Molecular-level tradeoffs and metabolic adaptation to simultaneous stressors
Section snippets
Mathematical modeling of microbial responses to environment
Microbes are complex systems; mathematical expressions have been used to predict and interpret these dynamic systems for more than a century (e.g. [1, 2, 3]). Microbial growth expressions were soon combined into systems of differential equations to consider a multitude of stressors including combinations of limiting substrates, competitors, predators, and the presence of inhibitors [4, 5]. Unfortunately, kinetic models are parameter heavy, in terms of both number and sensitivity. Literature
Stoichiometric analysis of single stress adaptations
The functional properties of metabolic systems are the product of evolutionary processes and are competitive given the organism's life history. Therefore, assumptions about competitive cellular behavior are used to select solutions to stoichiometry-based models. A widely utilized criterion presumes that microorganisms maximize biomass yield (microbe production from a fixed quantity of substrate). This criterion is convenient, simple, and successfully describes microbial behavior under certain
Economic considerations and metabolic strategies
Resource availability limits growth in most environments and is an important component of animal immune systems, commonly referred to as nutritional immunity [22••, 23]. This has driven microbial evolution toward strategies that allocate limiting resources to different metabolic isozymes and alternative pathways in a manner that favors fitness [24•]. Standard economics approaches such as resource allocation theory and tradeoff analysis can be used to quantitatively compare the huge number of
Resource allocations and simultaneous stresses
Life is inherently competitive and stressors are not mutually exclusive. Microbes cope simultaneously with an assortment of constraints [37]. Economic and ecological theory provides a framework for predicting and interpreting microbial adaptations to multiple stresses [28, 38•, 39]. When subjected to multiple pressures, cells must allocate finite resources to different subsystems in a proportion that improves fitness; the systems biology challenge is to determine how these allocations respond
Stress adaptations and opportunity costs
Microbial responses to a variety of stressors can be quantified using the economic concept of opportunity costs. As an example of opportunity costs, E. coli shifts from the phosphotransferase system (Km ∼ 5 μm) to a higher affinity ABC transporter (Km < 1 μm) coupled with glucose kinase under glucose-scarce conditions [42]. The high affinity system requires more resources to assemble and operate (Figure 3); however, these costs are offset by improved glucose uptake at low external concentrations. The
Biodiversity, network robustness, and the Darwinian demon
All life faces physical, physiological, energetic, and temporal constraints. Resources allocated to one capacity cannot be allocated elsewhere. The resulting tradeoffs have been used to explain biodiversity on both an evolutionary and a dynamic basis [43, 44•]. Ecologists often invoke a thought experiment to test the null hypothesis of free specialization. The exercise proposes the existence of a ‘superspecies’, termed a Darwinian demon, unconstrained by tradeoffs: living long, reproducing
Conclusions
Decades of economic and ecological studies have highlighted the importance of strategic resource allocation and the associated constraints on competitive functionality. These concepts are relevant at all biological scales, from individual microbes to ecosystems, and appear to play key roles in the composition, organization, and functioning of molecular-level metabolic systems. The large body of theoretical and applied work in these fields provides a firm foundation for systems approaches to
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgement
This work was supported by financial support from National Institutes of Health (EB006532 and P20 RR024237).
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