Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models.

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Characterizing isoprene production in cyanobacteria - Insights into the effects of light, temperature, and isoprene on Synechocystis sp. PCC 6803.

Engineering cyanobacteria for the production of isoprene and other terpenoids has gained increasing attention in the field of biotechnology. Several studies have addressed optimization of isoprene synthesis in cyanobacteria via enzyme and pathway engineering. However, only little attention has been paid to the optimization of cultivation conditions. In this study, an isoprene-producing strain of Synechocystis sp. PCC 6803 and two control strains were grown under a variety of cultivation conditions. Isoprene production, as quantified by modified membrane inlet mass spectrometer (MIMS) and interpreted using Flux Balance Analysis (FBA), increased under violet light and at elevated temperature. Increase of thermotolerance in the isoprene producer was attributed to the physical presence of isoprene, similar to plants. The results demonstrate a beneficial effect of isoprene on cell survival at higher temperatures. This increased thermotolerance opens new possibilities for sustainable bio-production of isoprene and other products.

Time-Optimal Adaptation in Metabolic Network Models.

Analysis of metabolic models using constraint-based optimization has emerged as an important computational technique to elucidate and eventually predict cellular metabolism and growth. In this work, we introduce time-optimal adaptation (TOA), a new constraint-based modeling approach that allows us to evaluate the fastest possible adaptation to a pre-defined cellular state while fulfilling a given set of dynamic and static constraints. TOA falls into the mathematical problem class of time-optimal control problems, and, in its general form, can be broadly applied and thereby extends most existing constraint-based modeling frameworks. Specifically, we introduce a general mathematical framework that captures many existing constraint-based methods and define TOA within this framework. We then exemplify TOA using a coarse-grained self-replicator model and demonstrate that TOA allows us to explain several well-known experimental phenomena that are difficult to explore using existing constraint-based analysis methods. We show that TOA predicts accumulation of storage compounds in constant environments, as well as overshoot uptake metabolism after periods of nutrient scarcity. TOA shows that organisms with internal temporal degrees of freedom, such as storage, can in most environments outperform organisms with a static intracellular composition. Furthermore, TOA reveals that organisms adapted to better growth conditions than present in the environment ("optimists") typically outperform organisms adapted to poorer growth conditions ("pessimists").

Fast-growing phototrophic microorganisms and the productivity of phototrophic cultures.

Fast-growing cyanobacterial and microalgal strains are considered to be a promising resource to overcome current productivity barriers of phototrophic cultivation. The purpose of this communication, however, is to argue that a high maximal growth rate itself is not a sufficient or necessary property for high phototrophic productivity. Rather, the light-limited specific growth rate of a phototrophic microorganism is a product of several factors, including the rate of light absorption, the photosynthetic efficiency, and the maximal biomass yield per mol photons. It is suggested that, in addition to the maximal growth rate, reports on fast-growing strains should also assess photosynthetic efficiency and maximal biomass yield as predictors of culture productivity. The arguments within the communication are underpinned by a theoretical analysis of a light-limited chemostat, compared to its heterotrophic counterpart. It is shown that for the light-limited chemostat maximal productivity occurs at low dilution rates.

Deciphering the physiological response of Escherichia coli under high ATP demand.

One long-standing question in microbiology is how microbes buffer perturbations in energy metabolism. In this study, we systematically analyzed the impact of different levels of ATP demand in Escherichia coli under various conditions (aerobic and anaerobic, with and without cell growth). One key finding is that, under all conditions tested, the glucose uptake increases with rising ATP demand, but only to a critical level beyond which it drops markedly, even below wild-type levels. Focusing on anaerobic growth and using metabolomics and proteomics data in combination with a kinetic model, we show that this biphasic behavior is induced by the dual dependency of the phosphofructokinase on ATP (substrate) and ADP (allosteric activator). This mechanism buffers increased ATP demands by a higher glycolytic flux but, as shown herein, it collapses under very low ATP concentrations. Model analysis also revealed two major rate-controlling steps in the glycolysis under high ATP demand, which could be confirmed experimentally. Our results provide new insights on fundamental mechanisms of bacterial energy metabolism and guide the rational engineering of highly productive cell factories.

Modelling microbial communities using biochemical resource allocation analysis.

To understand the functioning and dynamics of microbial communities is a fundamental challenge in current biology. To tackle this challenge, the construction of computational models of interacting microbes is an indispensable tool. There is, however, a large chasm between ecologically motivated descriptions of microbial growth used in many current ecosystems simulations, and detailed metabolic pathway and genome-based descriptions developed in the context of systems and synthetic biology. Here, we seek to demonstrate how resource allocation models of microbial growth offer the potential to advance ecosystem simulations and their parametrization. In particular, recent work on quantitative resource allocation allow us to formulate mechanistic models of microbial growth that are physiologically meaningful while remaining computationally tractable. These models go beyond Michaelis-Menten and Monod-type growth models, and are capable of accounting for emergent properties that underlie the remarkable plasticity of microbial growth. We outline the utility and advantages of using biochemical resource allocation models by considering a coarse-grained model of cyanobacterial growth and demonstrate how the model allows us to address specific questions of relevance for the simulation of marine microbial ecosystems, including the physiological acclimation of protein expression to different environments, the description of co-limitation by several nutrients and the differential use of alternative nutrient sources, as well as the description of metabolic diversity based on our increasing knowledge about quantitative cell physiology.

Optimal proteome allocation strategies for phototrophic growth in a light-limited chemostat.

BACKGROUND: Cyanobacteria and other phototrophic microorganisms allow to couple the light-driven assimilation of atmospheric [Formula: see text] directly to the synthesis of carbon-based products, and are therefore attractive platforms for microbial cell factories. While most current engineering efforts are performed using small-scale laboratory cultivation, the economic viability of phototrophic cultivation also crucially depends on photobioreactor design and culture parameters, such as the maximal areal and volumetric productivities. Based on recent insights into the cyanobacterial cell physiology and the resulting computational models of cyanobacterial growth, the aim of this study is to investigate the limits of cyanobacterial productivity in continuous culture with light as the limiting nutrient. RESULTS: We integrate a coarse-grained model of cyanobacterial growth into a light-limited chemostat and its heterogeneous light gradient induced by self-shading of cells. We show that phototrophic growth in the light-limited chemostat can be described using the concept of an average light intensity. Different from previous models based on phenomenological growth equations, our model provides a mechanistic link between intracellular protein allocation, population growth and the resulting reactor productivity. Our computational framework thereby provides a novel approach to investigate and predict the maximal productivity of phototrophic cultivation, and identifies optimal proteome allocation strategies for developing maximally productive strains. CONCLUSIONS: Our results have implications for efficient phototrophic cultivation and the design of maximally productive phototrophic cell factories. The model predicts that the use of dense cultures in well-mixed photobioreactors with short light-paths acts as an effective light dilution mechanism and alleviates the detrimental effects of photoinhibition even under very high light intensities. We recover the well-known trade-offs between a reduced light-harvesting apparatus and increased population density. Our results are discussed in the context of recent experimental efforts to increase the yield of phototrophic cultivation.

Quantitative insights into the cyanobacterial cell economy.

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological acclimations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial acclimations to different growth rates have implications to understand and optimize photosynthetic productivity.

Kinetic modeling of the Calvin cycle identifies flux control and stable metabolomes in Synechocystis carbon fixation.

Biological fixation of atmospheric CO2 via the Calvin-Benson-Bassham cycle has massive ecological impact and offers potential for industrial exploitation, either by improving carbon fixation in plants and autotrophic bacteria, or by installation into new hosts. A kinetic model of the Calvin-Benson-Bassham cycle embedded in the central carbon metabolism of the cyanobacterium Synechocystis sp. PCC 6803 was developed to investigate its stability and underlying control mechanisms. To reduce the uncertainty associated with a single parameter set, random sampling of the steady-state metabolite concentrations and the enzyme kinetic parameters was employed, resulting in millions of parameterized models which were analyzed for flux control and stability against perturbation. Our results show that the Calvin cycle had an overall high intrinsic stability, but a high concentration of ribulose 1,5-bisphosphate was associated with unstable states. Low substrate saturation and high product saturation of enzymes involved in highly interconnected reactions correlated with increased network stability. Flux control, that is the effect that a change in one reaction rate has on the other reactions in the network, was distributed and mostly exerted by energy supply (ATP), but also by cofactor supply (NADPH). Sedoheptulose 1,7-bisphosphatase/fructose 1,6-bisphosphatase, fructose-bisphosphate aldolase, and transketolase had a weak but positive effect on overall network flux, in agreement with published observations. The identified flux control and relationships between metabolite concentrations and system stability can guide metabolic engineering. The kinetic model structure and parameterizing framework can be expanded for analysis of metabolic systems beyond the Calvin cycle.

The Circadian Clock Regulates Metabolic Phenotype Rewiring Via HKDC1 and Modulates Tumor Progression and Drug Response in Colorectal Cancer.

An endogenous molecular clockwork drives various cellular pathways including metabolism and the cell cycle. Its dysregulation is able to prompt pathological phenotypes including cancer. Besides dramatic metabolic alterations, cancer cells display severe changes in the clock phenotype with likely consequences in tumor progression and treatment response. In this study, we use a comprehensive systems-driven approach to investigate the effect of clock disruption on metabolic pathways and its impact on drug response in a cellular model of colon cancer progression. We identified distinctive time-related transcriptomic and metabolic features of a primary tumor and its metastatic counterpart. A mapping of the expression data to a comprehensive genome-scale reconstruction of human metabolism allowed for the in-depth functional characterization of 24 h-oscillating transcripts and pointed to a clock-driven metabolic reprogramming in tumorigenesis. In particular, we identified a set of five clock-regulated glycolysis genes, ALDH3A2, ALDOC, HKDC1, PCK2, and PDHB with differential temporal expression patterns. These findings were validated in organoids and in primary fibroblasts isolated from normal colon and colon adenocarcinoma from the same patient. We further identified a reciprocal connection of HKDC1 to the clock in the primary tumor, which is lost in the metastatic cells. Interestingly, a disruption of the core-clock gene BMAL1 impacts on HKDC1 and leads to a time-dependent rewiring of metabolism, namely an increase in glycolytic activity, as well as changes in treatment response. This work provides novel evidence regarding the complex interplay between the circadian clock and metabolic alterations in carcinogenesis and identifies new connections between both systems with pivotal roles in cancer progression and response to therapy.

Modules of co-occurrence in the cyanobacterial pan-genome reveal functional associations between groups of ortholog genes.

Cyanobacteria are a monophyletic phylogenetic group of global importance and have received considerable attention as potential host organisms for the renewable synthesis of chemical bulk products from atmospheric CO2. The cyanobacterial phylum exhibits enormous metabolic diversity with respect to morphology, lifestyle and habitat. As yet, however, research has mostly focused on few model strains and cyanobacterial diversity is insufficiently understood. In this respect, the increasing availability of fully sequenced bacterial genomes opens new and unprecedented opportunities to investigate the genetic inventory of organisms in the context of their pan-genome. Here, we seek understand cyanobacterial diversity using a comparative genome analysis of 77 fully sequenced and assembled cyanobacterial genomes. We use phylogenetic profiling to analyze the co-occurrence of clusters of likely ortholog genes (CLOGs) and reveal novel functional associations between CLOGs that are not captured by co-localization of genes. Going beyond pair-wise co-occurrences, we propose a network approach that allows us to identify modules of co-occurring CLOGs. The extracted modules exhibit a high degree of functional coherence and reveal known as well as previously unknown functional associations. We argue that the high functional coherence observed for the modules is a consequence of the similar-yet-diverse nature of cyanobacteria. Our approach highlights the importance of a multi-strain analysis to understand gene functions and environmental adaptations, with implications beyond the cyanobacterial phylum. The analysis is augmented with a simple toolbox that facilitates further analysis to investigate the co-occurrence neighborhood of specific CLOGs of interest.

A model of optimal protein allocation during phototrophic growth.

Photoautotrophic growth depends upon an optimal allocation of finite cellular resources to diverse intracellular processes. Commitment of a certain mass fraction of the proteome to a specific cellular function typically reduces the proteome available for other cellular functions. Here, we develop a semi-quantitative kinetic model of cyanobacterial phototrophic growth to describe such trade-offs of cellular protein allocation. The model is based on coarse-grained descriptions of key cellular processes, in particular carbon uptake, metabolism, photosynthesis, and protein translation. The model is parameterized using literature data and experimentally obtained growth curves. Of particular interest are the resulting cyanobacterial growth laws as fundamental characteristics of cellular growth. We show that the model gives rise to similar growth laws as observed for heterotrophic organisms, with several important differences due to the distinction between light energy and carbon uptake. We discuss recent experimental data supporting the model results and show that coarse-grained growth models have implications for our understanding of the limits of phototrophic growth and bridge a gap between molecular physiology and ecology.

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth.

Cyanobacteria are an integral part of Earth's biogeochemical cycles and a promising resource for the synthesis of renewable bioproducts from atmospheric CO2 Growth and metabolism of cyanobacteria are inherently tied to the diurnal rhythm of light availability. As yet, however, insight into the stoichiometric and energetic constraints of cyanobacterial diurnal growth is limited. Here, we develop a computational framework to investigate the optimal allocation of cellular resources during diurnal phototrophic growth using a genome-scale metabolic reconstruction of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate phototrophic growth as an autocatalytic process and solve the resulting time-dependent resource allocation problem using constraint-based analysis. Based on a narrow and well-defined set of parameters, our approach results in an ab initio prediction of growth properties over a full diurnal cycle. The computational model allows us to study the optimality of metabolite partitioning during diurnal growth. The cyclic pattern of glycogen accumulation, an emergent property of the model, has timing characteristics that are in qualitative agreement with experimental findings. The approach presented here provides insight into the time-dependent resource allocation problem of phototrophic diurnal growth and may serve as a general framework to assess the optimality of metabolic strategies that evolved in phototrophic organisms under diurnal conditions.

Activity and functional properties of the isocitrate lyase in the cyanobacterium Cyanothece sp. PCC 7424.

Cyanobacteria are ubiquitous photoautotrophs that assimilate atmospheric CO2 as their main source of carbon. Several cyanobacteria are known to be facultative heterotrophs that are able to grow on diverse carbon sources. For selected strains, assimilation of organic acids and mixotrophic growth on acetate has been reported for decades. However, evidence for the existence of a functional glyoxylate shunt in cyanobacteria has long been contradictory and unclear. Genes coding for isocitrate lyase (ICL) and malate synthase were recently identified in two strains of the genus Cyanothece, and the existence of the complete glyoxylate shunt was verified in a strain of Chlorogloeopsis fritschii. Here, we report that the gene PCC7424_4054 of the strain Cyanothece sp. PCC 7424 encodes an enzymatically active protein that catalyses the reaction of ICL, an enzyme that is specific for the glyoxylate shunt. We demonstrate that ICL activity is induced under alternating day/night cycles and acetate-supplemented cultures exhibit enhanced growth. In contrast, growth under constant light did not result in any detectable ICL activity or enhanced growth of acetate-supplemented cultures. Furthermore, our results indicate that, despite the presence of a glyoxylate shunt, acetate does not support continued heterotrophic growth and cell proliferation. The functional validation of the ICL is supplemented with a bioinformatics analysis of enzymes that co-occur with the glyoxylate shunt. We hypothesize that the glyoxylate shunt in Cyanothece sp. PCC 7424, and possibly other nitrogen-fixing cyanobacteria, is an adaptation to a specific ecological niche and supports assimilation of nitrogen or organic compounds during the night phase.

A Comprehensively Curated Genome-Scale Two-Cell Model for the Heterocystous Cyanobacterium Anabaena sp. PCC 7120.

Anabaena sp. PCC 7120 is a nitrogen-fixing filamentous cyanobacterium. Under nitrogen-limiting conditions, a fraction of the vegetative cells in each filament terminally differentiate to nongrowing heterocysts. Heterocysts are metabolically and structurally specialized to enable O2-sensitive nitrogen fixation. The functionality of the filament, as an association of vegetative cells and heterocysts, is postulated to depend on metabolic exchange of electrons, carbon, and fixed nitrogen. In this study, we compile and evaluate a comprehensive curated stoichiometric model of this two-cell system, with the objective function based on the growth of the filament under diazotrophic conditions. The predicted growth rate under nitrogen-replete and -deplete conditions, as well as the effect of external carbon and nitrogen sources, was thereafter verified. Furthermore, the model was utilized to comprehensively evaluate the optimality of putative metabolic exchange reactions between heterocysts and vegetative cells. The model suggested that optimal growth requires at least four exchange metabolites. Several combinations of exchange metabolites resulted in predicted growth rates that are higher than growth rates achieved by only considering exchange of metabolites previously suggested in the literature. The curated model of the metabolic network of Anabaena sp. PCC 7120 enhances our ability to understand the metabolic organization of multicellular cyanobacteria and provides a platform for further study and engineering of their metabolism.

Feedback, Mass Conservation and Reaction Kinetics Impact the Robustness of Cellular Oscillations.

Oscillations occur in a wide variety of cellular processes, for example in calcium and p53 signaling responses, in metabolic pathways or within gene-regulatory networks, e.g. the circadian system. Since it is of central importance to understand the influence of perturbations on the dynamics of these systems a number of experimental and theoretical studies have examined their robustness. The period of circadian oscillations has been found to be very robust and to provide reliable timing. For intracellular calcium oscillations the period has been shown to be very sensitive and to allow for frequency-encoded signaling. We here apply a comprehensive computational approach to study the robustness of period and amplitude of oscillatory systems. We employ different prototype oscillator models and a large number of parameter sets obtained by random sampling. This framework is used to examine the effect of three design principles on the sensitivities towards perturbations of the kinetic parameters. We find that a prototype oscillator with negative feedback has lower period sensitivities than a prototype oscillator relying on positive feedback, but on average higher amplitude sensitivities. For both oscillator types, the use of Michaelis-Menten instead of mass action kinetics in all degradation and conversion reactions leads to an increase in period as well as amplitude sensitivities. We observe moderate changes in sensitivities if replacing mass conversion reactions by purely regulatory reactions. These insights are validated for a set of established models of various cellular rhythms. Overall, our work highlights the importance of reaction kinetics and feedback type for the variability of period and amplitude and therefore for the establishment of predictive models.

Optimizing cyanobacterial product synthesis: Meeting the challenges.

The synthesis of renewable bioproducts using photosynthetic microorganisms holds great promise. Sustainable industrial applications, however, are still scarce and the true limits of phototrophic production remain unknown. One of the limitations of further progress is our insufficient understanding of the quantitative changes in photoautotrophic metabolism that occur during growth in dynamic environments. We argue that a proper evaluation of the intra- and extracellular factors that limit phototrophic production requires the use of highly-controlled cultivation in photobioreactors, coupled to real-time analysis of production parameters and their evaluation by predictive computational models. In this addendum, we discuss the importance and challenges of systems biology approaches for the optimization of renewable biofuels production. As a case study, we present the utilization of a state-of-the-art experimental setup together with a stoichiometric computational model of cyanobacterial metabolism for quantitative evaluation of ethylene production by a recombinant cyanobacterium Synechocystis sp. PCC 6803.

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach.

Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes.

A quantitative evaluation of ethylene production in the recombinant cyanobacterium Synechocystis sp. PCC 6803 harboring the ethylene-forming enzyme by membrane inlet mass spectrometry.

The prediction of the world's future energy consumption and global climate change makes it desirable to identify new technologies to replace or augment fossil fuels by environmentally sustainable alternatives. One appealing sustainable energy concept is harvesting solar energy via photosynthesis coupled to conversion of CO2 into chemical feedstock and fuel. In this work, the production of ethylene, the most widely used petrochemical produced exclusively from fossil fuels, in the model cyanobacterium Synechocystis sp. PCC 6803 is studied. A novel instrumentation setup for quantitative monitoring of ethylene production using a combination of flat-panel photobioreactor coupled to a membrane-inlet mass spectrometer is introduced. Carbon partitioning is estimated using a quantitative model of cyanobacterial metabolism. The results show that ethylene is produced under a wide range of light intensities with an optimum at modest irradiances. The results allow production conditions to be optimized in a highly controlled setup.

Resource allocation in metabolic networks: kinetic optimization and approximations by FBA.

Based on recent theoretical results on optimal flux distributions in kinetic metabolic networks, we explore the congruences and differences between solutions of kinetic optimization problems and results obtained by constraint-based methods. We demonstrate that, for a certain resource allocation problem, kinetic optimization and standard flux balance analysis (FBA) give rise to qualitatively different results. Furthermore, we introduce a variant of FBA, called satFBA, whose predictions are in qualitative agreement with kinetic optimization.