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[Selection criteria in metabolic systems].

J G Reich

    Biomedica Biochimica Acta
    |January 1, 1984
    PubMed
    Summary
    This summary is machine-generated.

    Evolutionary selection can optimize gene expression programs in metabolic systems. This optimization is possible for most controllable parameters, especially those in autocatalytic cycles essential for cell growth.

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    Area of Science:

    • Systems biology
    • Evolutionary biology
    • Biochemistry

    Background:

    • Cellular metabolism involves physico-chemical parameters often considered constant.
    • Living cells regulate catalytic activities via gene expression programs.
    • Understanding the optimization potential of these programs is crucial.

    Purpose of the Study:

    • Investigate the extent to which gene expression programs are amenable to optimization by evolutionary selection.
    • Evaluate mathematical models of cellular metabolic systems and biomacromolecule metabolism.
    • Determine if selection criteria, like cell growth rate, lead to optimal parameter values.

    Main Methods:

    • Formulated and evaluated mathematical models of cellular metabolic systems.
    • Incorporated epigenetic metabolism of biomacromolecules (enzyme and structural proteins).

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  • Defined cell growth rate as the selection criterion for parameter optimization.

    • Main Results:

    • Identified two types of parameters influencing growth rate: monotonous and those with a maximum positive influence.
    • Found that a maximum is defined for parameters within autocatalytic cycles limited by competition.
    • Demonstrated that biomacromolecules essential for biosynthesis and growth, while competing, exhibit such maxima.

    Conclusions:

    • Most controllable parameters of gene expression are amenable to evolutionary optimization.
    • Optimization is particularly effective for parameters involved in autocatalytic cycles essential for biosynthesis.
    • The natural upper bound of biosynthesis and the role of proteins in metabolism support this evolutionary optimization.