ReviewAnnals of biomedical engineering2011
Biomedical engineering strategies in system design space.
Review in Annals of biomedical engineering, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- The best models of metabolism.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection.Frontiers in physiology · 2017Review
- Quantification of pre-mRNA escape rate and synergy in splicing.Nucleic acids research · 2014Article
- Article
- Design of the lac gene circuit revisited.Mathematical biosciences · 2011Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Modern systems biology and synthetic bioengineering face two major challenges in relating properties of the genetic components of a natural or engineered system to its integrated behavior. The first is the fundamental unsolved problem of relating the digital representation of the genotype to the analog representation of the parameters for the molecular components. For example, knowing the DNA sequence does not allow one to determine the kinetic parameters of an enzyme. The second is the fundamental unsolved problem of relating the parameters of the components and the environment to the phenotype of the global system. For example, knowing the parameters does not tell one how many qualitatively distinct phenotypes are in the organism's repertoire or the relative fitness of the phenotypes in different environments. These also are challenges for biomedical engineers as they attempt to develop therapeutic strategies to treat pathology or to redirect normal cellular functions for biotechnological purposes. In this article, the second of these fundamental challenges will be addressed, and the notion of a "system design space" for relating the parameter space of components to the phenotype space of bioengineering systems will be focused upon. First, the concept of a system design space will be motivated by introducing one of its key components from an intuitive perspective. Second, a simple linear example will be used to illustrate a generic method for constructing the design space in which qualitatively distinct phenotypes can be identified and counted, their fitness analyzed and compared, and their tolerance to change measured. Third, two examples of nonlinear systems from different areas of biomedical engineering will be presented. Finally, after giving reference to a few other applications that have made use of the system design space approach to reveal important design principles, some concluding remarks concerning challenges and opportunities for further development will be made.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.