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

Sulfate assimilation regulates antioxidant defense response of the cyanobacterium Synechococcus elongatus PCC 7942 to high concentrations of carbon dioxide

The adaptive evolution of cyanobacteria over a prolonged period has allowed them to utilize carbon dioxide (CO2) at the low concentrations found in the atmosphere (0.04% CO2) for growth. However, whether the exposure of cyanobacteria to high concentrations of CO2 results in oxidative stress and the activation of antioxidant defense response remains unknown, albeit fluctuations in other culture conditions have been reported to exert these effects. The current study reveals the physiological regulation of the model cyanobacterium Synechococcus elongatus PCC 7942 upon exposure to 1% CO2 and the underlying mechanism. Exposure to 1% CO2 was demonstrated to induce oxidative stress and activate antioxidant defense responses in S. elongatus. Further analysis of variations in metabolism between S. elongatus cells grown at 0.04% CO2 and exposed to 1% CO2 revealed that sulfate assimilation was enhanced after the exposure to 1% CO2. A strain of S. elongatus lacking the gene cysR, encoding a global transcriptional regulator for genes involved in sulfate assimilation, was generated by deleting the gene from the genomic DNA. A comparative analysis of the wild-type and cysR-null strains indicated the regulation of the antioxidant response by sulfate assimilation. In addition, lines of evidence were presented that suggest a role of degradation of phycobilisome in the antioxidant response of S. elongatus under conditions of 1% CO2 and sulfate limitation. This study sheds light on the in situ effects of high CO2-induced stress on the ecophysiology of cyanobacteria upon exposure to diverse scenarios from a biotechnological and ecological perspective.IMPORTANCECyanobacteria that grow autotrophically with CO2 as the sole carbon source can be subject to high-CO2 stress in a variety of biotechnological and ecological scenarios. However, physiological regulation of cyanobacteria in response to high-CO2 stress remains elusive. Here, we employed microbial physiological, biochemical, and genetic techniques to reveal the regulatory strategies of cyanobacteria in response to high-CO2 stress. This study, albeit physiological, provides a biotechnological enterprise for manipulating cyanobacteria as the chassis for CO2 conversion and sheds light on the in situ ecological effects of high CO2 on cyanobacteria.

Cyanobacterial circadian regulation enhances bioproduction under subjective nighttime through rewiring of carbon partitioning dynamics, redox balance orchestration, and cell cycle modulation

BACKGROUND

The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction.

RESULTS

After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms.

CONCLUSIONS

Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.

Fabricating an advanced electrogenic chassis by activating microbial metabolism and fine-tuning extracellular electron transfer

Exploiting electrogenic microorganisms as unconventional chassis hosts offers potential solutions to global energy and environmental challenges. However, their limited electrogenic efficiency and metabolic versatility, due to genetic and metabolic constraints, hinder broader applications. Herein, we developed a multifaceted approach to fabricate an enhanced electrogenic chassis, starting with streamlining the genome by removing extrachromosomal genetic material. This reduction led to faster lactate consumption, higher intracellular NADH/NAD+ and ATP/ADP levels, and increased growth and biomass accumulation, as well as promoted electrogenic activity. Transcriptome profiling showed an overall activation of cellular metabolism. We further established a molecular toolkit with a vector vehicle incorporating native replication block and refined promoter components for precise gene expression control. This enabled engineered primary metabolism for greater environmental robustness and fine-tuned extracellular electron transfer (EET) for improved efficiency. The enhanced chassis demonstrated substantially improved pollutant biodegradation and radionuclide removal, establishing a new paradigm for utilizing electrogenic organisms as novel biotechnology chassis.

Conditional flux balance analysis toolbox for python: application to research metabolism in cyclic environments

Summary

We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments.

Availability and implementation

Extensive documentation, installation steps, tutorials, and examples are available at https://tp-watson-python-cfba.readthedocs.io/en/. The py_cFBA python package is available at https://pypi.org/project/py-cfba/.

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability

Natural environments of living organisms are often dynamic and multifactorial, with multiple parameters fluctuating over time. To better understand how cells respond to dynamically interacting factors, we quantified the effects of dual fluctuations of osmotic stress and glucose deprivation on yeast cells using microfluidics and time-lapse microscopy. Strikingly, we observed that cell proliferation, survival, and signaling depend on the phasing of the two periodic stresses. Cells divided faster, survived longer, and showed decreased transcriptional response when fluctuations of hyperosmotic stress and glucose deprivation occurred in phase than when the two stresses occurred alternatively. Therefore, glucose availability regulates yeast responses to dynamic osmotic stress, showcasing the key role of metabolic fluctuations in cellular responses to dynamic stress. We also found that mutants with impaired osmotic stress response were better adapted to alternating stresses than wild-type cells, showing that genetic mechanisms of adaptation to a persistent stress factor can be detrimental under dynamically interacting conditions.

Mechanistic constraints on the trade-off between photosynthesis and respiration in response to warming

Phytoplankton are responsible for half of all oxygen production and drive the ocean carbon cycle. Metabolic theory predicts that increasing global temperatures will cause phytoplankton to become more heterotrophic and smaller. Here, we uncover the metabolic trade-offs between cellular space, energy, and stress management driving phytoplankton thermal acclimation and how these might be overcome through evolutionary adaptation. We show that the observed relationships between traits such as chlorophyll, lipid content, C:N, and size can be predicted on the basis of the metabolic demands of the cell, the thermal dependency of transporters, and changes in membrane lipids. We suggest that many of the observed relationships are not fixed physiological constraints but rather can be altered through adaptation. For example, the evolution of lipid metabolism can favor larger cells with higher lipid content to mitigate oxidative stress. These results have implications for rates of carbon sequestration and export in a warmer ocean.

An ATP-sensitive phosphoketolase regulates carbon fixation in cyanobacteria

Regulation of CO2 fixation in cyanobacteria is important both for the organism and global carbon balance. Here we show that phosphoketolase in Synechococcus elongatus PCC7942 (SeXPK) possesses a distinct ATP-sensing mechanism, where a drop in ATP level allows SeXPK to divert precursors of the RuBisCO substrate away from the Calvin-Benson-Bassham cycle. Deleting the SeXPK gene increased CO2 fixation particularly during light-dark transitions. In high-density cultures, the Δxpk strain showed a 60% increase in carbon fixation and unexpectedly resulted in sucrose secretion without any pathway engineering. Using cryo-EM analysis, we discovered that these functions were enabled by a unique allosteric regulatory site involving two subunits jointly binding two ATP, which constantly suppresses the activity of SeXPK until the ATP level drops. This magnesium-independent ATP allosteric site is present in many species across all three domains of life, where it may also play important regulatory functions.

Does temperature favour the spread of Raphidiopsis raciborskii, an invasive bloom-forming cyanobacterium, by altering cellular trade-offs?

As a tropical filamentous cyanobacterium, Raphidiopsis raciborskii has attracted much attention due to its expansion and toxin production. However, the mechanisms of its expansion to temperate regions have not been studied in detail. To address the potential strategies, the physiological and metabolomic profiles of R. raciborskii FACHB 1096 isolated from a temperate lake in China were determined and measured at different temperatures (10 °C, 15 °C, 20 °C, 25 °C, and 32 °C). The results demonstrated that temperature significantly changed cell viability, chlorophyll a content, specific growth rate, Chl a fluorescence, and filamentous shape of R. raciborskii. Low temperature decreased cell viability, specific growth rate, and photosynthetic efficiency, while the proportion of akinete and carbon fixation per unit cell were significantly increased compared with high temperature (32 °C). A constructed unimodal model indicated that filament length, cell volume, and cell length/width of R. raciborskii were significantly reduced in both high and low temperature environments. Under low-temperature conditions, R. raciborskii suffered different degrees of oxidative damage and produced corresponding antioxidant substances to resist oxidative stress, suggesting that low temperature changes the metabolic level of the cells, causing the cells to gradually switch from development to defense. Metabolomic data further confirmed that temperature change induced shifts in metabolic pathways in R. raciborskii, including starch and sucrose metabolic pathways, glutathione metabolic pathways, and the pentose phosphate pathways (PPP), as well as metabolic pathways related to the tricarboxylic acid (TCA) cycle. Our results indicated that the trade-offs of R. raciborskii cells among the growth, cell size, and metabolites can be significantly regulated by temperature, with broad implications for its global expansion in temperate waterbodies.

Predicting the impact of temperature on metabolic fluxes using resource allocation modelling: Application to polyphosphate accumulating organisms

The understanding of microbial communities and the biological regulation of its members is crucial for implementation of novel technologies using microbial ecology. One poorly understood metabolic principle of microbial communities is resource allocation and biosynthesis. Resource allocation theory in polyphosphate accumulating organisms (PAOs) is limited as a result of their slow imposed growth rate (typical sludge retention times of at least 4 days) and limitations to quantify changes in biomass components over a 6 hours cycle (less than 10% of their growth). As a result, there is no direct evidence supporting that biosynthesis is an exclusive aerobic process in PAOs that alternate continuously between anaerobic and aerobic phases. Here, we apply resource allocation metabolic flux analysis to study the optimal phenotype of PAOs over a temperature range of 4 °C to 20 °C. The model applied in this research allowed to identify optimal metabolic strategies in a core metabolic model with limited constraints based on biological principles. The addition of a constraint limiting biomass synthesis to be an exclusive aerobic process changed the metabolic behaviour and improved the predictability of the model over the studied temperature range by closing the gap between prediction and experimental findings. The results validate the assumption of limited anaerobic biosynthesis in PAOs, specifically "Candidatus Accumulibacter" related species. Interestingly, the predicted growth yield was lower, suggesting that there are mechanistic barriers for anaerobic growth not yet understood nor reflected in the current models of PAOs. Moreover, we identified strategies of resource allocation applied by PAOs at different temperatures as a result of the decreased catalytic efficiencies of their biochemical reactions. Understanding resource allocation is paramount in the study of PAOs and their currently unknown complex metabolic regulation, and metabolic modelling based on biological first principles provides a useful tool to develop a mechanistic understanding.

Recent functional insights into the magic role of (p)ppGpp in growth control

Rapid growth and survival are two key traits that enable bacterial cells to thrive in their natural habitat. The guanosine tetraphosphate and pentaphosphate [(p)ppGpp], also known as "magic spot", is a key second messenger inside bacterial cells as well as chloroplasts of plants and green algae. (p)ppGpp not only controls various stages of central dogma processes (replication, transcription, ribosome maturation and translation) and central metabolism but also regulates various physiological processes such as pathogenesis, persistence, motility and competence. Under extreme conditions such as nutrient starvation, (p)ppGpp-mediated stringent response is crucial for the survival of bacterial cells. This mini-review highlights some of the very recent progress on the key role of (p)ppGpp in bacterial growth control in light of cellular resource allocation and cell size regulation. We also briefly discuss some recent functional insights into the role of (p)ppGpp in plants and green algae from the angle of growth and development and further discuss several important open directions for future studies.

Acclimation traits determine the macromolecular basis of harmful dinoflagellate Alexandrium minutum in response to changing climate conditions

Ocean warming and acidification are expected to have profound impacts on the marine ecosystem, although the dinoflagellate Alexandrium minutum is reported to be acclimated to such conditions. However, it is unknown on the transition time scale how this species physiologically adjusts their element accumulation and associated resource allocation for this process. We designed a set of experiments to examine how different culture generations (1^st, 5^th, and 10^th) change their cell physiology, cellular quotas and macromolecular cellular contents related to functional processes in A. minutum grown with future (pCO₂, 1000 ppm; 25°C) and present (pCO₂, 400 ppm; 21°C) ocean conditions. The differing cell sizes and storage capacity at different generations confirmed that compared to ancestors (1^st generation), acclimation cells (10^th generation) gained increases in quota carbon (Q_C; 55%; [p < 0.05]) and quota phosphate (Q_P; 23% [ p < 0.05]). This variation in C:P and N:P influences was transition-specific and largely determined by phosphate-based molecules. It was observed that A. minutum was initially dependent on P molecules, which help cells act as alternative lipids for quick acclimation until N molecules resume carbon-based lipids for their long-term acclimation. Our study demonstrated that rising temperature and pCO₂ concentrations in ocean may increase A. minutum based on the comprehensive analysis of different physiological modifications, including its growth, element accumulation, transformation, and functional allocation.

N2 Fixation in Trichodesmium Does Not Require Spatial Segregation from Photosynthesis

The dominant marine filamentous N₂ fixer, Trichodesmium, conducts photosynthesis and N₂ fixation during the daytime. Because N₂ fixation is sensitive to O₂, some previous studies suggested that spatial segregation of N₂ fixation and photosynthesis is essential in Trichodesmium. However, this hypothesis conflicts with some observations where all the cells contain both photosystems and the N₂-fixing enzyme nitrogenase. Here, we construct a systematic model simulating Trichodesmium metabolism, showing that the hypothetical spatial segregation is probably useless in increasing the Trichodesmium growth and N₂ fixation, unless substances can efficiently transfer among cells with low loss to the environment. The model suggests that Trichodesmium accumulates fixed carbon in the morning and uses that in respiratory protection to reduce intracellular O₂ during the mid-daytime, when photosynthesis is downregulated, allowing the occurrence of N₂ fixation. A cell membrane barrier against O₂ and alternative non-O₂ evolving electron transfer also contribute to maintaining low intracellular O₂. Our study provides a mechanism enabling N₂ fixation despite the presence of photosynthesis across Trichodesmium. IMPORTANCE The filamentous Trichodesmium is a globally prominent marine nitrogen fixer. A long-standing paradox is that the nitrogen-fixing enzyme nitrogenase is sensitive to oxygen, but Trichodesmium conducts both nitrogen fixation and oxygen-evolving photosynthesis during the daytime. Previous studies using immunoassays reported that nitrogenase was limited in some specialized Trichodesmium cells (termed diazocytes), suggesting the necessity of spatial segregation of nitrogen fixation and photosynthesis. However, attempts using other methods failed to find diazocytes in Trichodesmium, causing controversy on the existence of the spatial segregation. Here, our physiological model shows that Trichodesmium can maintain low intracellular O₂ in mid-daytime and achieve feasible nitrogen fixation and growth rates even without the spatial segregation, while the hypothetical spatial segregation might not be useful if substantial loss of substances to the environment occurs when they transfer among the Trichodesmium cells. Our study then suggests a possible mechanism by which Trichodesmium can survive without the spatial segregation.

The paradoxes hidden behind the Droop model highlighted by a metabolic approach

We propose metabolic models for the haptophyte microalgae Tisochrysis lutea with different possible organic carbon excretion mechanisms. These models-based on the DRUM (Dynamic Reduction of Unbalanced Metabolism) methodology-are calibrated with an experiment of nitrogen starvation under day/night cycles, and then validated with nitrogen-limited chemostat culture under continuous light. We show that models including exopolysaccharide excretion offer a better prediction capability. It also gives an alternative mechanistic interpretation to the Droop model for nitrogen limitation, which can be understood as an accumulation of carbon storage during nitrogen stress, rather than the common belief of a nitrogen pool driving growth. Excretion of organic carbon limits its accumulation, which leads to a maximal C/N ratio (corresponding to the minimum Droop N/C quota). Although others phenomena-including metabolic regulations and dissipation of energy-are possibly at stake, excretion appears as a key component in our metabolic model, that we propose to include in the Droop model.

To Die or Not to Die-Regulated Cell Death and Survival in Cyanobacteria

Regulated cell death (RCD) is central to the development, integrity, and functionality of multicellular organisms. In the last decade, evidence has accumulated that RCD is a universal phenomenon in all life domains. Cyanobacteria are of specific interest due to their importance in aquatic and terrestrial habitats and their role as primary producers in global nutrient cycling. Current knowledge on cyanobacterial RCD is based mainly on biochemical and morphological observations, often by methods directly transferred from vertebrate research and with limited understanding of the molecular genetic basis. However, the metabolism of different cyanobacteria groups relies on photosynthesis and nitrogen fixation, whereas mitochondria are the central executioner of cell death in vertebrates. Moreover, cyanobacteria chosen as biological models in RCD studies are mainly colonial or filamentous multicellular organisms. On the other hand, unicellular cyanobacteria have regulated programs of cellular survival (RCS) such as chlorosis and post-chlorosis resuscitation. The co-existence of different genetically regulated programs in cyanobacterial populations may have been a top engine in life diversification. Development of cyanobacteria-specific methods for identification and characterization of RCD and wider use of single-cell analysis combined with intelligent image-based cell sorting and metagenomics would shed more light on the underlying molecular mechanisms and help us to address the complex colonial interactions during these events. In this review, we focus on the functional implications of RCD in cyanobacterial communities.

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”).

Predicting Metabolic Adaptation Under Dynamic Substrate Conditions Using a Resource-Dependent Kinetic Model: A Case Study Using Saccharomyces cerevisiae

Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism Saccharomyces cerevisiae by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in S. cerevisiae is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived “overcapacity” present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model’s ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.

Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metaproteomics. Different taxa, rather than different environmental conditions, formed distinct clusters based on their ribosomal and photosynthetic proteomic proportions, and we propose that these characteristics relate to ecological differences. We defined and used a proteomic proxy for regulatory cost, which showed that SAR11 had the lowest regulatory cost of any taxa we observed at our summertime Southern Ocean study site. Haptophytes had lower regulatory cost than diatoms, which may underpin haptophyte-to-diatom bloom progression in the Ross Sea. We were able to make these proteomic trait inferences by assessing various sources of bias in metaproteomics, providing practical recommendations for researchers in the field. We have quantified several proteomic traits (ribosomal and photosynthetic proteomic proportions, regulatory cost) in eukaryotic and bacterial taxa, which can then be incorporated into trait-based models of microbial communities that reflect resource allocation strategies.

Discovery of nondiazotrophic Trichodesmium species abundant and widespread in the open ocean

Filamentous and colony-forming cells within the cyanobacterial genus Trichodesmium might account for nearly half of nitrogen fixation in the sunlit ocean, a critical mechanism that sustains plankton's primary productivity. Trichodesmium has long been portrayed as a diazotrophic genus. By means of genome-resolved metagenomics, here we reveal that nondiazotrophic Trichodesmium species not only exist but also are abundant and widespread in the open ocean, benefiting from a previously overlooked functional lifestyle to expand the biogeography of this prominent marine genus. Near-complete environmental genomes for those closely related candidate species reproducibly shared functional features including a lack of genes related to nitrogen fixation, hydrogen recycling, and hopanoid lipid production concomitant with the enrichment of nitrogen assimilation genes. Our results elucidate fieldwork observations of Trichodesmium cells fixing carbon but not nitrogen. The Black Queen hypothesis and burden of low-oxygen concentration requirements provide a rationale to explain gene loss linked to nitrogen fixation among Trichodesmium species. Disconnecting taxonomic signal for this genus from a microbial community's ability to fix nitrogen will help refine our understanding of the marine nitrogen balance. Finally, we are reminded that established links between taxonomic lineages and functional traits do not always hold true.

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis

BACKGROUND

The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings.

RESULTS

In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology.

CONCLUSIONS

We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

Modeling photosynthetic resource allocation connects physiology with evolutionary environments

The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extent do they reflect ancestral environments? Here, we explore these questions for C₃, C₄, and C₃–C₄ intermediate plants of the model genus Flaveria. We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns. Models of C₄ plants optimized for conditions experienced by evolutionary ancestors perform better than models accounting for experimental growth conditions, indicating low phenotypic plasticity. Supporting this interpretation, the model predicts that C₄ species need to re-allocate more nitrogen between photosynthetic components than C₃ species to adapt to new environments. We thus hypothesize that observed resource distribution patterns in C₄ plants still reflect optimality in ancestral environments, allowing the quantitative inference of these environments from today’s plants. Our work allows us to quantify environmental effects on photosynthetic resource allocation and performance in the light of evolutionary history.

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

Escherichia coli is capable of growing in many environments, each of which requires a different collection of enzymes to metabolize the nutrients within that environment. Each individual enzyme requires its own set of amino acids and oftentimes cofactors, which are accessory molecules essential for the enzyme to function. Thus, the composition of the micronutrients (amino acids, cofactors, etc.) within a cell will differ depending on its metabolic needs. The presented work is the first effort to employ metabolic models to probe the connection between E. coli’s diverse growth environments and its biomass composition. We first show how differences in model-predicted enzyme use for aerobic or anaerobic growth results in distinct amino acid and cofactor usage. Alternatively, we show that the metabolic models can predict how modifying the cell’s biomass composition will affect growth. For example, by modeling the exposure of E. coli to trimethoprim or sulfamethoxazole—two antibiotics that target folate (vitamin B9) synthesis—we predicted how E. coli could adapt to grow under folate-limited conditions. This work demonstrates how models can be used to study antibiotic resistance of drugs that target amino acid or cofactor synthesis.

Slow Protein Turnover Explains Limited Protein-Level Response to Diurnal Transcriptional Oscillations in Cyanobacteria

Metabolically engineered cyanobacteria have the potential to mitigate anthropogenic CO₂ emissions by converting CO₂ into renewable fuels and chemicals. Yet, better understanding of metabolic regulation in cyanobacteria is required to develop more productive strains that can make industrial scale-up economically feasible. The aim of this study was to find the cause for the previously reported inconsistency between oscillating transcription and constant protein levels under day-night growth conditions. To determine whether translational regulation counteracts transcriptional changes, Synechocystis sp. PCC 6803 was cultivated in an artificial day-night setting and the level of transcription, translation and protein was measured across the genome at different time points using mRNA sequencing, ribosome profiling and quantitative proteomics. Furthermore, the effect of protein turnover on the amplitude of protein oscillations was investigated through in silico simulations using a protein mass balance model. Our experimental analysis revealed that protein oscillations were not dampened by translational regulation, as evidenced by high correlation between translational and transcriptional oscillations (r = 0.88) and unchanged protein levels. Instead, model simulations showed that these observations can be attributed to a slow protein turnover, which reduces the effect of protein synthesis oscillations on the protein level. In conclusion, these results suggest that cyanobacteria have evolved to govern diurnal metabolic shifts through allosteric regulatory mechanisms in order to avoid the energy burden of replacing the proteome on a daily basis. Identification and manipulation of such mechanisms could be part of a metabolic engineering strategy for overproduction of chemicals.

Selective Uptake of Pelagic Microbial Community Members by Caribbean Reef Corals

We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play.

Coral reefs are possible sinks for microbes; however, the removal mechanisms at play are not well understood. Here, we characterize pelagic microbial groups at the CARMABI reef (Curaçao) and examine microbial consumption by three coral species: Madracis mirabilis, Porites astreoides, and Stephanocoenia intersepta. Flow cytometry analyses of water samples collected from a depth of 10 m identified 6 microbial groups: Prochlorococcus, three groups of Synechococcus, photosynthetic eukaryotes, and heterotrophic bacteria. Minimum growth rates (μ) for Prochlorococcus, all Synechococcus groups, and photosynthetic eukaryotes were 0.55, 0.29, and 0.45 μ day⁻¹, respectively, and suggest relatively high rates of productivity despite low nutrient conditions on the reef. During a series of 5-h incubations with reef corals performed just after sunset or prior to sunrise, reductions in the abundance of photosynthetic picoeukaryotes, Prochlorococcus and Synechococcus cells, were observed. Of the three Synechococcus groups, one decreased significantly during incubations with each coral and the other two only with M. mirabilis. Removal of carbon from the water column is based on coral consumption rates of phytoplankton and averaged between 138 ng h⁻¹ and 387 ng h⁻¹, depending on the coral species. A lack of coral-dependent reduction in heterotrophic bacteria, differences in Synechococcus reductions, and diurnal variation in reductions of Synechococcus and Prochlorococcus, coinciding with peak cell division, point to selective feeding by corals. Our study indicates that bentho-pelagic coupling via selective grazing of microbial groups influences carbon flow and supports heterogeneity of microbial communities overlying coral reefs.

IMPORTANCE We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play. Peaks in removal rates of Prochlorococcus and Synechococcus cyanobacteria appear highest during postsunset incubations and coincide with microbial cell division. Grazing rates and effort vary across coral species and picoplankton groups, possibly influencing overall microbial composition and abundance over coral reefs. For reef corals, use of such a numerically abundant source of nutrition may be advantageous, especially under environmentally stressful conditions when symbioses with dinoflagellate algae break down.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner-Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

Effects of rising atmospheric CO2 levels on physiological response of cyanobacteria and cyanobacterial bloom development: A review

Increasing atmospheric CO₂ concentration negatively impacts aquatic ecosystems and may exacerbate the problem of undesirable cyanobacterial bloom development in freshwater ecosystems. Elevated levels of atmospheric CO₂ may increase the levels of dissolved CO₂ in freshwater systems, via air-water exchanges, enhancing primary production in the water and catchments. Although high CO₂ levels improve cyanobacterial growth and increase cyanobacterial biomass, the impacts on their internal physiological processes can be more complex. Here, we have reviewed previous studies to evaluate the physiological responses of cyanobacteria to high concentrations of CO₂. In response to high CO₂ concentrations, the pressures of inorganic carbon absorption are reduced, and carbon concentration mechanisms are downregulated, affecting the intracellular metabolic processes and competitiveness of the cyanobacteria. Nitrogen and phosphorus metabolism and light utilization are closely related to CO₂ assimilation, and these processes are likely to be affected by resource and energy reallocation when CO₂ levels are high. Additionally, the responses of diazotrophic and toxic cyanobacteria to elevated CO₂ levels were specifically reviewed. The responses of diazotrophic cyanobacteria to elevated CO₂ concentrations were found to be inconsistent, probably because of differences in other factors in experimental designs. Toxic cyanobacteria tended to be superior to non-toxic strains at low levels of CO₂; however, the specific effects of microcystin on the regulation require further investigation. Furthermore, the effects of increasing CO₂ levels on cyanobacterial competitiveness in phytoplankton communities and nutrient cycling in aquatic ecosystems were reviewed. High CO₂ concentrations may make cyanobacteria less competitive relative to other algal taxa; however, due to the complexity of natural systems and the specificity of algal species, the dominant positions of the cyanobacteria do not seems to be changed. To better understand cyanobacterial responses to elevated CO₂ levels and help control cyanobacterial bloom developments, this review has identified key areas for future research.

The promise of dawn: Microalgae photoacclimation as an optimal control problem of resource allocation

Photosynthetic microorganisms are known to adjust their photosynthetic capacity according to light intensity. This so-called photoacclimation process is thought to maximize growth at equilibrium, but its dynamics under varying conditions remains less understood. To tackle this problem, microalgae growth and photoacclimation are represented by a (coarse-grained) resource allocation model. Using optimal control theory (the Pontryagin maximum principle) and numerical simulations, we determine the optimal strategy of resource allocation to maximize microalgal growth rate over a time horizon. We show that, after a transient, the optimal trajectory approaches the optimal steady state, a behavior known as the turnpike property. Then, a bi-level optimization problem is solved numerically to estimate model parameters from experimental data. The fitted trajectory represents well a Dunaliella tertiolecta culture facing a light down-shift. Finally, we study photoacclimation dynamics under day/night cycle. In the optimal trajectory, the synthesis of the photosynthetic apparatus surprisingly starts a few hours before dawn. This anticipatory behavior has actually been observed both in the laboratory and in the field. This shows the algal predictive capacity and the interest of our method which predicts this phenomenon.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Alternative Crassulacean Acid Metabolism Modes Provide Environment-Specific Water-Saving Benefits in a Leaf Metabolic Model

Crassulacean acid metabolism (CAM) evolved in arid environments as a water-saving alternative to C₃ photosynthesis. There is great interest in engineering more drought-resistant crops by introducing CAM into C₃ plants. However, it is unknown whether full CAM or alternative water-saving modes would be more productive in the environments typically experienced by C₃ crops. To study the effect of temperature and relative humidity on plant metabolism in the context of water saving, we coupled a time-resolved diel (based on a 24-h day-night cycle) model of leaf metabolism to an environment-dependent gas-exchange model. This combined model allowed us to study the emergence of CAM as a trade-off between leaf productivity and water saving. We show that vacuolar storage capacity in the leaf is a major determinant of the extent of CAM. Moreover, our model identified an alternative CAM cycle involving mitochondrial isocitrate dehydrogenase as a potential contributor to initial carbon fixation at night. Simulations across a range of environmental conditions show that the water-saving potential of CAM strongly depends on the daytime weather conditions and that the additional water-saving effect of carbon fixation by isocitrate dehydrogenase can reach 11% total water saving for the conditions tested.

Phytoplankton Temporal Strategies Increase Entropy Production in a Marine Food Web Model

We develop a trait-based model founded on the hypothesis that biological systems evolve and organize to maximize entropy production by dissipating chemical and electromagnetic free energy over longer time scales than abiotic processes by implementing temporal strategies. A marine food web consisting of phytoplankton, bacteria, and consumer functional groups is used to explore how temporal strategies, or the lack thereof, change entropy production in a shallow pond that receives a continuous flow of reduced organic carbon plus inorganic nitrogen and illumination from solar radiation with diel and seasonal dynamics. Results show that a temporal strategy that employs an explicit circadian clock produces more entropy than a passive strategy that uses internal carbon storage or a balanced growth strategy that requires phytoplankton to grow with fixed stoichiometry. When the community is forced to operate at high specific growth rates near 2 d⁻¹, the optimization-guided model selects for phytoplankton ecotypes that exhibit complementary for winter versus summer environmental conditions to increase entropy production. We also present a new type of trait-based modeling where trait values are determined by maximizing entropy production rather than by random selection.

Engineering biology approaches for food and nutrient production by cyanobacteria

As photoautotrophic organisms, cyanobacteria capture and store solar energy in the form of biomass. Cyanobacterial biomass has been an important component of diet and nutrition in several regions for centuries. Synthetic biology strategies are currently being applied to increase the yield and productivity of cyanobacterial biomass by optimizing solar energy utilization and CO₂ fixation rates for carbon storage. Likewise, engineering cyanobacteria as cellular factories to synthesize carbohydrates, amino acids, proteins, lipids and fatty acids is providing an attractive way to sustainably produce food and nutrients for human consumption. In this review, we have summarized recent progress in both aspects and prospective trends under development.

Using optimal control to understand complex metabolic pathways

BACKGROUND

Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria.

RESULTS

Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments.

CONCLUSIONS

We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle

Cyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

•

We identify and quantify 67 polar intermediate metabolites in cyanobacteria via LC-MS

•

A number of metabolites show large variations during the diurnal cycle

•

Intermediates of the CBB cycle peak at midday, coinciding with peak in growth rate

•

Gamma-glutamyl dipeptides identified as new storage compounds that peak at dawn

Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

Integrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events. In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting. With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers.

Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana

Availability of plant-specific enzyme kinetic data is scarce, limiting the predictive power of metabolic models and precluding identification of genetic factors of enzyme properties. Enzyme kinetic data are measured in vitro, often under non-physiological conditions, and conclusions elicited from modeling warrant caution. Here we estimate maximal in vivo catalytic rates for 168 plant enzymes, including photosystems I and II, cytochrome-b6f complex, ATP-citrate synthase, sucrose-phosphate synthase as well as enzymes from amino acid synthesis with previously undocumented enzyme kinetic data in BRENDA. The estimations are obtained by integrating condition-specific quantitative proteomics data, maximal rates of selected enzymes, growth measurements from Arabidopsis thaliana rosette with and fluxes through canonical pathways in a constraint-based model of leaf metabolism. In comparison to findings in Escherichia coli, we demonstrate weaker concordance between the plant-specific in vitro and in vivo enzyme catalytic rates due to a low degree of enzyme saturation. This is supported by the finding that concentrations of nicotinamide adenine dinucleotide (phosphate), adenosine triphosphate and uridine triphosphate, calculated based on our maximal in vivo catalytic rates, and available quantitative metabolomics data are below reported KM values and, therefore, indicate undersaturation of respective enzymes. Our findings show that genome-wide profiling of enzyme kinetic properties is feasible in plants, paving the way for understanding resource allocation.

A universal trade-off between growth and lag in fluctuating environments

The rate of cell growth is crucial for bacterial fitness and drives the allocation of bacterial resources, affecting, for example, the expression levels of proteins dedicated to metabolism and biosynthesis1,2. It is unclear, however, what ultimately determines growth rates in different environmental conditions. Moreover, increasing evidence suggests that other objectives are also important3-7, such as the rate of physiological adaptation to changing environments8,9. A common challenge for cells is that these objectives cannot be independently optimized, and maximizing one often reduces another. Many such trade-offs have indeed been hypothesized on the basis of qualitative correlative studies8-11. Here we report a trade-off between steady-state growth rate and physiological adaptability in Escherichia coli, observed when a growing culture is abruptly shifted from a preferred carbon source such as glucose to fermentation products such as acetate. These metabolic transitions, common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. Metabolomic analysis reveals that long lags result from the depletion of key metabolites that follows the sudden reversal in the central carbon flux owing to the imposed nutrient shifts. A model of sequential flux limitation not only explains the observed trade-off between growth and adaptability, but also allows quantitative predictions regarding the universal occurrence of such tradeoffs, based on the opposing enzyme requirements of glycolysis versus gluconeogenesis. We validate these predictions experimentally for many different nutrient shifts in E. coli, as well as for other respiro-fermentative microorganisms, including Bacillus subtilis and Saccharomyces cerevisiae.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Dynamic metabolic resource allocation based on the maximum entropy principle

Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing 'bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.

Formalizing Metabolic-Regulatory Networks by Hybrid Automata

Computational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control.

A Mechanistic Model of Macromolecular Allocation, Elemental Stoichiometry, and Growth Rate in Phytoplankton

We present a model of the growth rate and elemental stoichiometry of phytoplankton as a function of resource allocation between and within broad macromolecular pools under a variety of resource supply conditions. The model is based on four, empirically-supported, cornerstone assumptions: that there is a saturating relationship between light and photosynthesis, a linear relationship between RNA/protein and growth rate, a linear relationship between biosynthetic proteins and growth rate, and a constant macromolecular composition of the light-harvesting machinery. We combine these assumptions with statements of conservation of carbon, nitrogen, phosphorus, and energy. The model can be solved algebraically for steady state conditions and constrained with data on elemental stoichiometry from published laboratory chemostat studies. It interprets the relationships between macromolecular and elemental stoichiometry and also provides quantitative predictions of the maximum growth rate at given light intensity and nutrient supply rates. The model is compatible with data sets from several laboratory studies characterizing both prokaryotic and eukaryotic phytoplankton from marine and freshwater environments. It is conceptually simple, yet mechanistic and quantitative. Here, the model is constrained only by elemental stoichiometry, but makes predictions about allocation to measurable macromolecular pools, which could be tested in the laboratory.

Combining Molecular Observations and Microbial Ecosystem Modeling: A Practical Guide

Advances in technologies for molecular observation are leading to novel types of data, including gene, transcript, protein, and metabolite levels, which are fundamentally different from the types traditionally compared with microbial ecosystem models, such as biomass (e.g., chlorophyll a) and nutrient concentrations. A grand challenge is to use these data to improve predictive models and use models to explain observed patterns. This article presents a framework that aligns observations and models along the dimension of abstraction or biological organization-from raw sequences to ecosystem patterns for observations, and from sequence simulators to ecological theory for models. It then reviews 16 studies that compared model results with molecular observations. Molecular data can and are being combined with microbial ecosystem models, but to keep up with and take advantage of the full scope of observations, models need to become more mechanistically detailed and complex, which is a technical and cultural challenge for the ecological modeling community.

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.

Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems

Ecosystems are controlled by 'bottom-up' (resources) and 'top-down' (predation) forces. Viral infection is now recognized as a ubiquitous top-down control of microbial growth across ecosystems but, at the same time, cell death by viral predation influences, and is influenced by, resource availability. In this Review, we discuss recent advances in understanding the biogeochemical impact of viruses, focusing on how metabolic reprogramming of host cells during lytic viral infection alters the flow of energy and nutrients in aquatic ecosystems. Our synthesis revealed several emerging themes. First, viral infection transforms host metabolism, in part through virus-encoded metabolic genes; the functions performed by these genes appear to alleviate energetic and biosynthetic bottlenecks to viral production. Second, viral infection depends on the physiological state of the host cell and on environmental conditions, which are challenging to replicate in the laboratory. Last, metabolic reprogramming of infected cells and viral lysis alter nutrient cycling and carbon export in the oceans, although the net impacts remain uncertain. This Review highlights the need for understanding viral infection dynamics in realistic physiological and environmental contexts to better predict their biogeochemical consequences.

Circadian clock helps cyanobacteria manage energy in coastal and high latitude ocean

The circadian clock coordinates cellular functions over the diel cycle in many organisms. The molecular mechanisms of the cyanobacterial clock are well characterized, but its ecological role remains a mystery. We present an agent-based model of Synechococcus (harboring a self-sustained, bona fide circadian clock) that explicitly represents genes (e.g., kaiABC), transcripts, proteins, and metabolites. The model is calibrated to data from laboratory experiments with wild type and no-clock mutant strains, and it successfully reproduces the main observed patterns of glycogen metabolism. Comparison of wild type and no-clock mutant strains suggests a main benefit of the clock is due to energy management. For example, it inhibits glycogen synthesis early in the day when it is not needed and energy is better used for making the photosynthesis apparatus. To explore the ecological role of the clock, we integrate the model into a dynamic, three-dimensional global circulation model that includes light variability due to seasonal and diel incident radiation and vertical extinction. Model output is compared with field data, including in situ gene transcript levels. We simulate cyanobaceria with and without a circadian clock, which allows us to quantify the fitness benefit of the clock. Interestingly, the benefit is weakest in the low latitude open ocean, where Prochlorococcus (lacking a self-sustained clock) dominates. However, our attempt to experimentally validate this testable prediction failed. Our study provides insights into the role of the clock and an example for how models can be used to integrate across multiple levels of biological organization.