Negotiating Early Reuse of Components - A Model-Based Analysis

UML and the Unified Process ◽

10.4018/978-1-93177-744-5.ch005 ◽

2011 ◽

pp. 66-79 ◽

Cited By ~ 1

Author(s):

J. A. Sykes

Keyword(s):

Component Composition ◽

The Other ◽

Component Reuse ◽

System Specification ◽

Modeling Framework ◽

Model Based ◽

Model Based Analysis ◽

Semiotic Theory

Unless existing components are considered during formulation of a system specification, the amount of component reuse that is possible may be limited. In order to increase the amount of reuse, it may be necessary to alter the functionality or performance of the system from that originally envisioned. Tension between stakeholders thus exists. Reuse of components also significantly changes the specification activity because it must now deal with component specifications as input models, which is not necessarily the case when reuse is not the goal. These issues are investigated using a modeling framework based on semiotic theory. The nature of modeling abstractions that could support the negotiation between stakeholders is also explored. Two scenarios are examined: one based on the idea of functional abstractions that can be composed and the other one using structural abstractions of the kind available in the UML as the basis of component composition. Even though at this stage, there are no good examples of functional abstractions that can be composed, it is concluded that functional abstractions are the best prospect for supporting collaboration and negotiation.

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Mechanistic Model Based Analysis of Biopharmaceutics Experiments: Application of In Vitro/In Vivo Extrapolation (IVIV_E) Techniques within a PBPK Modeling Framework

10.26226/morressier.57d6b2b6d462b8028d88eca5 ◽

2016 ◽

Author(s):

Shriram Pathak

Keyword(s):

Mechanistic Model ◽

Pbpk Modeling ◽

Modeling Framework ◽

Model Based ◽

In Vivo Extrapolation ◽

Model Based Analysis

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Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1715806115 ◽

2018 ◽

Vol 115 (19) ◽

pp. E4340-E4349 ◽

Cited By ~ 73

Author(s):

Simon J. Moore ◽

James T. MacDonald ◽

Sarah Wienecke ◽

Alka Ishwarbhai ◽

Argyro Tsipa ◽

...

Keyword(s):

Time Course ◽

Resource Competition ◽

Regulatory Elements ◽

Limiting Factors ◽

Model Parameters ◽

Modeling Framework ◽

Experimental Conditions ◽

Rapid Acquisition ◽

Model Based ◽

Model Based Analysis

Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host,Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this toB. megateriumto quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.

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