An introduction to kinetic, constraint-based and Boolean modeling in systems biology

Ederer, Michael; Schlatter, Rebekka; Witt, Johannes; Feuer, Ronny; Bóna-Lovász, Judit; Henkel, Sebastian; Sawodny, Oliver

doi:10.1109/cca.2010.5611136

Cited by 2 publications

(2 citation statements)

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“…Based on biochemical knowledge, classical kinetic models provide detailed dynamic and quantitative descriptions of the systems. However, they depend on many, usually difficult-to-measure, parameters and are also computationally expensive to solve in a genome-scale context (32)(33)(34). Indeed, to date, there are no dynamic genome-scale models of metabolism that can be used effectively in ME efforts, mainly because of the difficulty in obtaining the relevant kinetic data (35,36).…”

Section: Constraint-based Modelsmentioning

confidence: 99%

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In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Maia

Rocha

2016

Microbiol Mol Biol Rev

116

107

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SUMMARYShifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together within silicostrain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the mainin silicoconstraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

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Section: Constraint-based Modelsmentioning

confidence: 99%

“…For several metabolic network analysis or metabolic engineering tasks, a simpler approach might be sufficient to obtain useful results. For these purposes, certain realistic assumptions can be adopted, avoiding the burden of determining kinetic rate equations and their parameters (33).…”

Section: Constraint-based Modelsmentioning

confidence: 99%