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Ways of classifying models are presented and the experimental approach to model development through system identification and optimisation is introduced.
This work introduces a novel generic Model-based Design of Experiments (M-DoE) routine with its main target being model development and system characterization.
Model development Multiscale systems pharmacology model for bone markers and BMD.
These include support for data integration, model development for systems biology, and visualization of large masses of data.
The presented MINLP-based optimization approach for nested-model selection and identification is a powerful methodology for model development in systems biology.
This case study clearly reveals the usefulness of the proposed identification procedure to improve efficiency and robustness during model development in systems biology.
Despite the usefulness and broad applicability of the presented approach for model development in systems biology, there are three major limitations worth mentioning here: Scaling up to large-scale models: the corresponding MINLPs might become rather large and therefore the computational effort needed for their solution might become prohibitive.
To deal with these challenges, we previously developed the MEtabolic MOdel research and development System (MEMOSys) to support the construction, modification and management of GEMs (13).
Initially, as a model development of the system, this paper tends to focus on the overall system design of the fuel cell.
MEMOSys (MEtabolic MOdel research and development System) is a versatile platform for the management, storage, and development of genome-scale metabolic models.
The MEtabolic MOdel research and development System (MEMOSys) is a versatile database for the management, storage and development of genome-scale models (GEMs).
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