original Papers Computer Science Applications Computational Mathematics ComputingMethodologies_PATTERNRECOGNITION medicine.anatomical_structure Computational Theory and Mathematics 030217 neurology & neurosurgery Biological network Bacteria |
| Zdroj: | Bioinformatics |
| ISSN: | 1460-2059 1367-4803 |
| DOI: | 10.1093/bioinformatics/btaa1040 |
| Popis: | MotivationCells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes.ResultsWe developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria.Availability and implementationCode, models and tutorials are available online (https://github.com/networkbiolab/atlas).Supplementary informationSupplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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