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5
FiatFlux-P
This third application scenario is concerned with the automation of time-
consuming metabolic flux analysis procedures based on the software Fiat-
Flux [353]. In addition to the integration of its principal functionality as
elementary services, new services were implemented that emulate the user
interaction with FiatFlux in order to enable automation. Like GeneFisher-
P, the resulting
FiatFlux-P
facilitates building variants and defining batch
processing workflows at the user level.
5.1 Background: Metabolic Flux Analysis
Rapidly improving analytical techniques in the life science laboratories,
especially in the field of high-throughput procedures, have made it possible to
investigatebiologicalphenomena ona morecomprehensivescale.It has become
commonpracticetoconsidertheentiretyofgenes,RNA,proteinsormetabolites
that arepresent in the cell at a particulartime point, in addition to the examina-
tion of single molecular structures. These comprehensive datasets can be used
to infer metabolic pathways, complete genetic regulatory or reaction networks
and be integrated to obtain comprehensive (computational) models of biolog-
ical systems [279, p. 171]. This approach, aiming at “understanding biological
organisms in their entirety” [279, p. 273] via integration of data from genomics,
transcriptomics,proteomics,metabolomics,andphenomics measurements into
computational models, has been coined as
Systems Biology
.
5.1.1 Metabolomics
Metabolomics, one sub-discipline of Systems Biology, “deals with the
analysis of metabolites” [279, p. 262]. More precisely, it aims at studying the
quantitative occurrences of metabolites within metabolic pathways that are
possibly already known qualitatively. There is currently no technique available
that can measure the entirety of all cellular metabolites simultaneously, but
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