Literature DB >> 21203848

Biomedical engineering strategies in system design space.

Michael A Savageau1.   

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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Mesh:

Year:  2011        PMID: 21203848      PMCID: PMC3074507          DOI: 10.1007/s10439-010-0220-2

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  27 in total

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Journal:  Eur J Cell Biol       Date:  2005-12-19       Impact factor: 4.492

4.  Stochastic Dynamics of Membrane Protrusion Mediated by the DOCK180/Rac Pathway in Migrating Cells.

Authors:  Erik S Welf; Jason M Haugh
Journal:  Cell Mol Bioeng       Date:  2010-03-01       Impact factor: 2.321

5.  Duplication frequency in a population of Salmonella enterica rapidly approaches steady state with or without recombination.

Authors:  Andrew B Reams; Eric Kofoid; Michael Savageau; John R Roth
Journal:  Genetics       Date:  2010-01-18       Impact factor: 4.562

Review 6.  Mathematics of cell motility: have we got its number?

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Journal:  J Math Biol       Date:  2008-05-07       Impact factor: 2.259

7.  Phenotypic repertoire of the FNR regulatory network in Escherichia coli.

Authors:  Dean A Tolla; Michael A Savageau
Journal:  Mol Microbiol       Date:  2010-11-08       Impact factor: 3.501

8.  Relating mutant genotype to phenotype via quantitative behavior of the NADPH redox cycle in human erythrocytes.

Authors:  Pedro M B M Coelho; Armindo Salvador; Michael A Savageau
Journal:  PLoS One       Date:  2010-09-28       Impact factor: 3.240

9.  Quantifying global tolerance of biochemical systems: design implications for moiety-transfer cycles.

Authors:  Pedro M B M Coelho; Armindo Salvador; Michael A Savageau
Journal:  PLoS Comput Biol       Date:  2009-03-20       Impact factor: 4.475

10.  Universally sloppy parameter sensitivities in systems biology models.

Authors:  Ryan N Gutenkunst; Joshua J Waterfall; Fergal P Casey; Kevin S Brown; Christopher R Myers; James P Sethna
Journal:  PLoS Comput Biol       Date:  2007-08-15       Impact factor: 4.475

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Review 2.  Design of the lac gene circuit revisited.

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3.  Quantification of pre-mRNA escape rate and synergy in splicing.

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Review 4.  The best models of metabolism.

Authors:  Eberhard O Voit
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2017-05-19

5.  Regulatory design governing progression of population growth phases in bacteria.

Authors:  Agustino Martínez-Antonio; Jason G Lomnitz; Santiago Sandoval; Maximino Aldana; Michael A Savageau
Journal:  PLoS One       Date:  2012-02-21       Impact factor: 3.240

Review 6.  Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection.

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Journal:  Front Physiol       Date:  2017-08-30       Impact factor: 4.566

  6 in total

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