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Adelinde Uhrmacher (2007)

Multiple Scales and Perspectives for Modeling and Simulation in Life Science Automation

Miscellaneous publication, 5th International Forum of Life Science Automation.

Life science automation facilitates conducting large numbers of bio-chemical experiments in a systematic, reproducable manner. Thereby, a better understanding of biochemical dynamic processes is supported. These experiments form the basis for "in-silico" experiments in Computational Biology, i.e. simulation  based on formal models. As Computational Biology moves from describing a system at one level of detail to including different scales, the methodological challenges for modeling and simulation increase: processes at different temporal, spatial, and organizational scales have to be effectively described and efficiently executed. To understand the structure and function of molecules, molecular dynamics (MD) simulation resort to atoms and their interactions based on known laws of physics.  To describe the processes within a cell, deterministic concentration-based inspection on macro view, in-detail stochastic inspection at micro-view, with or without spatial resolution are used and combined. For cell to cell interaction cellular automata type models capture morphological phenomena. Modeling and simulation methods are as different as the scales and perspectives they shall support. The design of these models depends on a broad range of biological information, including high-throughput genomics and proteomics data. In the opposite direction models and "in-silico" experiments lead to the design of new high-throughput experiments that in themselves form complex dynamic processes. They depend on the biological entity to be analyzed, on the facilities used for analysis, the timing of analysis procedures, and their interactions. The purpose of modeling and simulation has been traditionally used to analyze those processes off-line to identify bottle-necks, or to control it in an online  modus to help scheduling tasks. The later has been joined more recently by approaches that focus on the design of these experiments. The type of models again vary: models whose online evaluation shall support the scheduling have to contain more technical insights than workflow models with their data-oriented view.  The semi-automatic design of an experiment requires more information about the biological entities under test and the particular constraints they impose on the facilities and the timing than a re-scheduling of tasks requires. As do the models the execution and interpretation of these models vary. Whereas the execution of workflows follows a discrete pattern, each facility being part of the workflow comes along with a set of continuous simulation procedures for training and adjusting purposes. Many Laboratory Information Systems (LIMS) utilize meanwhile state of the art computer science technologies, like service oriented architectures, XML specification of experiments and workflow methods, all of which are inherently based on models,  orchestrated in an experimental setting to support the analyses of biochemical processes at different levels. To make these models explicit is likely to foster truely flexible and supportive Life Science Automation Systems.  

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