Quantitative and logic modelling of gene and molecular networks

By Nicolas Le Novère. Nature reviews genetics, 2015

408 citations recorded by the source corpus.

Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/657a296539dcebfe6c8c7fd33d9fcf1377c58898

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In Molecular Biology

1. Young M. Park
2. Ramón Doallo
3. Julio R. Banga
4. Michael Hucka
5. Nicola Buso

Citations in the corpus

• BioModels—15 years of sharing computational models in life science (2019, background): “Hence, predictive computational models are very useful to fully understand the behaviours of biological systems and generate hypotheses (1).”
• Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy (2017, background): “Recent efforts have been focused on scaling-up the development of dynamic (kinetic) models [19–25], with the ultimate goal of obtaining whole-cell models [26, 27].”