Stoichiometric Network Analysis of Cyanobacterial Acclimation to Photosynthesis-Associated Stresses Identifies Heterotrophic Niches

Abstract

Metabolic acclimation to photosynthesis-associated stresses was examined in the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 using integrated computational and photobioreactor analyses. A genome-enabled metabolic model, complete with measured biomass composition, was analyzed using ecological resource allocation theory to predict and interpret metabolic acclimation to irradiance, O₂, and nutrient stresses. Reduced growth efficiency, shifts in photosystem utilization, changes in photorespiration strategies, and differing byproduct secretion patterns were predicted to occur along culturing stress gradients. These predictions were compared with photobioreactor physiological data and previously published transcriptomic data and found to be highly consistent with observations, providing a systems-based rationale for the culture phenotypes. The analysis also indicated that cyanobacterial stress acclimation strategies created niches for heterotrophic organisms and that heterotrophic activity could enhance cyanobacterial stress tolerance by removing inhibitory metabolic byproducts. This study provides mechanistic insight into stress acclimation strategies in photoautotrophs and establishes a framework for predicting, designing, and engineering both axenic and photoautotrophic-heterotrophic systems as a function of controllable parameters.

1. Introduction

Environmental stresses dictate competitive ecological strategies impacting nutrient and energy flows from the scale of individual cells to ecosystems [1,2]. Cyanobacteria are significant drivers of global nutrient and energy flows, accounting for ~10% of global primary productivity [3] and forming essential links in carbon and nitrogen biogeochemical cycles [4]. Cyanobacteria are also used in wastewater treatment and as bioprocess catalysts for bioproduction of specialty chemicals [5,6]. Cyanobacteria are deeply rooted in the tree of life and have adapted competitively to common stressors associated with photosynthesis and are model organisms for examining metabolic acclimation to these stresses.

Photoinhibition is a broad term encompassing different types of photosynthesis-associated stresses including photo-damage by excitation, damage by reactive oxygen species (ROS), and high localized O₂ concentrations [7]. Cyanobacteria can mitigate photo-damage by downregulating synthesis of photosystems, as well as adjusting relative photon absorption at photosystems I and II (PSI, PSII) to modulate ATP and NADPH regeneration (PSII extracts electrons from water which can be used in conjunction with PSI to regenerate NADPH and ATP, while PSI operating alone recycles electrons to regenerate ATP only; see Figure 1) [8]. High excitation can lead to oxidative damage at the photosystems and/or a highly reduced electron transport chain, which may also lead to cellular oxidative damage via ROS. Acclimation strategies include directing excess electrons toward alternative biochemical routes, such as reduction of O₂ (by either cellular respiration or the water-water cycle (photoreduction of O₂ to water)) or secretion of reduced carbon byproducts. High rates of oxygenic photosynthesis can also lead to locally high O₂ levels [9,10], and environments with high concentrations of O₂ relative to CO₂ can cause additional metabolic stress. Ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO) is a dual-functioning enzyme which can react with either CO₂ or O₂. When RuBisCO reacts with O₂, 2-phosphoglycolate is produced, which is either secreted as the inhibitory compound glycolate or catabolized using one of three photorespiration pathways found in cyanobacteria [11]. Cyanobacteria have evolved mechanisms to reduce O₂ consumption at RuBisCO, including species-specific enzymes with varying affinities for CO₂ and O₂, as well as expression of carboxysomes to increase the relative CO₂ concentration in the vicinity of RuBisCO [12,13].

Stoichiometric modeling of metabolism enables prediction and interpretation of system-wide properties of complex metabolic networks, including community-level networks [14,15,16,17,18,19,20]. These systems biology approaches, such as flux balance analysis (FBA) and elementary flux mode analysis (EFMA), use genomic and physiological data to inform the construction of computational representations of metabolism. The application of a steady state assumption simplifies the mass-balanced metabolic reactions into a series of solvable linear equations, reducing the need for difficult-to-measure, condition-dependent enzyme kinetic parameters [16]. Whereas FBA uses objective functions such as biomass production to predict an optimal flux distribution under a specific set of conditions, EFMA calculates the complete set of minimal pathways (elementary flux modes, EFMs) through a metabolic network using steady state, reaction reversibility, and indecomposability constraints. Non-negative linear combinations of EFMs define the entire phenotypic solution space of a steady state metabolic network using a single simulation and can be used to examine all possible physiologies in an unbiased manner [19,21]. Similarities and differences in the output of EFMA versus other stoichiometric modeling techniques can be found in the review by Trinh et al. [19]. The enumerated EFMs can be evaluated by resource allocation theory, which quantitatively assesses the computational phenotypic space according to tradeoffs in consumption of different resources for the production of bioproducts [22,23,24,25,26]. Previous stoichiometric modeling studies of cyanobacterial metabolism have examined the occurrence of photorespiration as well as irradiance and carbon limitations [27,28,29,30].

The presented study analyzes metabolic acclimation to photosynthesis-associated stresses in the thermophilic, non-diazotrophic unicellular cyanobacterium Thermosynechococcus elongatus BP-1 (hereafter BP-1) and the formation of heterotrophic niches. BP-1 was isolated from the alkaline (pH 8.6) Beppu hot springs in Japan where temperatures range from 50–65 °C [31,32]. BP-1 is a major primary producer in its native hot springs where it often grows in bacterial mat communities with heterotrophs and is subject to high irradiance, high O₂, and low nutrient availability stresses. The objectives of this study were to (i) identify ecologically relevant acclimation strategies to high irradiance, O₂/CO₂ competition at RuBisCO, and nutrient limitation at varying degrees using a computational BP-1 stoichiometric model and EFMA combined with resource allocation theory, (ii) analyze BP-1 acclimation to high irradiance through controlled photobioreactors, (iii) compare general computational predictions to specific photobioreactor observations to interpret BP-1 acclimation strategies, and (iv) examine the impact of stress acclimation strategies on the ability of BP-1 to interact with heterotrophic partners. The presented study contributes to the understanding of cyanobacterial metabolism by examining specific photorespiration pathways, relative photon absorption of the photosystems, and byproduct secretion profiles under simultaneous stress conditions of high irradiance and O₂/CO₂ competition at RuBisCO, as well as by predicting cross-feeding photoautotrophic-heterotrophic interactions. The computational resource allocation-based modeling integrated with photobioreactor observations provides a rational basis for interpreting natural cyanobacterial behavior and a framework for controlling cyanobacteria for bioprocess applications.

2. Materials and Methods

2.1. Photobioreactor Culturing

T. elongatus BP-1 cultures were grown using modified BG-11 (mBG-11) medium [33,34], containing 17.6 mM NaNO₃, 0.304 mM MgSO₄·7H₂O, 0.175 mM KH₂PO₄, 0.245 mM CaCl₂·2H₂O, 0.0028 mM Na₂EDTA, and 0.0144 mM FeCl₃. A trace metal supplement was added (1 mL/L), comprised of 46.254 mM H₃BO₃, 9.146 mM MnCl₂·4H₂O, 0.772 mM ZnSO₄·7H₂O, 1.611 mM Na₂MoO₄·2H₂O, 0.316 mM CuSO₄·5H₂O, and 0.170 mM Co(NO₃)₂·6H₂O. Inoculum cultures of BP-1 were initiated from frozen stocks into 150-mL sealed serum bottles filled with 50 mL mBG-11 amended with 15 mM sodium bicarbonate and adjusted to pH 7.5 under N₂ headspace containing 10% CO₂.

Photobioreactors were operated as turbidostats as described in Bernstein et al. [33], similar to Bernstein et al. and Melnicki et al. [35,36]. Reactors were inoculated with exponentially growing inoculum culture to OD_730nm = 0.01. All cultures were grown under continuous light of varying irradiances at 52 °C, pH 7.5, and were continuously sparged at 4 L min⁻¹ with a 98% N₂ and 2% CO₂ gas mixture. Incident and transmitted scalar irradiances were measured and used to adjust the turbidostat growth rate. The specific optical cross section (σ, m² (g CDW)⁻¹, CDW, cell dry weight) was determined according to a previously described method using a light diffuser and spectrophotometer [37]. The specific photon absorption rate was calculated by multiplying the specific optical cross section by the incident irradiance.

2.2. Biomass Composition Determination

Macromolecular composition was analyzed from turbidostat biomass samples (pelleted and frozen at Pacific Northwest National Laboratory and then shipped to Montana State University for subsequent analysis) according to the following procedures. DNA was quantified from alkali-lysed solutions with Hoechst 33258 fluorescent dye [38]. Glycogen was quantified by co-precipitation with sodium sulfate and detection with anthrone [39]. Lipids were quantified gravimetrically via chloroform-methanol extraction [40]. Total protein and amino acid distribution were quantified with HPLC fluorescence detection using o-phthalaldehyde (OPA) and 9-fluorenylmethylchloroformate (FMOC) derivatizations of acid-hydrolyzed protein [41]. Cysteine, methionine, and tryptophan were degraded, and asparagine and glutamine were converted to aspartate and glutamate, respectively, during hydrolysis [42]; therefore, abundances were predicted from protein-coding gene codon usage. RNA was quantified by lysis with potassium hydroxide, extraction into cold perchloric acid, and measurement of UV absorbance at 260 nm [43]. Appendix A contains detailed protocols for each method.

2.3. Model Construction

The metabolic network model for BP-1 was constructed in CellNetAnalyzer [44,45] from the annotated genome [46] with the aid of MetaCyc, KEGG, BRENDA, and NCBI databases [47,48,49]. Reversible exchange reactions were defined for protons and water. Irreversible exchange reactions defined bicarbonate, magnesium, nitrate, phosphate, photons, and sulfate as possible substrates and O₂, acetate, alanine, ethanol, formate, glycolate, lactate, pyruvate, and sucrose as possible byproducts. Biomass was also defined as a product.

Macromolecular synthesis reactions were defined for nucleic acids, glycogen (most common form of cyanobacterial carbohydrate storage [50]), lipid, and protein. Synthesis reactions utilized two phosphate bonds per nucleic acid monomer, one phosphate bond per glycogen monomer, and four phosphate bonds per protein monomer [51]. Nucleotide distributions were set based on percent GC content of the genome for DNA and nucleotide sequence of the rRNA genes for RNA. Fatty acid distribution was assigned based on literature values of fatty acid chain and lipid types measured for BP-1 [52,53,54]. The amino acid distribution was set using the experimentally measured values in the current study. Macromolecular composition (DNA, glycogen, lipid (including chlorophyll), protein, and RNA) was determined experimentally in the current study (see Section 2.2) and used to set the molar coefficients in the biomass synthesis reaction, normalized to 1 kg dry biomass (File S1 in the Supplementary Materials). Chlorophyll was also included in biomass synthesis using the mass fraction measured for Synechococcus sp. PCC 7002 [29], and the lipid mass fraction was adjusted to reflect the proportion of chlorophyll. The biomass composition was converted into an electron requirement using degree of reduction (moles of electrons per mole of carbon) calculations [55] with the assumption that each biosynthetic electron requires two photons (one absorbed at each PSII and PSI). Degree of reduction was calculated with respect to nitrate as a nitrogen source. To estimate photons necessary for ATP regeneration, the phosphate bond requirement for polymerization of monomers into macromolecules was converted into a photon requirement via a stoichiometry of four photons per phosphate bond (one photon absorbed at PSI per proton pumped, with four protons translocated per ATP molecule synthesized). Photon and proton stoichiometries remain active areas of research, and this estimate is recognized as an upper bound considering linear photosynthesis without a Q-cycle [56,57].

All reactions were balanced for elements, charge, and electrons. Thermodynamic considerations were built into the model via reaction reversibilities, based on data from BRENDA [49]; in the event that data for bacterial species were not available from BRENDA, thermodynamic calculations were performed with eQuilibrator (http://equilibrator.weizmann.ac.il/) to determine physiological reversibility, using a product concentration three orders of magnitude greater than the reactant concentration [58,59]. Nitrogen requirements were determined for each reaction by summing the number of nitrogen atoms specified by the enzyme amino acid sequences. Iron requirements were determined for central carbon metabolism and photosynthesis reactions based on metal requirements of similar cyanobacterial species in BRENDA [49]. For instances of missing or conflicting information in the database, literature values compiled for oxygenic photoautotrophs were used [60]. A one-to-one (minimal resource investment) correspondence of enzyme to reaction was used to calculate the total cost per EFM, as it has previously been shown to provide a good approximation of flux distributions in Escherichia coli [23,24]. EFMs were enumerated using EFMtool [61]. Resource allocation analysis (cost assessment) of the resulting EFMs was performed with MATLAB and Python. The metabolic model with supporting details and CellNetAnalyzer metabolite and reaction input, SBML model version, and documented analysis routines can be found in the Supplementary Materials (Files S1–S4).

3. Results

3.1. Computational BP-1 Metabolic Model and Photobioreactor Biomass Composition Measurement

The BP-1 computational metabolic model was constructed from the annotated genome [46]. Genetic potential was mapped to enzymes and metabolic reactions which encompassed photosynthesis, central metabolism, and biosynthetic reactions leading to biomass production according to a defined macromolecular composition reaction. Transport reactions were defined for nutrient uptake and product secretion. Subsequent EFMA resulted in a description of the phenotypic space spanning the range of possible nutrient uptake and product secretion rates, which could then be analyzed for ecologically relevant stress acclimation strategies. The model accounted for 334 metabolism-associated genes which were mapped to 279 metabolites and 284 reactions (File S1 in the Supplementary Materials). Photons were assumed to be within the spectrum of photosynthetically active radiation (PAR; 400–700 nm). A stoichiometrically balanced schematic demonstrating operation of the photosynthetic electron transport chain (linear and cyclic photosynthesis) and carbon flow in the model is shown in Figure 1. Nutrient substrates for the model were selected in alignment with the photobioreactor culturing medium. Bicarbonate was modeled as the sole carbon source based on culturing pH while interconversion with CO₂ was modeled via the carboxysomal carbonic anhydrase enzyme, and nitrate was modeled as the sole nitrogen source. Two of the three photorespiration pathways possible in cyanobacteria [11] were identified in BP-1, namely, the C2 cycle and the glycerate pathway. A variety of organic byproducts (

Table 1. T. elongatus BP-1 metabolic model inputs and outputs, including potential reduced carbon byproducts, with corresponding degree of reduction.

	Compound	Formula	Charge	Degree of Reduction
Inputs	Carbon dioxide	CO2	0	0
	Water	H2O	0	0
	Photons	NA	NA	NA
	Nitrate	NO3	−1	−8/−5/0
Outputs	Molecular oxygen	O2	0	−4
	Biomass	CH1.6N0.2O0.3P0.01S0.005	−0.7	4.3/5.0/6.1 a
	Acetate	C2H3O2	−1	4
	Alanine	C3H7NO2	0	4/5/6.7 a
	Ethanol	C2H6O	0	6
	Formate	CHO2	−1	2
	Glycolate	C2H3O3	−1	3
	Lactate	C3H5O3	−1	4
	Pyruvate	C3H3O3	−1	3.3
	Sucrose	C12H22O11	0	4

^a Degree of reduction calculated with respect to ammonia/molecular nitrogen/nitrate. NA, not applicable.

) were considered based on previous genomic analysis of BP-1 [62] and culturing studies of related unicellular cyanobacteria [63,64]. Secretion of several different amino acids has been observed in BP-1 and related species [33,63,64]; alanine was included as a representative amino acid byproduct in the current model, closely linked to central metabolism.

Biomass composition impacts growth and byproducts [65], making appropriate composition parameters important for computational growth predictions. BP-1 macromolecular biomass composition was determined analytically from continuous culture samples and was used to parameterize the model growth reactions. The major measured macromolecule classes (DNA, glycogen, lipid (including chlorophyll), protein, and RNA) summed to 98.1% of cell dry weight (

Table 2. Experimentally determined T. elongatus BP-1 biomass composition from turbidostat biomass samples grown under an irradiance of 2000 μmol photons m⁻² s⁻¹.

Macromolecule	Mass Percent	Extraction Method/Analytical Method
DNA	0.4	Alkaline lysis/Hoechst 33258 fluorescence
Glycogen	2.0	Sodium sulfate co-precipitation/Anthrone detection
Lipid (including chlorophyll)	17.4	Chloroform-methanol/Gravimetric
Protein	62.0	Hydrochloric acid hydrolysis/OPA, FMOC derivatization
RNA	16.3	Alkaline lysis, perchloric acid/UV absorbance
Total	98.1

); the remaining 1.9% was assumed to be ash. Protein and lipid/chlorophyll comprised the largest mass fractions of biomass, accounting for 62.0% and 17.4%, respectively. Since protein comprises the largest fraction of biomass, amino acid monomer distribution was also determined analytically (

Table A1. Experimentally measured amino acid distribution from OPA/FMOC derivatization and HPLC fluorescence detection. Amino acids are abbreviated according to IUPAC 1-letter convention; average mole percent of two separately hydrolyzed samples and percent relative standard deviation are reported.

Amino Acid	Mole %	% RSD
Q/E	13.1	0.16
N/D	10.1	0.07
L	9.4	0.08
A	9.2	0.24
R	8.5	0.96
V	6.1	0.97
I	5.4	0.29
G	5.0	0.02
F	4.9	0.15
T	4.9	0.07
K	4.5	1.96
Y	4.1	0.01
S	4.0	0.08
P	3.4	1.21
H	1.5	0.14

in Appendix C) and used to parameterize the model reaction for protein synthesis. A strong correlation was observed between the measured amino acid distribution and the distribution predicted from protein-coding gene sequences (Figure A1 in Appendix B).

3.2. Computational Analysis of Stress Acclimation

The computational BP-1 metabolic model was decomposed into 4,636,498 unique EFMs using EFMtool [61], with ~99.5% producing biomass. Each EFM, as well as any non-negative linear combination of multiple EFMs, represented a mathematically feasible phenotype and possible stress acclimation strategy. Competitive stress acclimation strategies were identified using ecological resource allocation theory. Resource allocation theory analyzes the amount of catabolic or anabolic resource required to synthesize a cellular product, often biomass. Minimizing the requirement of a limiting resource represents a competitive, cost-effective phenotype and is hypothesized to be a probable cellular strategy selected by evolution. When two or more resources are considered simultaneously, a multi-dimensional tradeoff surface is created that quantifies the utilization relationship between the limiting resources [22,23,24,25]. Biomass-producing EFMs were ranked quantitatively based on efficiency of resource use for biomass production under simulated environmental stresses including high irradiance, O₂/CO₂ competition at RuBisCO, and limited availability of dissolved inorganic carbon (DIC) as well as nitrogen or iron. The tradeoff between optimal use of two resources was quantified by simultaneously minimizing the cost of biomass production under two different stress factors. Similar methods have been applied to extend FBA to account for biosynthetic costs [26], but enumeration of complete EFMs combined with resource allocation theory allows exploration of the entire phenotypic space.

3.2.1. Irradiance and Photosynthetic Electron Flow

Photosynthetic electron flow was examined as a function of irradiance-induced stress to interpret relationships between photon absorption and photocatalytic water oxidation. Net O₂ production per carbon mole (Cmol) biomass produced was plotted as a function of photons absorbed per Cmol biomass produced, a metric of irradiance-induced stress (Figure 2). Each net O₂ molecule is the byproduct of four photosynthetically derived electrons extracted from water and requires eight total photons absorbed [66]; this relationship is reflected in the slope of the upper boundary of the phenotypic cone. Photons absorbed at PSI during cyclic photosynthesis are decoupled from O₂ production. Growth phenotypes were analyzed for the ability to direct electrons toward either biomass or reduced byproducts. The EFMs along the lowest boundary of the phenotypic cone in Figure 2 represented growth where all electrons were directed to biomass and no reduced byproducts were secreted, extending up to ~80 mol photons absorbed per Cmol biomass produced. Net O₂ production (~1.53 mol O₂ per Cmol biomass) at the lowest boundary corresponds to the biomass degree of reduction, ~6.1 mol electrons available to reduce O₂ per Cmol biomass (Table 1). EFMs with higher net O₂ production directed electrons to reduced carbon byproducts, such as formate or acetate.

Biomass-producing EFMs were assessed for photon absorption at PSII relative to PSI to quantify the contribution of the two photosystems to photosynthetic electron flow (Figure 2, shaded color bar). A value less than one indicated elevated cyclic photosynthesis, a value greater than one indicated elevated operation of PSII independent of linear photosynthesis (i.e., reduction of O₂ through either cellular respiration or the water-water cycle), and a value equal to one indicated linear photosynthesis or, alternatively, equivalent cyclic photosynthesis and O₂ reduction. Figure 1 provides greater detail on cyclic and linear photosynthesis. An increase in photon absorption at PSII at a fixed net O₂ production indicated greater gross production of O₂, which was consumed by cellular respiration and/or the water-water cycle. In general, photon absorption at PSII relative to PSI was predicted to increase as irradiance-induced stress increased (Figure 2), indicating a greater contribution of PSII to photon absorption at high irradiance.

3.2.2. Irradiance, High O₂, and Nutrient Limitation

Computational BP-1 growth phenotypes were interrogated for stress acclimation strategies under a range of relative O₂ to DIC concentrations, represented by O₂/CO₂ competition at RuBisCO (Figure 3a). The tradeoff curve simultaneously minimizes the cost of biomass production under O₂/CO₂ competition (moles O₂ per mole CO₂ consumed at RuBisCO) and irradiance-induced stress; EFMs on the tradeoff curve (or non-negative linear combinations thereof) represent optimal predicted growth phenotypes under the combined stresses. Photorespiration, as opposed to secretion of glycolate, was predicted as an essential process on the tradeoff curve except at zero O₂/CO₂ competition, and utilization of photorespiration reactions increased with increasing stress. Utilization of the C2 photorespiration cycle was predicted to increase along the tradeoff curve, whereas use of the glycerate pathway remained minimal. Photon absorption at PSII relative to PSI was also predicted to increase along the tradeoff curve. Neither cellular respiration nor the water-water cycle was active along the tradeoff curve, indicating that all photosynthetically derived electrons were directed to either biomass or reduced carbon byproducts. The tradeoff curve was divided into four phenotypic zones based on the suite of byproducts predicted. Zone 1 phenotypes did not secrete reduced byproducts, but as O₂/CO₂ competition at RuBisCO increased, more energy from photons was required to mediate the stress as indicated by higher photon absorption per biomass. At high O₂/CO₂ competition (~0.8 mol O₂ (mol CO₂)⁻¹), byproduct secretion represented the most resource-efficient acclimation strategy under the combined stresses. Byproduct synthesis effectively consumed photosynthetically derived electrons at the expense of fixed DIC and, conditionally, reduced nitrogen, as seen in the transition in byproducts produced along the tradeoff curve. Formate was predicted to be the most resource-efficient byproduct (zone 2 phenotypes), followed by combinations of formate and amino acids, represented in the model as alanine (zone 3 phenotypes), and acetate and amino acids (zone 4 phenotypes). Secretion of glycolate was not the most competitive use of metabolic potential under the considered stresses. Net O₂ production of the tradeoff curve EFMs quantified the fraction of electrons directed to biomass and reduced byproducts as a function of stress acclimation (Figure 3b). A nonlinear increase in net O₂ production per Cmol biomass was predicted; the increase in net O₂ production correlated with the secretion of reduced byproducts (formate, acetate, and/or alanine).

In addition to DIC, nitrogen and iron are essential anabolic resources and place constraints on cellular functions such as growth or ATP regeneration [70]. Acclimation to nitrogen- or iron-limited growth, assessed by investment into enzymes, was analyzed in conjunction with O₂/CO₂ competition (Figure A2a,b in Appendix B). Increasing O₂/CO₂ competition at RuBisCO necessitated an increase in nitrogen and iron investments into metabolic enzymes due to the requirement to process 2-phosphoglycolate. Tradeoff curve analysis of simultaneous acclimation to O₂/CO₂ competition and nutrient limitation showed trends similar to those predicted under irradiance-induced stress in Figure 3a, and amino acid secretion was again predicted at the highest resource limitation stress. However, under nitrogen limitation, reduced byproduct secretion was required for the most competitive phenotypes over the entire range of resource-limited growth. BP-1 metabolism was predicted to be less robust to nitrogen-limited stress than irradiance-induced stress as indicated by relatively fewer suboptimal EFMs near the tradeoff curve (Figure A2a in Appendix B). Additional details on nitrogen and iron limitation are found in Appendix D. While the majority of EFMs produced biomass, energy-producing EFMs (not producing biomass) also showed similar optimal byproducts under irradiance-induced stress and O₂/CO₂ competition (data not shown).

3.3. Comparison of Computational Predictions with Photobioreactor Physiological Data

The optimal predicted growth phenotypes identified along the tradeoff curve (Figure 3a) were compared with data from turbidostat culturing experiments. Irradiance levels altered both specific growth rate and biomass yield during cultivation. Specific growth rates ranged from 0.06–0.29 h⁻¹ at irradiances varying from 200–2000 μmol photons m⁻² s⁻¹ (Figure 4a). Specific growth rates increased linearly as a function of incident irradiance below 500 μmol photons m⁻² s⁻¹. Above 500 μmol photons m⁻² s⁻¹, irradiance became saturating, possibly inhibitory, and specific growth rate approached a maximum at 1800–2000 μmol photons m⁻² s⁻¹. Conversely, biomass yield per photon absorbed had a maximum at low irradiance (200–300 μmol photons m⁻² s⁻¹) and decreased nonlinearly as a function of incident irradiance (Figure 4b). Irradiance-induced stress at 2000 μmol photons m⁻² s⁻¹ reduced the biomass production efficiency by more than 50% compared to low irradiance conditions. The decrease in biomass per photon yield is consistent with predicted acclimation strategies, as is the nonlinear relationship between stress and biomass growth efficiency (Figure 3).

BP-1 maintenance energy requirements were estimated by analyzing specific photon absorption rate as a function of specific growth rate (Figure 5). The non-growth associated maintenance energy requirement (0.16 mol photons (g CDW)⁻¹ h⁻¹) was estimated by extrapolating the specific photon absorption rate data to a zero growth rate. Photon requirements for growth can be partitioned into the cellular energy required to (1) reduce nutrients such as DIC and nitrate into biomass monomers and (2) polymerize monomers into macromolecules. The photon requirement to reduce nutrient substrates, including bicarbonate and nitrate, to biomass monomers was calculated using the experimentally measured biomass composition. Macromolecular synthesis reactions in the model incorporated the energy cost of phosphate bonds required to polymerize monomers.

Photon requirement per Cmol biomass increased nonlinearly at higher growth rates (Figure 5), which corresponded to higher incident irradiance and represented successively increasing irradiance-induced stress and reduced biomass production efficiency. The difference between the photon requirement for biomass and the experimentally measured photon requirement is hypothesized to be the photon requirement for growth-associated maintenance energy, including tasks such as general protein repair, enzyme turnover, and maintenance of gradients, or other drains such as non-photochemical quenching of absorbed photons [8,71,72,73]. Additionally, the repair and recycling of PSII due to increased photoinactivation at high irradiance requires a large investment of nitrogen and poses a significant limitation on growth [74,75]. The implications of nitrogen source degree of reduction were also factored into maintenance energy calculations. A comparison of the effects of different nitrogen sources on the photon requirement is shown in Figure A3 in Appendix B. Molecular nitrogen and ammonia required fewer photons per biomass since they are more reduced than nitrate. Nitrate may be a preferred nitrogen source for photoautotrophs under high irradiance conditions, likely because it represents a possible sink for electrons which can buffer over-reduced photosynthetic machinery.

3.4. Comparison of Computational Predictions with Photobioreactor Transcriptomic Data

The optimal predicted growth phenotypes identified along the tradeoff curve (Figure 3a) were compared with previously published BP-1 transcriptomic data [33]. The transcriptomic data were analyzed for differentially expressed genes (two-fold or greater difference) between high and low irradiance conditions (2000 versus 200 μmol photons m⁻² s⁻¹). A change in expression of two-fold or greater was observed for 1147 genes. Differentially expressed genes were examined according to metabolic pathways and compared with the pathways utilized in the predicted optimal stress acclimation phenotypes. Consistencies and inconsistencies between predicted and observed metabolic functionalities were grouped into six categories (

Table 3. Comparison between computational predictions of stress acclimations and previously published photobioreactor gene expression data under high versus low irradiance conditions [33]. Metabolic functionalities were predicted from competitive pathways along the optimal tradeoff curve for irradiance-induced stress and O₂/CO₂ competition (Figure 3a), and observations were made from gene expression data comparing change in transcripts from high to low irradiance (2000 versus 200 μmol photons m⁻² s⁻¹) [33].

	Prediction	Observation
1. Photosystem contribution	• Increased PSII photon absorption relative to PSI	• Upregulation of PSII-associated genes • No change in PSI-associated genes
2. Photorespiration pathways	• Use of photorespiration • Increase in photorespiration with higher O2/CO2 competition	• Transcription of photorespiration genes • Upregulation of C2 cycle genes
	• Primarily C2 cycle, minimal glycerate pathway usage	• No change in glycerate pathway genes
3. Byproduct secretion	• Production of reduced byproducts• Formate and acetate production	• Upregulation of formate, acetate, and sucrose synthesis genes
	• Amino acid (alanine) secretion at highest stress	• Upregulation of amino acid synthesis pathway and transporter genes
4. Glycolysis	• Increased use of lower portion of glycolysis	• Upregulation of genes in lower portion of glycolysis
5. TCA cycle	• No change in TCA cycle use	• Upregulation of TCA cycle genes leading to synthesis of α-ketoglutarate
6. Nitrate and sulfate assimilation	• Increased nitrate uptake in pathways that secrete amino acid byproducts	• Upregulation of nitrate uptake and assimilation genes
	• No change in sulfate uptake	• Upregulation of sulfate uptake and assimilation genes

) and are discussed in detail below.

3.4.1. Photosynthesis, Photorespiration, and Byproducts

The predicted increase in photon absorption at PSII relative to PSI (Figure 3a) was reflected in the transcriptomic data [33] showing upregulation of genes coding for several PSII subunit and repair genes but no upregulation of PSI-associated genes (

Table A2. Upregulated BP-1 genes under high versus low irradiance conditions (2000 versus 200 μmol photons m⁻² s⁻¹) [33] involved in photosystem II (PSII), carbon-concentrating mechanism (CCM), Calvin cycle carbon fixation, glycolysis, TCA cycle, oxidative phosphorylation (OP), and nitrate and sulfate uptake and assimilation. For each pathway, upregulated genes are listed in the left column with corresponding fold change in the right column.

PSII		CCM		Calvin		Glycolysis
psbA3	42.0	ccmK1	2.5	gap2	5.3	eno	4.6
psbP	4.3	ccmK2	2.1	gapA	4.5	gap2	5.3
psbX	3.3	ccmK3	2.6	glpX	6.6	gapA	4.5
psbQ	2.7	ccmM	2.5	pgk	2.1	gmpA	6.4
psb29	2.6	tlr0311	2.4	prkB	13.4	gpmI	5.4
psb32	4.1			rpiA	16.2	pfkA	2.3
				tpiA	5.2	pgk	2.1
						pyk	3.5
						tpiA	5.2
TCA		OP		Nitrate		Sulfate
acnB	2.2	atpC	2.2	narM	2.3	cysA	2.5
fumC	3.2	atpD	5.2	nirA	3.4	cysC1	3.9
gltA	4.7	atpE	2.2	ntcB	4.9	cysH	4.6
idh3	6.3	atpF	2.9	nrtA	2.2	cysQ	3.1
sdhC	2.0	atpG	3.2	nrtD	2.5	met3	8.5
		atpH	2.8	tll1357	3.1
		cydB	3.6
		ndh	2.0
		ndhA	6.3
		ndhB	4.4
		ndhC	6.1
		ndhD3	13.2
		ndhE	4.7
		ndhF3	2.4
		ndhG	3.0
		ndhH	2.2
		ndhI	4.9
		ndhJ	6.6
		ndhK	3.1
		ndhL	4.9
		ndhM	13.1
		ndhN	9.8
		ppa	3.6
		sdhC	2.0

in Appendix C). The increase in transcript level could be due to increased photon absorption, or it could reflect an increased turnover of PSII, which has been reported during culturing at high irradiance [74,75]. A relative increase in photon absorption at PSII would suggest an increased relative contribution of PSII to photosynthetic electron flow under irradiance-induced stress. Photorespiration was a predicted strategy under high irradiance and O₂/CO₂ competition, corresponding to upregulation of photorespiration pathway genes observed in the transcriptomic data [33]. Predicted pathways indicated preferential utilization of the C2 photorespiration cycle as opposed to the glycerate pathway, and transcriptomic data [33] indicated upregulation of C2 cycle genes with no change in expression of glycerate pathway genes (Figure 6). Byproduct secretion was predicted as a competitive strategy at high irradiance and O₂/CO₂ competition. Irradiance, nitrogen investment, and iron investment analyses in conjunction with O₂/CO₂ competition all predicted amino acid secretion as a resource-efficient strategy at the highest combined stress conditions (Figure 3a and Figure A2). These predictions corresponded with observations of upregulated genes for synthesis pathways of organic compounds such as acetate and formate (Figure 6), as well as for more than 50 amino acid synthesis pathway and transporter genes (

Table A3. Genes involved in amino acid synthesis pathways found to be upregulated under high irradiance conditions (2000 versus 200 μmol photons m⁻² s⁻¹) [33]. Amino acids are abbreviated according to IUPAC 1-letter convention and number of nitrogen atoms follows in parentheses. For each amino acid, upregulated genes are listed in the left column with corresponding fold change in the right column.

R (4)		N/D (2/1)		C (1)		Q (2)		H (3)		I/L/V (1/1/1)		K (2)		M (1)		F/W/Y (1/2/1)		P (1)		S (1)		T (1)		Transporters
argB	3.0	asnB	2.5	cysE	4.0	glnA	3.6	hisA	3.4	avtA	2.8	dapA	2.9	metE	12.9	aroA	9.2	proB	6.7	glyA	4.5	thrA	2.6	tlr1046	2.2
argC	4.8	ansA2	3.0					hisB	3.9	cimA	2.5	dapB	4.5			aroB	4.3			serA	7.8	thrB1	3.7	tlr1067	3.4
argD	2.5	aspB	2.7					hisIE	6.7	ilvE	5.6	dapF	4.1			aroC	4.3					thrCII	6.3	tll1105	2.4
argG	14.0							hisG	2.3	leuB	6.0	dapL1	4.0			aroE	2.4							tlr1120	16.5
argH	4.4							hisF	2.0	leuC	2.9	lysA	3.3			trpA	2.6							tlr1121	3.6
argJ	4.0											lysC	2.1			trpB	2.5							tlr1151	2.3
																trpC	2.2							tll1318	2.7
																trpE	4.1							tlr1592	2.1
																trpF	2.3
																tyrAa	2.4

in Appendix C). Altogether, these parallels with the transcriptomic data [33] suggest increased electron flow into the system, increased photorespiration, and reprocessing of salvaged carbon into other byproducts with greater degree of reduction (Table 1) at higher irradiance.

3.4.2. Central Metabolism and Nutrient Assimilation

Several glycolysis genes were observed to be upregulated under high irradiance conditions [33], primarily genes involved in the lower portion of glycolysis after glyceraldehyde-3-phosphate (glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase, enolase, and pyruvate kinase) (Table A2 in Appendix C). Tradeoff curve analysis of the EFMs under high irradiance and O₂/CO₂ competition predicted that utilization of the reactions catalyzed by these enzymes increased with increasing stress except for glyceraldehyde-3-phosphate dehydrogenase. Both glyceraldehyde-3-phosphate and 3-phosphoglycerate intersect the Calvin cycle and glycolysis; thus, increased use of the lower portion of glycolysis suggested funneling of glyceraldehyde-3-phosphate from the Calvin cycle into glycolysis to produce pyruvate, which may be used to synthesize byproducts such as formate, acetate, and amino acids. Several TCA cycle genes were also observed to be upregulated under high irradiance conditions, predominantly genes catalyzing reactions up to the synthesis of α-ketoglutarate, from which several amino acids are synthesized (Table A2 in Appendix C). Tradeoff curve analysis of the EFMs under high irradiance and O₂/CO₂ competition predicted no change in utilization of any TCA cycle reactions. The BP-1 model utilized alanine as a representative amino acid which could be secreted as a byproduct; alanine is synthesized via pyruvate. However, if the computational model was modified to allow secretion of amino acids that are synthesized via TCA cycle intermediates, such as glutamate, it would lead to predictions of increases in some TCA cycle fluxes.

Finally, the transcriptomic data [33] showed upregulation of genes involved in both nitrate and sulfate uptake and assimilation under high irradiance conditions (Table A2 in Appendix C). Tradeoff curve analysis of the EFMs under high irradiance and O₂/CO₂ competition predicted increased use of the nitrate uptake reaction for strategies that secreted amino acids; conversely, no change in use of the sulfate uptake reaction was predicted. Reduction of nitrate to ammonia for amino acid synthesis represents an effective strategy for using excess electrons from the photosynthetic electron transport chain, consuming 8 moles of electrons per mole of nitrate reduced. Thus, at high irradiance and O₂ production, secretion of amino acids represents an economical stress acclimation strategy. Similarly, sulfate reduction also consumes 8 moles of electrons per mole of sulfate reduced to hydrogen sulfide, which is used in synthesis of cysteine and methionine. Permitting secretion of cysteine or methionine in the computational model would lead to predictions of increased sulfate uptake. Altogether, comparison of the predicted competitive strategies with transcriptomic data under high irradiance (Table 3) suggests overall consistency of the computational model with photobioreactor observations.

3.5. Stress Acclimation and Photoautotrophic-Heterotrophic Interactions

BP-1 acclimation to a variety of culturing stresses was predicted to result in secretion of reduced carbon byproducts including organic acids and amino acids (Figure 3a and Figure A2a,b; illustrated in Figure 7a). These byproducts represent a nutritional niche for heterotrophs. Photoautotrophic-heterotrophic cross-feeding could represent a mutually beneficial mechanism for buffering a photoautotroph from environmental stresses. Consumption of reduced carbon byproducts by the heterotroph would relieve potential inhibitory organic acid stress, as well as maximize the efficiency of total resource usage by the community (illustrated in Figure 7b). Cross-feeding of byproducts could also promote growth of the photoautotroph through consumption of O₂ by an aerobic heterotroph, thus decreasing local O₂ concentrations and lowering O₂/CO₂ competition. The amount of heterotroph able to be supported by secreted byproducts was predicted as a function of stress using published heterotrophic biomass per byproduct yields [76,77,78,79,80,81,82] (Figure 7c, see File S5 in the Supplementary Materials for calculations). The predicted amount of heterotrophic biomass that can be supported by BP-1 through cross-feeding of byproducts increased as stress increased due to higher byproduct yields at higher stress levels, as well as the varying heterotrophic biomass yields on different byproducts (

Table A4. Byproduct yields with respect to BP-1 biomass for pathways that secreted byproducts along the tradeoff curve for irradiance-induced stress and O₂/CO₂ competition and the corresponding yield of heterotroph biomass. Heterotroph yield was obtained via substrate consumption costs estimated from the literature for the respective byproducts (Cmol per Cmol heterotroph biomass): 4.53 for formate [77], 2.66 for alanine [78], and 2.61 for acetate [76]. Photons absorbed per BP-1 are reported in mol Cmol⁻¹, O₂/CO₂ competition is reported in mol mol⁻¹, and yields are reported in Cmol Cmol⁻¹.

Photons Absorbed Per BP-1 Biomass	O2/CO2 Competition	Formate Per BP-1 Biomass	Alanine Per BP-1 Biomass	Acetate Per BP-1 Biomass	Heterotroph Biomass Per BP-1 Biomass
33.26	0.86	0.13	NA	NA	0.03
51.36	1.01	NA	0.30	NA	0.11
52.44	1.02	NA	0.31	0.01	0.12
100.05	1.10	NA	0.84	0.36	0.45
105.55	1.11	NA	0.90	0.40	0.49

in Appendix C). The cross-feeding was also predicted to reduce local O₂ levels, which was calculated based on heterotrophic biomass O₂ requirements (Figure 7d, File S5 in the Supplementary Materials).

The predicted ratio of heterotroph to photoautotroph as a function of stress acclimation was compared to published photobioreactor co-culture data of BP-1 with the aerobic heterotroph Meiothermus ruber strain A [33]. Experiments reported heterotroph to photoautotroph ratios of ~1:10 [33]. This ratio, with some variation accounting for cell size differences between the two populations, falls within the range of heterotroph to photoautotroph ratios predicted at modest culturing stress (Figure 7c). These predictions considered autotrophic-heterotrophic interactions based on secreted carbon and not necessarily nitrogen source. Additional analysis of potential cross-feeding based on nitrogen or iron limitation can be found in Appendix B (Figure A2c–f).

4. Discussion

Computational modeling was integrated with photobioreactor analyses to identify and interpret, from a systems perspective, the inferred mechanisms that underpin cyanobacterial acclimation to irradiance-associated stress. The combined results of this study show how different cyanobacterial systems, such as the photosynthetic apparatus and central carbon metabolism, can respond to environmentally induced stresses. Photobioreactor steady state growth of BP-1 showed decreased biomass production efficiency at high irradiance (Figure 4b), indicating that electrons were partitioned into non-biomass-producing alternative metabolic routes. Examination of transcriptomic data [33] comparing high to low irradiance conditions identified upregulation of genes involved in PSII operation, photorespiration, organic acid synthesis, and amino acid synthesis, among other pathways (Figure 6, Table A2 and Table A3). Interrogation of BP-1 metabolic pathways with EFMA and resource allocation theory under conditions of high irradiance, high O₂, and limited nutrient availability provided a theoretical explanation for utilization of these pathways. Evolution has selected phenotypes which allocate limiting resources competitively. The origin of the stresses is the imbalance in resource acquisition which is manifested as a resource limitation. Acclimation to the resource stresses resulted in the secretion of reduced byproducts in a behavior analogous to classic overflow metabolism in heterotrophs. The byproduct-secreting phenotypes represent a competitive and economical response to the stress [1]. It is worth noting that photobioreactor observations and computational predictions for BP-1 are in general agreement with the transcriptional patterns and physiological trends observed in the closely related Synechococcus sp. PCC 7002 [83]. The predicted byproduct secretion profiles furthermore control nutrient niches for proximal heterotrophic partners (Figure 7). In some cases, heterotrophic consumption of the byproducts represents a mutually beneficial interaction in that byproduct removal prevents accumulation of byproducts to a degree that represents an additional stress. This mutually beneficial interaction template likely plays a significant role in the many reported occurrences of photoautotrophic-heterotrophic consortia [84,85,86]. In fact, cross-feeding between BP-1 and the aerobic heterotroph M. ruber strain A has been both predicted by genome-scale modeling and observed in a laboratory setting [14,33].

The computational analyses investigated several metabolic acclimations to photosynthesis-associated stresses that apply broadly to photoautotrophs, including photosystem utilization and photorespiration strategies, the nature of reduced carbon byproducts, and the severity of O₂/CO₂ competition at RuBisCO. PSII was predicted to increase in photon absorption relative to PSI as irradiance increased (Figure 3), supported by transcriptomic data [33] (Table 3 and Table A2). Increased relative photon absorption of PSII under higher irradiance is also reported in the literature from studies with the mesophilic cyanobacterium Synechocystis sp. PCC 6803 and is hypothesized to aid in reducing overall electron transport [68]. Additionally, increased utilization of the C2 photorespiration cycle at high irradiances may intersect with byproduct secretion strategies and contribute to amino acid synthesis as a resource-efficient strategy at high irradiances. Photorespiration permits salvage of carbon from unusable RuBisCO oxygenation byproducts; this carbon may be directed toward other byproduct pathways. The C2 photorespiration cycle requires more enzymatic steps and thus more biosynthetic resources (e.g., nitrogen) than the glycerate pathway, but links into glycine-serine interconversion and amino acid synthesis pathways.

Formate is the least reduced organic byproduct considered in the model (Table 1). It is predicted to be a more competitive byproduct secretion strategy at intermediate irradiance-induced stress and O₂/CO₂ competition, releasing a minimal quantity of electrons in the form of reduced carbon byproducts and retaining the remaining electrons for biomass (Figure 3). Alanine is predicted to be a competitive byproduct at high stress levels due to its high degree of reduction (Table 1). At high electron load (supported by high rates of oxygenic photosynthesis) and high O₂/CO₂ competition, alanine synthesis consumes more electrons per Cmol, resulting in a more efficient redox sink (Figure 3). Alanine was selected in this study as a representative amino acid; however, amino acids with higher nitrogen content, such as arginine, histidine, or lysine, would serve as even more effective electron sinks when nitrate is the nitrogen source. Genes involved in synthesis pathways for several amino acids beside alanine (Table A3 in Appendix C) were identified as upregulated under high irradiance conditions in the transcriptomic data [33]. Additionally, qualitative measurements from BP-1 steady state cultures have previously identified a variety of amino acids in the extracellular environment, including glutamate, isoleucine, leucine, lysine, phenylalanine, serine, threonine, and valine [33].

Experimental assessment of O₂/CO₂ competition and actual concentrations of O₂ and CO₂ at the active site of RuBisCO in vivo is challenging. The specificity factor, a kinetic constant describing the relative affinity of RuBisCO for CO₂ versus O₂ ($v_c/v_o=\mathrm{SF}\left[{\mathrm{CO}}_2\right]/\left[{\mathrm{O}}_2\right]$) [87], has been measured for a variety of phototrophs and is typically obtained from enzyme extracts. Falkowski and Raven [88] compiled a list of specificity factors from a variety of organisms, including cyanobacteria, algae, and plants, and estimated $v_o/v_c$ ratios under assumptions of air equilibrium at 25 °C. These experimental estimates were compared with the predicted $v_o/v_c$ values from the BP-1 model irradiance tradeoff curve (Figure 8a(A)). The variation in values within and among different types of organisms highlights the diversity of RuBisCO enzyme properties, which organisms are thought to have optimized over time based on different selective pressures [89,90]. However, the experimental estimates do not account for the optimal temperature environment of the organism, the confounding influence of photosynthetic O₂ evolution, or effects of the carbon-concentrating mechanism, which may also be influenced by pH [91].

The specificity factor can be used to convert predicted O₂/CO₂ competition values to local relative O₂/CO₂ concentrations around RuBisCO [87], thereby permitting extension of stoichiometric modeling into the kinetic realm. Equivalent relative O₂/CO₂ competition values convert to different relative concentrations depending upon the magnitude of the specificity factor. Values for mesophilic cyanobacteria range from 45 to 70, and higher plants have an average value around 100 [87]. Figure 8a(B–D) shows the effect of varying the specificity factor on the O₂/CO₂ concentrations necessary to achieve the predicted $v_o/v_c$ values along the irradiance tradeoff curve in Figure 3a; relative concentrations are lowered with a smaller specificity factor and raised with a higher specificity factor. A higher specificity factor indicates a greater tolerance to stress from O₂/CO₂ competition. In vivo measurements of oxygenation and carboxylation rates are sparse in the literature, particularly for microbial species; Taffs et al. [20] calculated a range of 3–7% oxygenation based on measurements of extracellularly secreted glycolate [92], but these values are likely an underestimate considering glycolate may be salvaged through the complete photorespiration pathway rather than excreted (Figure 3). Isotopic labeling studies of cyanobacteria also provide experimental data, but extrapolation of $v_c/v_o$ ratios should be exercised with caution. Studies have shown operation of photorespiration even under high CO₂ (5%) conditions [93]. Another study has presented both modeling and experimental validation of the necessity of photorespiration even under saturating CO₂ conditions, positing that high CO₂ stimulates high photosynthetic rates to provide adequate energy for carbon fixation, which thereby leads to increased O₂ production levels [94]. Additionally, elevated temperatures have been shown to enhance oxygenation due to both changes in the specificity of RuBisCO and the different solubilities of O₂ and CO₂ [95] (Figure 8b). Experimental data on $v_o/v_c$. values is variable and dependent on the conditions under which the measurements were made. However, an environmental scenario with low O₂/CO₂ ratios may indicate that greater priority is placed on minimizing O₂/CO₂ competition than on minimizing photon absorption cost particularly under high irradiance conditions, e.g., O₂/CO₂ competition is a stronger driver of stress acclimation. Byproduct production and existence of heterotrophic partners is observed in experimental cyanobacterial systems, suggesting that byproduct production is an effective strategy for managing electrons from excess photon absorption. Instead, the cell may be simultaneously optimizing for other stresses such as biosynthetic nutrient investment like nitrogen or iron (Figure A2).

The systems-level analysis provided by this study indicated that the suite of metabolic carbon and electron sinks (i.e., secreted byproducts and biomass) is dependent upon environmental stressors. Pathway utilization and resource investments were co-dependent upon irradiance, O₂/CO₂ competition at RuBisCO, and DIC, nitrogen, and iron levels. These results provided novel insight into ecologically competitive metabolic strategies that cyanobacteria use to acclimate to environmental conditions. Physiological and transcriptomic [33] data paralleled the predictions, providing an additional level of support to the stoichiometric modeling predictions. It is noted that the stoichiometric model does not account for kinetic constraints, regulatory effects, or other aspects of thermodynamics beside reaction reversibilities [97,98], which may account for some of the differences between predictions and data and represents an avenue for further development. Finally, analysis of predicted optimal growth phenotypes was extended to make inferences about the nature of photoautotrophic-heterotrophic interactions and provide a theoretical basis for examining community composition. Taken holistically, this work presents a synergistic experimental and theoretical approach for understanding metabolic acclimation and provides a new level of insight into how different cyanobacterial systems, such as the photosynthetic apparatus and central carbon metabolism, coordinate and respond to environmental stresses that influence resource allocation.