A Network Approach to Genetic Circuit Designs
Autor: | Angel Goñi-Moreno, Matthew Crowther, Anil Wipat |
---|---|
Přispěvatelé: | Comunidad de Madrid, Agencia Estatal de Investigación (España), Crowther, Matthew, Wipat, Anil, Goñi-Moreno, Ángel |
Rok vydání: | 2022 |
Předmět: |
Structure (mathematical logic)
Hierarchy (mathematics) Computer science Biomedical Engineering Visualisation technique General Medicine computer.software_genre Biochemistry Genetics and Molecular Biology (miscellaneous) Glyph (data visualization) Visualization Synthetic biology Graph drawing Cluster Analysis Gene Regulatory Networks Synthetic Biology Data mining Representation (mathematics) computer Metabolic Networks and Pathways Software |
Zdroj: | ACS synthetic biology. 11(9) |
ISSN: | 2161-5063 |
Popis: | 9 Pág. Centro de Biotecnología y Genómica de Plantas As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information. This work was supported by the Grants BioSinT-CM (Y2020/TCS-6555) and CONTEXT (Atracción de Talento Program; 2019-T1/BIO-14053) Projects of the Comunidad de Madrid, MULTI-SYSBIO (PID2020-117205GA-I00) and the Severo Ochoa Program for Centres of Excellence in R&D (CEX2020-000999-S) funded by MCIN/AEI/10.13039/501100011033 and the EPSRC studentship 34000024085 (M.C.) |
Databáze: | OpenAIRE |
Externí odkaz: |