Revista: | Computación y sistemas |
Base de datos: | PERIÓDICA |
Número de sistema: | 000423231 |
ISSN: | 1405-5546 |
Autores: | Martinez Garcia, Juan Carlos1 Aguilar Ibanez, Carlos2 Soria Lopez, Alberto1 |
Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación y de Estudios Avanzados, Ciudad de México. México 2Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México. México |
Año: | 2017 |
Periodo: | Abr-Jun |
Volumen: | 21 |
Número: | 2 |
País: | México |
Idioma: | Inglés |
Tipo de documento: | Artículo |
Enfoque: | Experimental, aplicado |
Resumen en inglés | Our concern is the tuning of mathematical models describing rationally designed genetic biocircuits. Based on a deterministic lumped continuous-time approach, we propose a tuning methodology combining both exact algebraic parameter reconstruction and nonlinear parameter estimation of a given model supporting the design of a specific genetic biocircuit, i.e., we bridge the gap between model-based design and implementation as the solution of a systems inverse problem. As a proof of concept, our proposal is constrained to cyclic feedback systems characterizing synthesized transcriptional networks conditioned to display sustained oscillatory behavior. Our proposed methodology is illustrated via computer–based simu-lations involving the tuning of a state–based model describing a well–know cyclic feedback biocircuit: the celebrated repressilator. Tuning in our case is conceived as a procedure to adjust the parameter values of the mathematical model taking into account for this the actual behavior observed from the corresponding synthesized biocircuit |
Disciplinas: | Biología, Ciencias de la computación |
Palabras clave: | Procesamiento de datos, Biología de sistemas, Biología sintética, Modelos matemáticos, Sistemas de identificación basados en observadores, Redes sintéticas transcripcionales, Biocircuitos de retroalimentación cíclica |
Keyword: | Data processing, Systems biology, Synthetic biology, Mathematical models, Observer based system identification, Synthetic transcriptional networks, Cyclic feedback biocircuits |
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