Limits...
Metabolism at evolutionary optimal States.

Rabbers I, van Heerden JH, Nordholt N, Bachmann H, Teusink B, Bruggeman FJ - Metabolites (2015)

Bottom Line: For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism.The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering.In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

View Article: PubMed Central - PubMed

Affiliation: Department of Systems Bioinformatics, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands. i.rabbers@vu.nl.

ABSTRACT
Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

No MeSH data available.


Related in: MedlinePlus

Cells with the same genotype can adapt to changing environments (different colour of tube content), by random switches between phenotypic states (dashed or non-dashed edges of cells). Phenotypic heterogeneity of a population of cells can allow quick resumption of growth upon a switch in the environment, as a subpopulation will be “preadapted” to the new circumstances.
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metabolites-05-00311-f007: Cells with the same genotype can adapt to changing environments (different colour of tube content), by random switches between phenotypic states (dashed or non-dashed edges of cells). Phenotypic heterogeneity of a population of cells can allow quick resumption of growth upon a switch in the environment, as a subpopulation will be “preadapted” to the new circumstances.

Mentions: As the maintenance of an environmental sensing system—like the catabolite repression machinery in the last example—entails fitness costs, randomly switching between phenotypic states can also be a beneficial strategy (see Figure 7). This was illustrated by Acar et al. [68]. They showed that fitness is enhanced when a fraction of the population is pre-adapted or “blindly anticipating” by means of stochastic switching between phenotypes in isogenic populations. They used an engineered gal pathway that allows experimental tuning of the rate of stochastic on/off switching of the uracil biosynthesis gene Ura3. In the absence of uracil, the “on” state is favoured (as uracil needs to be produced by the cells), while in an environment with uracil and 5FOA (a compound that is converted into a toxic product by Ura3) the “off” state is favoured. In a turbidostat culture, the environment was then changed between these two conditions. Fast switching of the cells between the on and off state allowed for quick recovery upon a change in the environment (as more cells are in the preferred state already), while slow switching allowed for a higher steady state growth rate (less cells switch back to the non-preferred state). They confirmed that this difference was due to the switching frequency of the cells, using a mathematical model. This model was subsequently used to predict that fast switching of environments favours fast switching cells, and slow switching of environments favours slow switching cells. Next, those predictions were experimentally confirmed.


Metabolism at evolutionary optimal States.

Rabbers I, van Heerden JH, Nordholt N, Bachmann H, Teusink B, Bruggeman FJ - Metabolites (2015)

Cells with the same genotype can adapt to changing environments (different colour of tube content), by random switches between phenotypic states (dashed or non-dashed edges of cells). Phenotypic heterogeneity of a population of cells can allow quick resumption of growth upon a switch in the environment, as a subpopulation will be “preadapted” to the new circumstances.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495375&req=5

metabolites-05-00311-f007: Cells with the same genotype can adapt to changing environments (different colour of tube content), by random switches between phenotypic states (dashed or non-dashed edges of cells). Phenotypic heterogeneity of a population of cells can allow quick resumption of growth upon a switch in the environment, as a subpopulation will be “preadapted” to the new circumstances.
Mentions: As the maintenance of an environmental sensing system—like the catabolite repression machinery in the last example—entails fitness costs, randomly switching between phenotypic states can also be a beneficial strategy (see Figure 7). This was illustrated by Acar et al. [68]. They showed that fitness is enhanced when a fraction of the population is pre-adapted or “blindly anticipating” by means of stochastic switching between phenotypes in isogenic populations. They used an engineered gal pathway that allows experimental tuning of the rate of stochastic on/off switching of the uracil biosynthesis gene Ura3. In the absence of uracil, the “on” state is favoured (as uracil needs to be produced by the cells), while in an environment with uracil and 5FOA (a compound that is converted into a toxic product by Ura3) the “off” state is favoured. In a turbidostat culture, the environment was then changed between these two conditions. Fast switching of the cells between the on and off state allowed for quick recovery upon a change in the environment (as more cells are in the preferred state already), while slow switching allowed for a higher steady state growth rate (less cells switch back to the non-preferred state). They confirmed that this difference was due to the switching frequency of the cells, using a mathematical model. This model was subsequently used to predict that fast switching of environments favours fast switching cells, and slow switching of environments favours slow switching cells. Next, those predictions were experimentally confirmed.

Bottom Line: For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism.The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering.In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

View Article: PubMed Central - PubMed

Affiliation: Department of Systems Bioinformatics, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands. i.rabbers@vu.nl.

ABSTRACT
Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

No MeSH data available.


Related in: MedlinePlus