However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function.  13C MFA practitioners know that modeling results can be sensitive to minor modifications of the metabolic model. Certain parts of the metabolic model that are not well mapped to a molecular mechanism (e.g. drains to biomass or ATP maintenance) can have an inordinate impact on the final fluxes. The only way to ascertain the validity of the model is by checking that the result does not significantly differ from previously observed flux profiles. However, that approach diminishes the possibility of discovering truly novel flux profiles. Because of this strong dependence on metabolic model details, it would be very useful to have a systematic and repeatable way to produce these metabolic models. And indeed there is one: genome-scale metabolic models can be systematically obtained from genomic sequences, and represent all the known genomically encoded metabolic information. However, these models are much larger than the traditionally used central carbon metabolism models. Hence, the number of degrees of freedom of the model (fluxes) significantly exceeds the number of measurements (metabolite labeling profiles and exchange fluxes). As a result, one expects many flux profiles compatible with the experimental data. The best way to represent these is by identifying all fluxes compatible with the experimental data. Our novel method BayFlux, based on Bayesian inference and Markov Chain Monte Carlo sampling, provides this capability. Interestingly, this approach leads to the observation that some traditional optimization approaches can significantly overestimate flux uncertainty, and that genome-scale models of metabolism produce narrower flux distributions than the small core metabolic models that are traditionally used in 13C MFA. Furthermore, we show that the extra information provided by this approach allows us to improve knockout predictions, compared to traditional methods. Although the method scales well with more reactions, improvements will be needed to tackle the large metabolic models found in microbiomes and human metabolism. Flux analysis, for example, has been used to improve biofuel production [Metabolic fluxes  works by using measurements of the exchange fluxes (fluxes coming in and out of the cell) to fully constrain a small core metabolic network (with less degrees of freedom than measurements). Flux Balance Analysis  best describes the data for a small metabolic model. Genome-scale 13C MFA uses the same data types but finds the flux profile for a (much larger) genome-scale metabolic model, often containing thousands of reactions [e.g. human cells or engineered bacterial strains). 13C MFA is considered the most accurate approach to measure metabolic fluxes but relies on expensive and time consuming 13C experiments which are nontrivial to do in a high-throughput fashion. Both methods have been used successfully to guide bioengineering processes [Although there are several popular methods for studying metabolic fluxes –1313C MFsis  uses a eactions , 15. FBAeactions  but is lrocesses , 18.13C MFA to determine fluxes [13C labeling for a single metabolite can completely derail flux calculation. This optimization approach also often depends on expensive commercial solvers, and is hard to parallelize. Finally and most importantly, uncertainty quantification relies on confidence intervals estimated with help of frequentist statistics. Such intervals depend on a given experimental outcome and may result in misinterpretation of the flux uncertainty, as has been noted by others before us [e.g. non-gaussian fitness distribution), such that a single point does not meaningfully represent the experimental data.The optimization approach used to date in e fluxes –22 showsefore us . For exaboth Bayesian inference and Markov Chain Monte Carlo (MCMC) methods to sample flux space, as informed by 13C labeling and flux exchange data. Just Monte Carlo sampling by itself provides all fluxes compatible with experimental data instead of just the fluxes that best fit the available data [e.g. fluxes coming in and out of the cell are not mass balanced). Bayesian inference can address this complication because it is based on a probabilistic interpretation that ensures a systematic approach to manage data inconsistencies and update flux probability distributions as more data becomes available [13C MFA. Furthermore, MCMC Bayesian inference is easier to parallelize, use with large datasets, and integrate with heterogenous data sources  that rigorously identifies the full distribution of flux profiles compatible with experimental data for a genome-scale model . The resble data –27. Howeble data , it failvailable –32. Combvailable . Unlike vailable , 30, 33,vailable , and helvailable , 15, andics data ).13C data, and demonstrating more informative flux profiles than can be achieved with non-Bayesian, optimization-based approaches. Using an E. coli model and data set [13C MFA. Based on BayFlux, we develop and evaluate novel methods (P-13C MOMA and ROOM) to predict the biological results of a gene knockout, that improve on FBA-based MOMA and ROOM methods by quantifying prediction uncertainty. Finally, we find that BayFlux scales well as more reactions are added, but efficiency improvements will be needed to sample the very large metabolic models required for microbiomes or human metabolism. We also discuss ideas for future improvement.We showcase BayFlux by performing the first flux sampling for a genome-scale model as constrained by data set , we show13C MFA. Bayesian and frequentist statistics represent two different paradigms to deal with probability and inference.BayFlux uses Bayesian rather than frequentist statistics to determine fluxes through 13C MFA, assume the existence of a true vector of fluxes v, use Maximum Likelihood Estimators (MLE) to find such a vector, and rely on confidence intervals to reflect uncertainties in flux estimates. The MLE determines v as the most likely to produce the measured experimental data: i.e., it is a point estimator that generates a single result even when many fluxes produce the same experimental data. The uncertainty in the result is reflected through a confidence interval, which can be computed in different ways that do not necessarily lead to the same outcome [Frequentist statistics, by far the most often used approach in  outcome . This app(v|y) representing the probability that a certain value of v is realized, given a certain prior knowledge and experimental data y available to the observer. Providing full probability distributions allows for a more accurate description in the case where many fluxes can equally best represent the experimental data. Bayesian inference calculates the posterior p(v|y) through the Bayes formula [p(v|y) is the probability of obtaining experimental data y given fluxes v, p(y) is the probability of observing data y, and p(v) is the probability of observing v (representing prior knowledge). Monte Carlo sampling is used to calculate the posterior p(v|y) by “jumping” from point to point in the phase spaces of fluxes v with a probability given by the numerator p(y|v)p(v)  [et al [Bayesian inference assumes a hypotheses-driven point of view, where the goal is to estimate the posterior  formula :p(v|y)=r all v) . This pr) [et al  to calcu13C experimental data. We achieve this by sampling in two phases: an initial ACHR phase which finds the center of the flux polytope, and a second Bayesian inference phase in which the center is locked, and samples are accepted or rejected based on how well they fit the available 13C experimental data. Briefly, for each flux point proposed in the Bayesian inference phase, the corresponding mass isotopomer distributions (MIDs) can be calculated using Elementary Metabolite Units . The fl13C MFA tool: 13CFLUX2 [www.coin-or.org/ipopt) and NAG C (www.nag.co.uk) mathematical optimization libraries. For this comparison, we use the E. coli data and core metabolic model employed in the demonstration of 13CFLUX2: measurements of glucose uptake, growth rate, and the labeling of eleven central carbon intracellular metabolites for an E. coli MG1655 strain grown in glucose-limited continuous culture, and a model comprised of 66 reactions and 37 metabolites describing central carbon metabolism (see Core Metabolic Model 1 below). Flux profiles for the best fit and best sample (i.e. highest likelihood) from both methods are very similar . This finding is surprising because one would expect that the extra degrees of freedom provided by the several thousand reactions in the genome-scale model (as compared to the ∼60 in the core model) would allow for many more distinct flux profiles to meet stoichiometric constraints and labeling data. However, it seems that central metabolic fluxes are well constrained by metabolite labeling, and the addition of several hundreds of extra reactions only add draws of cofactors that further constrain core fluxes. This is consistent with the known bow tie structure of metabolism [e.g. broader probability distrbution peaks) for the genome-scale model than the core model (Flux sampling (via BayFlux) for the genome-scale model of metabolism generally produces narrower distributions of fluxes compatible with the experimental data than the small core metabolic models that are traditionally used in 13C MFA . This mea et al.  . The po13C MFA since the results may depend significantly on the model used. Whereas all mathematical models require validation, sensitivity analysis, and statistical testing in order to be applied effectively, the traditional use of small scale 13C MFA is particularly fraught with modeler’s choices that can have an inordinate impact on the final fluxes, . Genomere model , 43, 44,In any case, we can see that the idea of narrowly constraining all fluxes is, often, misleading. For example, for the cases of F6PA, DHAPT and PYK, the genome-scale model shows very flat probability distributions over large ranges of flux values .13C Minimum of Metabolic Adjustment (P-13C MOMA), and Probabilistic 13C Regulatory On/Off Minimization (P-13C ROOM). Unlike traditional (FBA-based) MOMA [13C versions [13C MOMA and P-13C ROOM, on the other hand, work by computing the base flux profile distribution using BayFlux, then choosing a representative set of flux profiles through subsampling, and finally computing the MOMA [By leveraging the full probability distribution provided by BayFlux, we develop and evaluate two novel methods to predict fluxes after a knockout Figs : Probabied) MOMA  and ROOMed) MOMA  (or theiversions ), this athe MOMA  and ROOMthe MOMA  knockout13C MOMA and P-13C ROOM and those of traditional MOMA and ROOM, with flux profiles measured through 13C MFA (which we will take as ground truth) using data previously published in Toya et al. [pyk and pgi). Flux profile distributions for P-13C MOMA and P-13C ROOM are obtained by using the Toya et al. [13C labeling data to calculate a base flux profile distribution through BayFlux and use it . We then apply MOMA and ROOM to the resulting flux profiles to predict the knockout flux profiles for pyk5h and pgi16h knockouts. We compare this four predicted genome-scale flux profiles and distributions  to the flux profile for the corresponding knockout calculated in two different ways: the original result through 13C MFA using a core metabolic model by Toya et al., and through BayFlux. The comparison to the original flux profile from Toya et al. is meant to provide a comparison to a ground truth that is not influenced by BayFlux, and the comparison to a BayFlux profile is meant to provide a comparison to a more comprehensive genome-scale flux profile.We compare the predictions of P-a et al.  for wilda et al.  WT 13C lFig D in  to yieldet al. using a core model shows how P-13C MOMA and P-13C ROOM provide uncertainty quantification for MOMA and ROOM predictions, and yield some solutions that improve on MOMA and ROOM results (pyk and pgi respectively) included in the core metabolic model, and that converged to a stable final result across 4 repeated BayFlux runs  of the flux profiles predicted by P-13C MOMA and P-13C ROOM prediction are closer to the ground truth fluxes than traditional MOMA and ROOM predictions . This adictions .13C MOMA outperforms MOMA and P-13C ROOM outperforms ROOM. The best predictions for all methods are provided by P-13C MOMA, in terms of minimal distance from experimentally measured fluxes . This last, genome-scale, model requires 5.86 days to reach convergence after ≈ 33M samples using two Intel Xeon Gold 6154 (3 to 3.7 Ghz) cpu cores per sampler, for a sampler performance of approximately 65.4 samples per second. In contrast, the 13CFLUX2 optimization approach takes 2–5 minutes for the E. coli core metabolism model using two Intel(R) Xeon(R) CPUs E7–8870 v3 (2.10GHz), whereas BayFlux takes 8–9 hours using two Intel Xeon Gold 6154 (3 to 3.7 Ghz) cpu cores.The number of required samples to reach convergence using BayFlux seems to scale linearly with the number of reactions in the model . Converg r^<1.10  :Novel, faster, sampling algorithms will be required to apply BayFlux to the large metabolic models involved in microbial communities and human metabolism. A quick back-of-the-envelope calculation shows that a community of ≈ 200 species  would require over 19 years to achieve convergence based on the linear slope  and intercept  shown in et al [Similarly the recent human metabolic model by Thiele et al   is likely to produce many flux profiles compatible with the experimental data. For this reason, we believe that a probabilistic approach such as Bayflux would yield flux distributions that are flat and extended rather than peaked and concentrated, for which a single flux profile would be a poor representation of all the possible flux profiles.x speedup by using the popular sparse matrix solver algorithm SuperLU in place of the Numpy dense matrix solver from the Intel Distribution for Python that we are currently using, based on the mean of seven repeated matrix solving steps in our E. coli genome scale model [Preliminary tests show that we can get approximately an order of magnitude speedup just by using sparse matrix solvers. We find that we can get a ≈ 8.10le model . We inte13C experimental data for a genome-scale metabolic model (13C MFA (We have presented here a method (BayFlux) that provides all fluxes compatible with ic model . BayFluxic model , and pro, it wil13C MOMA and ROOM) to predict the biological results of a gene knockout, that improve on traditional FBA-based MOMA and ROOM methods  reactions, where at least one product or reactant is extracellular. Exchange reactions can be observed directly, by measuring the rate of change in extracellular concentration for a given chemical species: e.g. the exchange flux for glucose will match the rate at which the glucose concentration falls in the culture media. Internal or intracellular fluxes can only be inferred indirectly from other types of experimental data, as the chemical species they consume and produce are immediately also consumed and produced by other reactions. For example, a reaction ‘A’ may have a very high flux, but its chemical product can still be almost absent from a cell if reaction ‘B’ consumes its product at the same or a higher rate.The overarching goal of this work is to leverage diverse and noisy experimental data to infer the metabolic fluxes in a cell: e.g. using stochastic chemical kinetics [e.g. constant cellular doubling time) the random effects of individual chemical reaction events are cancelled out, allowing us to apply a steady state approximation. This assumes that the quantity of each chemical species has reached a quasi-stable equilibrium, and therefore one can regard each reaction as having a specific flux, where the total flux of reactions generating a chemical species always equals the sum of fluxes consuming it. In addition, applying the simplifying approximation that each cellular compartment or organelle is ‘well mixed,’ allows a single flux value to represent each reaction in each compartment.Metabolic flux is a coarse-grained concept, comprising a large number of heterogeneous chemical reactions in a living cell. For very simple systems, it is possible to treat individual models, atoms, and enzymes as distinct events, kinetics . Howevere.g. glucose with extra neutrons on specific atoms. Since different metabolic flux vectors result in shuffling of atoms in different manners, they produce distinct MIDs. Using the Elementary Metabolic Unit (EMU) method [An important challenge involves integrating together diverse data sources with fundamentally different types of error, and underlying relationships to metabolic flux. Most common among these data types are exchange fluxes and mass isotopomer distributions (MIDs). Extracellular exchange fluxes account for mass entering and exiting the cell, and include measurements such as nutrient consumption and metabolic byproduct production rate, as estimated by a time series of extracellular concentration measurements and biomass or growth rate measurements. Exchange fluxes are represented directly as metabolic fluxes for specific reactions, and can be either applied as stoichiometric constraints to specific reactions, or as probablistic constraints on those fluxes. Mass isotopomer distributions (MIDs) are the result of feeding the organism with defined isotopomer nutrient substrates: ) method , we can 13C MFA tackles this issue by considering only the reactions in central carbon metabolism and ignoring the rest. This approach conveniently, and artificially, reduces the number of degrees of freedom of the system below the number of measurements. This simplification can be justified by the “bow tie” structure of metabolism [Perhaps the most important challenge is that, given the incredible complexity of metabolism and the paucity of (noisy) experimental data, the metabolic flux system is severely underdetermined, with substantial uncertainty about the true metabolic flux profile. As shown in the examples below, the number of fluxes for a genome-scale model is typically on the order of ∼ 3000, whereas the available data to constrain is usually on the order of 1–5 exchange fluxes and ∼ 60 metabolite measurements. Traditional tabolism   (i.e. a priori probability that the flux vector is v) on the polytope Sv = 0: i.e. For the purpose of finding the joint distribution, we use a Bayesian inference approach . The likp(v|y) up to a normalizing constant, i.e. p(v|y) ∝ p(y|v)p(v), for which we use Markov Chain Monte Carlo (MCMC). MCMC allows us to draw samples from p(v|y) by creating a Markov chain that converges to the target distribution p(v|y) [S  . Our pro(v|y) [S . A generbability , 55:α=mThe normalizing constant cancels out, and so does the prior, since it is uniform. In the current implementation we use the AcMet algorithm, described below to compute the proposal.13C isotopomer mass distribution vectors for metabolites, as well as extracellular exchange fluxes computed from time series measurements of extracellular metabolite concentrations. Other experimental data can be added in a similar fashion, facilitating integration of diverse omics data into a single coherent model.We created a new algorithm , as identified by linear optimization. Here, we use the term ‘edge points’ instead of the more commonly used term ‘warm-up points’ to avoid confusion with the Markov Chain Monte Carlo (MCMC) concept of warm-up samples. These ‘edge points’ are coordinates in the feasible flux space, and should not be confused with the concept of edges in a mathematical graph.AcMet overcomes the center updates problem by sampling in two phases. First we collect a large number of uniform samples from the genome scale model using the ACHR algorithm, until the center converges into a stable position. Next, we lock the center while collecting samples with the Metropolis algorithm, which accepts or rejects samples taking into account the likelihood function derived from experimental data. By keeping the center locked, the chain becomes reversible, permitting Metropolis sampling. The ACHR algorithm begins with finding ‘edge points’ /‖wj − c‖4: Draw a sample S = 0, x ∈ {min, max}5: Select a step size v′ = vk + λk,xdk6: Generate a candidate α = L(v′)/L(vk) where L is the likelihood p(y|v).7: Calculate acceptance ratio 8: Draw u ≤ α or if k < q (warmup phase) accept the candidate by setting vk+1 = vk + λk,xdk9: If u > α and k ≥ q reject the candidate by setting vk+1 = vk10: If k = k + 111: Set k < q (warmup phase) set c = (kc + vk)/(k + 1) to update the center running average as per ACHR [12: If per ACHR 13: Go to Step 4L is computed for each step according to the normally distributed likelihood function . The exet al. wild type 5 hour (wt5h) data, which includes measurements of glucose uptake, acetate excretion, growth rate, and the labeling of nine central carbon intracellular metabolites for an E. coli BW25113 strain [E. coli core model with 63 reactions taken from previous literature [E. coli genome scale model described in this paper which combines iAF1260 and imEco726 models. Note that both models have a built in biomass composition, which we leave intact as originally described in each model, and are not directly comparable because they merge and separate different metabolites.To compare genome scale and core metabolic models, we use the previously published Toya 3 strain . As the terature , and as 13C MFA model with a genome scale stoichiometric model, and use this to evaluate previously published in vivo13C MFA data.To evaluate the method described by this paper using real world data, with a genome scale model, we merge a genome scale Escherichia coli [et al., and accommodate recent updates to the latest version of iAF1260, including new reactions and changes to some reaction and metabolite identifiers [We utilize atom transitions from the imEco726 genome scale isotope mappings, together with the latest version of the iAF1260 metabolic reconstruction of hia coli , 40. We ntifiers .This process was facilitated by the fact that imEco726 was initially based on iAF1260. Merging these models and adjusting them to be suitable for a specific experimental context required numerous refining steps, as described below. The full source code for this model creation and curation process is included with the BayFlux software package as a Python Jupyter notebook, such that it can serve as a template for users to repeat the general process for other organisms.The iAF1260 genome scale model contains 2382 reactions, and the imEco726 isotope mapping model contains carbon transitions for only 686 reactions. We find that 70 of the transitions in imEco726, and all of the metabolites participating in transitions  can not be mapped to any identical identifiers in the latest version of iAF1260. To address this issue, we performed a text based alignment using the Levenshtein distance between the unmapped identifiers in both models, which we then manually review for correctness . By insp13C labeling.For some reactions with the same or similar names we found that the products and reactants are swapped between the two models. For these reactions, we reorient the transitions from imEco726 to match those of iAF1260. Also, for this analysis, we are assuming steady state labeling, so we omit the “dil” transitions from imEco726, which account for metabolites which have not yet reached steady state e.g. succinate) exhibit structural symmetry, such that there is no single unique way of numbering the atoms. For reactions which act on symmetric metabolites, we duplicate the reactions atom transition(s) such that equal flux flows through the model with all possible orientations of the symmetric metabolite. Our software allows for an unlimited number of atom transitions for each reaction, over which the total flux for the reaction is equally divided.Some metabolites in the genome scale model  as implemented in the COBRApy function  to True . This re2O and Na+) were provided in the media that are not identified as essential in the analysis described above, and we enable the uptake of these.Next, to manually refine these for correctness, we obtain the actual experimentally used media composition as published by Toya et al., and identify the set of exchange reactions that correspond to the molecules in this media formulation. From this, we find that two additional nutrients  to identify non-essential reactions in the iAF1260 model which had also been omitted from imEco726. We find 1655 non-essential intracellular reactions, of which 1310 can both carry carbon and do not have provided transitions in imEco726, so we remove them from the model, along with 499 (now unused) metabolites.Next, we remove all reactions in iAF1260 which cannot carry flux under the experimentally derived extracellular exchange flux conditions, even if they can carry carbon, or have transitions from imEco726. This removes an additional 380 reactions and 371 metabolites from the model.After removing unused reactions from the iAF1260 model as described above, we identify 35 reactions which are both essential under our extracellular exchange flux conditions, do carry carbon, but do not contain transitions in imEco726. For these reactions, it was necessary to define atom transitions in order to obtain a complete genome scale isotope mapping model.As a first approximation for computing atom transitions without using chemical structures, we identify the number of carbons in each metabolite based on the chemical formula, and sort the reactants and products by increasing carbon count. For reactions where no two reactants have the same carbon count, and both the reactants and products have an identical set of carbon counts per molecule, we assume direct 1:1 transitions between the identically sized reactants and products.After applying the assumptions described above to estimate transitions for some reactions, we still have 8 reactions flagged as ambiguous, with either equal sized reactants, or a different distribution of sizes between reactants and products suggesting carbon exchange. For these reactions, we manually look up the corresponding reaction on MetaCyc, and manually write transitions that match the atom ordering used in imEco726.After all of the steps described above including removing reactions, removing metabolites, and adding transitions our final genome scale isotope mapping model contains 692 reactions, and 798 metabolites. Of these 692 reactions, 633 carry carbon and have one or more atom transition mapping.Please refer to the Jupyter Notebook entitled “imEco726_genome_scale” distributed with our BayFlux software, which outlines the full model curation process described above. All steps can be automatically reproduced, and used as a template for recreating this process with other species and/or experimental conditions.During the process of Bayesian inference from a metabolic model, our software simulates the mass isotopomer distributions (MIDs) for each flux sample and plugs them into the likelihood equation . Becausee.g. from parallel reactions with identical transitions) are automatically identified and merged, also without loss of information.We have developed a high performance implementation of the Elementary Metabolic Unit (EMU) method capable of simulating genome scale mass isotopomer distributions for millions of flux vectors in just a few hours on standard computer hardware . Three me.g. to cross validate a model with experimental data by leaving it out and re-inferring it. Additionally, it opens the possibility of generating simulated 13C experimental data for experimental design purposes. For example, one can simulate different sets of mass isotopomer distribution (MID) data for a wide array of labeled substrates, and measured metabolites. Next, one can perform Bayesian inference on these simulated data with BayFlux to predict the uncertainty surrounding key reactions of interest, and then select an experimental design that provides maximum information on these specific reactions. Additional metabolites to simulate must be defined while processing a model with BayFlux, because, for performance reasons BayFlux automatically avoids computing portions of a model unnecessary to simulate a given set of experimental data.This Elementary Metabolic Unit (EMU) code also provides the option of defining extra metabolites to simulate beyond those measured experimentally, S1 Text(PDF)Click here for additional data file.


Saccharomyces cerevisiae) in the production of metabolites of socio-economic relevance, knowledge gaps/obstacles in fungal biology and engineering need to be remedied for complete exploitation of valuable fungal strains. Herein, the thematic article discusses the novel attributes of bio-based products from fungi and the creation of high-value engineered fungal strains to promote yield, bio-functionality, and value-addition of the metabolites of socio-economic value. Efforts have been made to discuss the existing limitations in fungal chassis and how the advances in synthetic biology provide a plausible solution.Substantial progress has been achieved and knowledge gaps addressed in synthetic biology-mediated engineering of biological organisms to produce high-value metabolites. Bio-based products from fungi are extensively explored in the present era, attributed to their emerging importance in the industrial sector, healthcare, and food applications. The edible group of fungi and multiple fungal strains defines attractive biological resources for high-value metabolites comprising food additives, pigments, dyes, industrial chemicals, and antibiotics, including other compounds. In this direction, synthetic biology-mediated genetic chassis of fungal strains to enhance/add value to novel chemical entities of biological origin is opening new avenues in fungal biotechnology. While substantial success has been achieved in the genetic manipulation of economically viable fungi (including  Ascomycota and Basidiomycota), regarded as edible delicacies and key nutritional sources, across the globe. Although several yeasts  and multiple fungal strains have been industrially employed to produce high-value metabolites, filamentous fungi demonstrate key properties, including presence in solid-state cultures, metabolite production, and polymers degradation, and thereby, prospects in commercial applications [Basidiomycota species) are being explored and commercialized as nutraceuticals/supplements, and functional foods attributed to multiple pharmacological properties [Fusarium, Aspergillus, and Penicillium sp. The mycelia of some fungal species comprise healthy lipids, dietary fiber, vitamins, etc., and the consumption of these components defines key health advantages [The increasing prevalence of chronic diseases including neurodegenerative, diabetes, and cardiovascular diseases can be addressed by adopting a healthy lifestyle and a balanced diet. Different kinds of less/fat-free diets, low carbohydrate diets , and soyications ,9. The pications . In the operties ,12, and vantages ,14. Certvantages  while otvantages .Aspergillus niger [Penicillium rubens [A. oryzae and A. niger [Blakeslea trispora [A. terreus [Acremonium chrysogenum [Ashbya gossypii [Trichoderma sp. [Rhizopus oryzae [S. cerevisiae [Mortierella alpina [Saccharomyces boulardii [Penicillium sp. [Present decade has witnessed research initiatives on filamentous fungi yielding high-value substances of socio-economic significance, including citrate from us niger , penicilm rubens , pigmentm rubens , pharmacm rubens ,20, induA. niger , lycopentrispora , organic terreus ,24,25, βysogenum , riboflagossypii , β galacerma sp. , cosmetierma sp. , fortifis oryzae , and S. revisiae , polyunsa alpina , probiotoulardii , and fiblium sp. , includiTremella and their free-radical scavenging effect, and polysaccharides from Morchella esculenta improve the activity of antioxidant enzymes [Agaricus bisporus controlling cancer cell lines growth [Tremella that relieve epidermal bleeding and inflammation [Ganoderma that promote the immune function [Ganoderma capsules (for increased immunity) [Lentiana edodes (as chemotherapy adjuvant) [Coprinus comatus, and A. camphorata (which reduce high blood pressure) [Pleaurotus eryngii (food ingredient) [L. edodes  [In recent times, the production of food products from microbial sources has witnessed a tremendous upsurge, attributed to the nutritional content and health benefits of high-value compounds. Microbial biosynthesis not only improves the nutritional content/quality of the food, but it also improves the bio-functionality of the food products via imparting beneficial properties including antimicrobial action, peptide production, probiotic properties, removal of antinutritive substances, and fibrinolytic activity, among others ,35. A va enzymes ; antitums growth ; anti-agammation ; and immfunction , among ommunity) , Lentinadjuvant) , Hanqi eressure) , pork saredient) , Canned redient) , and Pasplement) , among oSeveral fungi culture strategies, including solid-state fermentation (SSF) and liquid-state fermentation (LSF), increase the production of fungal biomass, facilitating an easy recovery and increased metabolite production . FurtherPenicillium chrysogenum, Aspergillus terreus, Trichoderma reesei, and Thermothelomyces thermophiles are selected for genetic manipulations attributed to their biotechnological potentials [A. niger is extensively used in diverse industrial applications and harnessed to produce food ingredients, metabolites, vegan leather, vitamins, etc. [A. niger, many other filamentous strains are used nowadays, in diverse commercial applications, including biofuel, textile, food, pharma, and chemical industries.Fungal biotechnology aims to define and harness the metabolic functions of filamentous fungi of potential interest and their commercialization in diverse industries, and the health sector . The bratentials ,57. Partns, etc. . BesidesS. cerevisiae, the metabolic engineering attempts toward the chassis of filamentous fungi are still limited. The synthetic biology-mediated chassis of fungal strains for introducing a desired trait or enhancing the production of high-value metabolites for biotechnology or therapeutics is gaining momentum. Biotechnological advances in genome editing via CRISPR-Cas, metabolic pathway reconstitution, and expression in a heterologous host, and gene knockouts, have yielded translational success, as exemplified by key examples [While substantial progress has been made in engineering examples ,19,58. WThe bioactive compounds produced by fungi comprise novel scaffolds and are key bioresources in multi-faceted applications, ranging from bioactive compounds in healthcare to high-value functional food and compounds of commercial/industrial value. Fungal species as a biological platform have the potential for nanoparticle synthesis via extracellular or intracellular enzyme reduction . The proThe branch of metabolic engineering aims to modify a biological organism to modulate its metabolism. Genetic manipulation is commonly employed in yeast, plants, or bacteria for enhanced production of metabolites, demonstrating socio-economic attributes ,73 Figu. The treS. cerevisiae has been extensively studied as the production platform for metabolites of socio-economic value. Biotechnological interventions have been carried out in yeast including metabolic engineering for fatty acid-derived n-butanol (biofuel) production [A. niger for citric acid and galactaric acid (organic acid) production [Y. lipolytica for omega-3 eicosapentaenoic acid production [Blakeslea tripora for lycopene (carotenoid) production [In the past decades, oduction ,79, A. noduction ,81, Y. loduction , Blakesloduction , and othPichia pastoris (Komagataella spp.) glycosylated recombinant proteins and showed higher protein secretion [Kluyveromyces lactis was used for efficient recombinant protein production [Yarrowia lipolytica used hydrocarbon source and synthesized fatty products [S. cerevisiae was engineered for isoprenoid synthesis [P. pastoris was manipulated for membrane proteins production [Among other genetic studies in yeasts, key studies include: ecretion ; Kluyveroduction ; Yarrowiproducts ; S. cereynthesis ; and P. oduction . In addioduction .S. cerevisiae has efficient integration of DNA at desired regions, facilitating easy biological chassis [S. cerevisiae and design promoters, cloning plasmids, methods, etc., to benefit their diverse applications in biotechnology. In this direction, efficient transformation protocols, expression systems, and a range of selection markers have been developed [Monascus were developed for enhanced pigment production, monashin via enzyme polyketide synthase mutation [M. purpureus induced thermal shock protein and superoxide dismutase and improved survival in C. elegans [M. purpureus was cultivated in fermented rice extract and produced monapurpureusone (a new azaphylone) and monapurpureusin , respectively [Basidiomycetes biosynthesize natural food colorants such as phenazines, melanins, flavins, azaphilones, and quinones [Advances in synthetic biology strategies have contributed significantly to enhancing the production of high-value metabolites from diverse fungal strains. Genetic manipulations and mutagenesis aim to address the yield improvement of the desired metabolites from biological systems . Usually chassis . With an chassis ,134. Moreveloped ,136. Seveveloped . In anotmutation . The act elegans . A mutanectively . The filquinones . It is squinones . Recent Penicillium chrysogenum was screened for penicillin (an antibiotic) production and it produced 100-fold higher titers than the original strain. Further, developments in screening and strain improvement led to industrial production of penicillin  [High-value metabolites are increasingly identified and isolated from biological organisms, including fungi. However, the amount of these bioactive metabolites is in low concentrations, limiting industrial applications. Moreover, the presence of “cryptic pathways” which are silent and do not produce metabolites is documented and necessitates a requirement of strain improvement approaches . In prev strain) . This me strain) .Trichoderma reesei Rut C-30, [Penicillium chrysogenum [Penicillium oxalicum, phosphate-solubilizing fungi for improved production of organic acid and phosphate solubilization, respectively, which increased significantly on mutagenesis by radiation. In addition, physical and chemical mutagens were used to produce high-value metabolites from fungi, aimed toward a bio-based economy.For strain improvement in fungi, different mutagenesis approaches were adopted, using chemical or physical agents. To induce specific or random alterations, low/controlled levels were used, since they may lead to harmful mutations , and scrut C-30, , and Penysogenum . Random ysogenum . Yang anysogenum  employedP. chrysogenum BW1890 (with multiple copies of gene clusters) leads to a 64-fold increase in the production of penicillin [A. nidulans showed aminoadipyl-cysteinylvaline synthetase (ACVS) as a limiting enzyme, and the gene overexpression for ACVS resulted in enhanced ACVS expression and increased penicillin production [When the precursor supply does not affect product titer, the pathway enzyme expression may result in key outcomes. For example, a high-yielding strain of nicillin . The advnicillin , and pronicillin , includioduction . Howeveroduction . Malla aoduction  studied oduction . An impooduction .Streptomyces, gene clusters encode Streptomyces antibiotic regulatory protein (SARP) that positively regulates the production of antibiotics [Streptomyces griseus ATCC 49344 was overexpressed and led to higher antibiotic production in the engineered strain [MtmR (mithramycin gene cluster in Streptomyces argillaceus) overexpression increased mithramycin titer 16-fold, respectively, and the MTMR-activated actinorhodin-producing pathway when expressed in S. coelicolor [In some organisms, the production of desired metabolites has been improved by altering the regulatory components of a metabolic pathway. For example, in ibiotics . Moreoved strain . Furtherelicolor .S. coelicolor engineering by overexpression of ACCase genes for enhanced malonyl-CoA production, and the study resulted in enhanced actinorhodin production [E. coli, including pathway knockouts, gene overexpression, and limiting pathways for malonyl-CoA degradation, resulting in a 15-fold increase in malonyl-CoA [E. coli for precursor supply have focused on the heterologous expression of the MVA pathway or its improvement for increased isopentenyl pyrophosphate (IPP) production, a precursor in the generation of terpenoids [dxr), 1-deoxy-D-xylulose-5-phosphate synthase (dxs), and isopentenyl diphosphate isomerase (idi) overexpression enhanced production of isoprenoids [For all the major classes of natural products, increasing the supply of precursor molecules has been a successful method in both native and heterologous systems. These precursors can be primary metabolites or those derived from primary metabolites. For example, malonyl-CoA comprises a key precursor for polyketide biosynthesis; Ryu and colleagues attempted oduction . Zha andoduction  combinedonyl-CoA . Substanrpenoids ,164. Resprenoids . In primprenoids .erg9 and utilizes farnesyl-pyrophosphate (FPP), a sesquiterpene precursor, and amorphadiene production is increased by knocking out the erg9 gene, respectively. In another example discussing doxorubicin biosynthesis, multiple genes encoded by the dxr cluster were removed to improve desired protein production [E. coli. In the study, gene knockouts were identified utilizing a metabolic modeling approach, for improving NADPH availability and thereby, flavonoid production [Streptomyces avermillitis, in which the genome was reduced to 83% of its original size, creating space for the introduction of a gene cluster of streptomycin. The minimized genome of Streptomyces produced higher amounts of streptomycin, highlighting a prospective approach to enhance the production of biochemicals [Another prospective approach in this direction is to delete certain genes for pathway silencing so that associated metabolic pathways and their unnecessary intermediates can be avoided. A key example highlights that squalene synthase in yeast is encoded by oduction . In addioduction . Chemleroduction  showed Noduction . Komatsuoduction  reportedhemicals .Streptomyces species (producing dihydrogranaticin and medermycin) were engineered by introducing actinorhodin pathway genes, resulting in hybrid antibiotics, dihydrogranatihordin, and mederrhodin, respectively [In this direction, the existence of divergence and homologies among genes between related metabolic pathways have led to switching genes and modules between interlinked pathways for novel microbial chassis . The metectively . For patectively . The bioectively . Novel fectively . One keyectively .Aspergillus, Trichoderma, and Penicillium sp. having industrial importance. Moreover, studies have documented the genetic manipulation of fungal strains for heterologous protein production. Manganese peroxidase, classified in the family of heme-containing peroxidases, degrades lignin and is produced by white-rot fungi, which has relevance in chemical industries. The two proteins, manganese peroxide and Interleukin 6, were produced in Aspergillus species [Mortierella alpinis [Fusarium veneratum [A. japonicas [Chrysosporium lucknowense [Cordyceps militaris  chassis; codon-optimized cas9 was used with promoter Pcmlsm3, and terminator Tcmura3 was expressed in the system. A CRISPR-Cas9 system comprising a single-strand DNA template, Cas9 DNA endonuclease, and RNA pre-synthesized in vitro was employed for insertion and site-specific deletion. The study aimed at genome editing of edible mushrooms for increasing genomic chassis and rapid development as ‘functional food’, respectively [C. militaris for enhanced ergothioneine production by discovering and regulating the metabolic pathway for ergothioneine biosynthesis [Fusarium fujikuroi, genetic manipulation methods were employed for enhanced gibberellic acid production [An emerging genome editing tool, CRISPR/Cas has witnessed key success in genome editing of filamentous fungi to produce high-value metabolites including pigments, enzymes, secondary metabolites, compounds of industrial importance, and agriculture, respectively. The CRISPR/Cas tool has been widely employed in improving fungal strains including  species . Besides alpinis , Fusariueneratum , A. japoaponicas , Chrysosknowense , have beectively . Chen anectively  employedynthesis . In Fusaoduction .A. flavus, A. oryzae, S. cerevisiae, etc. [Y. lipolytica for β-hydroxylase and β-ketolase production by gene introduction in fungal species and increased astaxanthin production [Metabolic engineering of microbes has witnessed good translational success, with multiple bacterial and fungal species engineered for food additives production in recent times . For theae, etc. . The CRIoduction . This stoduction . Moreoveoduction .Harnessing biological resources for high-value products in drug discovery and research is gaining considerable recognition; however, bottlenecks in low product yield in the native organism, tedious procedures in isolation and characterization, and limited knowledge of metabolic machinery make it an ardent task. Chemical modifications of natural scaffolds are challenging, and the major limiting factors have declined the interest in natural products, necessitating a need to address associated concerns. The bio-based production of high-value compounds highlights promising attributes, and recent advances in high-throughput technologies and synthetic biology have contributed immensely to addressing these limitations. Applications of state-of-the-art technologies including omics biology, computational approaches, genome analysis, and genome editing facilitate unprecedented outcomes.Aspergillus sp. and genetic engineering is cumbersome. Other limitations with high-throughput sequencing of fungal species with large colony formation and long process of mutant construction (a long time for spore formation in agar culture) further add to the limitations in industrial exploitation.Metabolic engineering approaches coupled with drug discovery and development can be a powerful tool, exemplified and discussed with significant examples of fungal chassis. Furthermore, emerging insights/knowledge of natural product pathways, their regulations, and dynamics have facilitated the screening and development of novel chemical entities, with the potential to the impact socio-economic arena. Significant translational success has been achieved in the genetic chassis of the biological organisms; however, it is difficult to predict the biosynthetic categories of both NPs and BGCs and the biological function, attributed to the complexities in the regulation of BGCs. Moreover, gene disruption methods have unraveled multiple biosynthetic pathways including the mycotoxin asirochlorine pathway , the proWhile significant advances in synthetic biology approaches have substantially contributed to the genetic manipulation of fungal organisms, challenges with precursor availability for natural product biosynthesis, low metabolic flux, and low enzyme activity in microbes add to the limitations . In this


Gloeocapsopsis dulcis strain AAB1 is an extremely xerotolerant cyanobacterium isolated from the Atacama Desert  that holds astrobiological significance due to its ability to biosynthesize compatible solutes at ultra-low water activities. We sequenced and assembled the G. dulcis genome de novo using a combination of long- and short-read sequencing, which resulted in high-quality consensus sequences of the chromosome and two plasmids. We leveraged the G. dulcis genome to generate a genome-scale metabolic model (iGd895) to simulate growth in silico. iGd895 represents, to our knowledge, the first genome-scale metabolic reconstruction developed for an extremely xerotolerant cyanobacterium. The model's predictive capability was assessed by comparing the in silico growth rate with in vitro growth rates of G. dulcis, in addition to the synthesis of trehalose. iGd895 allowed us to explore simulations of key metabolic processes such as essential pathways for water-stress tolerance, and significant alterations to reaction flux distribution and metabolic network reorganization resulting from water limitation. Our study provides insights into the potential metabolic strategies employed by G. dulcis, emphasizing the crucial roles of compatible solutes, metabolic water, energy conservation, and the precise regulation of reaction rates in their adaptation to water stress. Gloeocapsopsis dulcis strain AAB1 is a hypolithic and extremely xerotolerant cyanobacterium first identified in biofilms collected from the underside of translucent quartz stones in the Atacama Desert’s Coastal Range1. Azua-Bustos et al.1 suggested that the hypolithic biofilms rely on the regular coastal fogs, known as camanchacas, as their main source of water. Specifically, G. dulcis was shown2 to tolerate desiccating conditions in-part by increasingly synthesizing the compatible solutes trehalose and sucrose following several weeks to months of desiccation at the extremely low sustained water activity (aw) of 0.4.3. Bacteria can biosynthesize or scavenge these solutes from the environment to maintain osmotic equilibrium and protein function5. Other cellular adjustments due to xeric stress may include changes to the fluidity of the cell membrane, upregulation of DNA-repair proteins and reactive oxygen species scavengers, potential dormancy or sporulation, and an up- or down-regulation of certain metabolic pathways depending on changes in energy demands6. The specific metabolic adaptations employed by extremely desiccation-tolerant cyanobacteria like G. dulcis remain relatively unknown. However, understanding these adaptations could be critical in identifying strategies for the survival of life in arid planetary environments such as Mars, especially given that the Atacama Desert is an established Martian analog7.The accumulation of compatible solutes is one of the many adaptation mechanisms involved in microbial xerotoleranceiGd895) to explore the metabolic capacity of G. dulcis undergoing desiccation. GEMs have been used to decipher microbial metabolism by integrating genomic information into a mathematical framework that simulates metabolism using optimization techniques, such as flux balance analysis (FBA)8. This technique allows for the prediction of cellular growth and production rates of metabolites under specified conditions. GEMs have been utilized for diverse applications, such as predicting enzyme functions, modeling microbial cell interactions9, and investigating microbial growth in various environmental contexts11.In this study, we developed a genome-scale model  to elucidate the potential metabolic strategies employed by G. dulcis undergoing desiccation. Through the utilization of these techniques, we identified crucial metabolic pathways and reactions potentially involved in desiccation tolerance. Additionally, we determined how these pathways may undergo shifts in response to water limitation. By shedding light on the metabolic adaptations employed by an extremely xerotolerant cyanobacterium, our findings provide not only new insights into the potential mechanisms of desiccation tolerance but also hold promise for the search for microbial life in similar arid environments.We simulated G. dulcis cells that were originally isolated by Azua-Bustos et al.2 from the Atacama Coastal Range. Briefly, 32 mg of the dried cells were hydrated with 300 µl of phosphate-buffered saline, vortexed for two minutes, and extracted using the Quick-DNA Fecal/Soil Microprep Kit . The extraction was performed according to manufacturer’s instructions, except for the lysis step, which was performed using a TerraLyzer Cell Disrupter (Zymo Research) for 3 min. DNA was eluted in 20 µl and determined by a fluorometric assay  to have a concentration of 6.2 ng µl−1.DNA was extracted directly from dried An Illumina paired-end sequencing library was created with the Nextera XT DNA library preparation kit , following the manufacturer’s instructions. The library was sequenced on a HiSeq instrument  in a 2 × 250 paired-end run (PE250). Following sequencing, 6,867,392 Illumina raw read pairs were obtained.12.To prepare for ONT sequencing, DNA was extracted using the Quick-DNA Fungal/Bacterial Kit  by adding five mg of dried cells and 200 µl of sterile deionized water directly to BashingBead tubes. Cell lysis was performed for 2 min using the Terralyzer Cell Disruptor, with a total of 23 mg lysed over five replicates. Replicates were pooled, and 500 ng of the extracted DNA was used to generate ONT sequencing libraries with the 1D ligation method (SQK-LSK108) and native barcoding  using a “one-pot” barcoding protocolONT sequencing was performed with a SpotON flow cell (R9.4.1) and MinKNOW software 1.14.1 (GUI 2.1.14) (Oxford Nanopore Technologies). Following sequencing, 8302 long-reads were obtained, of which 8105 passed the pre-set filter . Raw reads were rebasecalled using the “Super-Accurate” (SUP) model Guppy basecaller (v6.0.1) integrated in MinKNOW prior to sequence assembly.13. First, the ONT reads were subjected to quality control using Filtlong v0.2.114. The reads were subsampled with Trycycler to create 12 read sets. Twelve assemblies were generated from the read sets using the Flye v2.915 and Raven v1.8.116 assemblers . The assemblies were visually inspected using Bandage17.The genome sequence was assembled de novo using the Trycycler v0.5.3 hybrid assembly pipelineThe contigs were clustered and reconciled using Trycycler’s cluster and reconcile commands, respectively. This resulted in three clusters: one representing the main chromosome, and the other two representing each plasmid. ONT reads were then partitioned between the reconciled clusters using Trycycler’s partition command. Finally, Trycycler’s consensus command was used to make the consensus sequences for each of the three clusters.18, which dropped 0.38% of the total reads . The surviving 6,260,175 Illumina read pairs were quality controlled using fastp19. The Illumina reads were then used to polish the ONT consensus sequences in two rounds; first with Polypolish v0.5.0-120, and then again with POLCA21. Sequence quality, completeness, and potential contamination were checked using the CheckM lineage workflow22. The online RAST (Rapid Annotation using Subsystem Technology) service was used to annotate the genome23. We also used Prokka to verify gene locations within the main chromosome24. The annotated genome was visualized using the online Proksee tool (https://proksee.ca/). The consensus sequences  were deposited in the National Center for Biotechnology Information (NCBI) under BioProject PRJNA941297.Next, Illumina reads were trimmed with TrimmomaticGloeocapsopsis dulcis cells, provided by Dr. Armando Azua-Bustos2, were grown in 250 ml flasks with 100 ml of sterile BG-11 medium (UTEX). Growth assays were conducted in triplicate with the addition of a negative control flask filled with 100 ml of sterile BG-11 medium. The flasks were incubated at 28 °C over several weeks with a 12 h on/off light cycle. Two mL of sample were removed from each flask and placed into semi-micro polystyrene cuvettes  to determine the optical density (OD) at 600, 730, and 750 nm over several weeks using a NanoDrop OneC instrument .G. dulcis cells were counted using a light microscope and C-Chip Neubauer Improved hemocytometers . Samples at an OD730–750 of 0.1 were homogenized via vortex, and 10 µL were pipetted into each side of the hemocytometer. Cells within the five large squares (area of 1.0 mm2) of the hemocytometer were counted, and the average was used to determine cell concentration. The data was plotted in R, and the package Growthcurver25 was used to fit a linear regression model and determine the specific growth rate (µ).Following OD measurements, 26. KBase built the draft biomass objective function based on its Gram-negative biomass template, which assumes a DNA, RNA, protein, cell wall, lipid, and cofactor fraction of the biomass as 0.026, 0.0655, 0.5284, 0.25, 0.075, and 0.1, respectively. The draft model was gapfilled in KBase on “complete media”, and then downloaded in SBML and Excel formats for manual curation. In Python, the biomass objective function was updated based on the genome and cyanobacterial biomass compositions  using the COBRApy library29 to reflect a biomass fractions of DNA, RNA, protein, cell wall, lipid, cofactors, carbohydrates, and other as 0.031, 0.17, 0.51, 0.06, 0.12, 0.04, 0.034, and 0.003, respectively. Growth-associated maintenance energy, deoxyribonucleotides, ribonucleotides, amino acids, cofactors, and inorganic ions (mmol gDW−1) were determined using the methods of Thiele and Palsson30. The specific biomass composition calculations are detailed in File 31.The draft reconstruction was created in KBase using the “Build Metabolic Model” app and the RAST-annotated genome33 and later refined by Toyoshima et al.31 to model the photosynthetic metabolism of cyanobacteria under different spectral lights . Incident photon sites were described as reactions R0001, R0010, and R0022, for the phycobilisome, photosystem II, and photosystem I, respectively. State transitions, or the energy transfer from the phycobilisome to photosystems I and II, were described as reactions R0006 and R0007, respectively. We used the incident photon ratio of Synechocystis grown under white light as the flux of the photon incident sites31. The photon incident rate for the simulation was normalized to 495 mmol gDW−1 h−1 as per the rationale explained below.Following that, 45 photosynthesis-related reactions were added to the model File . The reaG. dulcis biomass was estimated as follows: a spherical G. dulcis cell is 3.3 µm diameter34, with a volume of 19 µm−3, and surface area of 34 µm2. We estimated a dry cell weight of 7.4 pg by assuming that 70% of the cell was water36, and that the remaining ~ 1/3 of cellular components had a density of 1.3 g cm−337. Thus, the surface area per gram dry weight of biomass was estimated as 4.59 m2. From there, a photon uptake rate of 495 mmol gDW−1 h−1 could be converted to 30 μE m−2 s−1, which is the average photosynthetic photon flux density experienced by G. dulcis living under quartz rocks, and in the laboratory1.The total cellular surface area per gram of 30. Briefly, confidence scores were assigned to each reaction based on what level of evidence is there to support inclusion of the biochemical reaction. Biochemical, genetic, or sequence/physiological data would receive a confidence score of 4, 3, or 2, respectively. Much of the G. dulcis reactions received confidence scores of 2, where only sequence data was available. Identified genes were manually assigned to the corresponding reactions in the Excel version of the model . Universally blocked reactions are those that cannot carry a flux even without constraints imposed by the growth medium. Both orphan and dead-end metabolites and universally blocked reactions represent knowledge gaps that can be filled in the future with additional data30. These reactions and metabolites were documented in File Once the manual curation was complete, a FROG report was generated using FLUXER, and on GitHub (https://github.com/Drrachelmoore/Gloeocapsopsis_dulcis-GEM) where it will be curated with additional data in the future.The final curated model was deposited in the EMBL-EBI BioModels database and was designated with the perennial identifier: MODEL2303050001. The full FROG report, and model in SBML, YAML, MATLAB, JSON, and Excel formats can be found in File S1G. dulcis in the laboratory  with the Gurobi Optimizer in Python  using the PyCharm IDE. Briefly, we used the model.optimize function to run the FBA and determine optimal growth rates within the constraints of the BG-11 medium. We also simulated single reaction deletions using the knock_out function to determine reaction essentiality for both water-uptake constrained and unconstrained simulations. Water exchange with the environment was restricted to 2 mmol gDW−1 day−1 in the constrained FBA simulations. This resulted in a maximum yield of 0.1 g H2O gDW−1 , which roughly corresponded to an Aw of 0.543.We developed the GEM growth medium based on the BG-11 recipe used to grow ory File  followinG. dulcis with and without water constraints45. Default parameters were used unless stated otherwise. Briefly, the ComMet method works by creating a constrained and an unconstrained version of a GEM, where the constraint is placed on the uptake of a specific substrate. It then follows a seven-step pipeline to decompose the flux spaces into modules, conduct principal component analysis, basis rotation, and independent component analysis to compare the metabolic states. Specifically, we constrained the water uptake to 12 mmol gDW−1 h−1 for the constrained GEM to investigate changes in the iGd895 metabolic network with xeric stress. Following ComMet preprocessing of the GEM flux spaces, we determined the flux distribution for all of the reactions in the unconstrained and constrained GEMs using the expectation propagation algorithm46. Next, we used ComMet’s PCA-based approach to decompose the flux spaces and extract global modules. Finally, xeric-stress-specific modules were identified using independent component analysis. Following analysis with ComMet, Cytoscape (Version 3.9.1) was utilized to plot the module networks47. R and R Studio (Version 2021.09.0) along with the ggplot2 library were used to generate plots of the flux distribution data49.We used the COMparison of METabolic states  method in MATLAB  to compare metabolic states for G. dulcis are summarized in Table 13 we obtained a genome comprised of a ~ 5.29 Mbp chromosome and two plasmids, 11,484 and 307,228 bp in size. The mean read depth of the main chromosome was 492× as determined by Polypolish20. The mean read depth for the larger and smaller plasmid was 518× and 1040×, respectively. Following genome polishing with POLCA21, the consensus quality and consensus QV were estimated to be 99.9999 and 61.22, respectively, corresponding to an expected value of 4 errors across the genome. We then used the CheckM lineage workflow22 to determine a genome completeness of 100%, and a low potential sequence contamination of 1.44%  of the closed genome matched well with the original draft sequence produced by Puente-Sánchez et al.2 previously identified two candidate genes involved in the synthesis of the compatible solute sucrose: sucrose 6-phosphate synthase and sucrose 6-phosphate phosphatase. We identified the location of those genes on the chromosome, as well as other potential genes associated with the production of trehalose, which is another compatible solute produced by G. dulcis  using the G. dulcis genome to further elucidate the potential mechanisms underlying desiccation tolerance  is presented in Table iGd895, we compared the model’s growth rate with experimental growth rates. We found that an OD730–750 of 0.1 for G. dulcis corresponded to a cell count of 6.9 ± 0.45 × 105 ml−1. We determined the in vitro growth rate of G. dulcis to be 0.06 ± 0.007 day−1 .To verify the accuracy of y−1 Fig. , which iiGd895 on the in-silico BG-11 growth medium was found to be 0.0694 day−1, falling within the range of the experimentally observed growth rates. This indicated that the model adequately replicated the metabolic activities that take place in G. dulcis on BG-11. Still, caution should be applied to the interpretation of iGd895 results as further validation would require additional experimental data that was beyond the scope of this study and not yet available in the literature.The simulated specific growth rate of iGd895 under clement and desiccating conditions: reactions rxn01134 and rxn01966 for trehalose production, and rxn00577, rxn00578, and rxn00579 for sucrose production , in vivo evidence of trehalose synthesis has only been shown with TreP isolated from fungi and certain bacteria like Caldanaerobacter subterraneus subsp. Tengcongensis and C. subterraneus subsp. pacificus . In constrained iGd895, rxn01966  synthesized 0.0023 mmol gDW−1 day−1 of trehalose, which suggested that trehalose phosphorylase may be able to function in the synthesis direction for G. dulcis. It should be noted that although trehalose was exclusively produced by TreP in constrained iGd895, it can also be generated by trehalose synthase when it is set as the objective function . Thus, while we anticipate that TreP would be upregulated in desiccated G. dulcis, TreS may also be upregulated to synthesize trehalose in vivo.Reaction rxn01966 encoded trehalose phosphorylase , which catalyzes the reversible hydrolysis of trehalose to produce glucose-1-phosphate and glucose. While the TreP enzyme has been found in cyanobacteria  prevented the expectation propagation algorithm from converging. Thus, as before, we did not consider it a true ‘desiccated’ model.We used the ComMet pipelineFurther preprocessing was carried out following the ComMet pipeline to remove 900 blocked reactions  from each model. Apart from the water constraint, ComMet set the bounds for the other exchange reactions in the models to allow for unlimited nutrient uptake, effectively removing them as a factor. However, to simulate photoautotrophy, 70 exchange reactions were removed from both models (edited ComMet code on GitHub). For example, exchange reactions of amino acids, nitrate, and organic carbon sources were removed to block mixotrophic metabolism and to better represent experimental lab testing scenarios. This preprocessing narrowed the unconstrained and constrained GEM to 289 and 282 reactions, respectively, from the original set of 1251.46 in ComMet to approximate the feasible spaces of both GEMs, and in doing so, calculated the mean and standard deviations of the flux distribution for every reaction in the simulations. We produced a reaction-wise comparison plot of the means  was found to be downregulated and the enzymatic activity of triose-phosphate isomerase  was found to decrease in response to water loss in Nostoc flagelliforme63. Consistently, our model revealed significant decreases in reaction flux for these glycolytic enzymes represented by reaction rxn01100 and rxn00747 in iGd895 when water uptake was constrained  activity could contribute to the modulation of carbon fixation and the allocation of carbon resources to glycogen storage68. Reduction of pentose phosphate pathway reaction fluxes could also help to conserve resources that could then be redirected toward glycogen synthesis. Reactions rxn00772 and rxn00770 . These PCs were chosen as they accounted for 99.9% of the flux space variation in both simulations . InitialG. dulcis network -Glycerate kinase and used in the Calvin cycle for carbon fixation, thus contributing indirectly to carbon fixation. However, the absence of these two reactions  in the constrained simulation, raises the possibility that G. dulcis may use glycogen as a carbon-storage mechanism during water-limited conditions, similar to what was indicated in the flux distribution data above  was only present in the constrained simulation as shown in Fig. Reaction rxn00411 was exclusively present in the unconstrained simulation Fig.  and reprG. dulcis may prioritize energy conservation and/or employ the nucleoside-diphosphate kinase (EC 2.7.4.6) enzyme, in addition to the other two enzymes, in order to meet energy demands.ATP synthase , which catalyzes oxidative phosphorylation, and ATP:NAD+ 2′-phosphotransferase , which can catalyze the production of ATP from ADP and NADP+ in the nicotinate and nicotinamide metabolism pathway, were present in both simulations. However, there were significant differences in the mean fluxes File  of reactG. dulcis may prioritize cellular processes such as DNA replication, RNA transcription, and growth primarily when water is abundant, but shift to prioritize energy production and conservation during water scarcity.The presence of the nucleoside-diphosphate kinase-catalyzed reaction , which can generate ATP in cyanobacteria by transferring a phosphate group from a nucleoside triphosphate to ADP, was exclusive to the constrained simulation. This reaction may be preferred over the ATP synthase-catalyzed reaction as it requires less water for catalysis. Additional reactions exclusive to the constrained simulation in the purine  and pyrimidine metabolism  pathways may support this function File . The latPhotosynthesis reactions  were present in both simulations, with R0047 and R0048 responsible for carotenoid transport and exchange, respectively. R543 represents the photosynthetic NADPH synthesis flux using ferredoxins. Only the unconstrained simulation contained reactions R0045 and R0046, which represent green light  exchange with the environment and absorption by the orange carotenoid protein. The only photosynthesis-related reaction exclusive to the constrained simulation was R0037, which represents quinol oxidase (EC 7.1.1.7), an enzyme that catalyzes the transfer of electrons from quinol molecules to molecular oxygen, producing water in the process.71. The production of metabolic water may also play a role in supporting the stability of cellular structures, such as proteins and membranes, which are prone to damage and denaturation under desiccation stress. In addition, quinol oxidase is involved in the regulation of electron transfer in the respiratory chain and redox homeostasis, which may be crucial for maintaining cellular energy balance under stress conditions. Overall, the identification of quinol oxidase in the constrained simulation suggested that this enzyme could play a crucial role in generating metabolic water and maintaining redox balance, enabling survival in desiccating environments like the Atacama Desert.The presence of quinol oxidase in the constrained simulation indicated that the production of metabolic water may be critical for survival during desiccation. For example, metabolic water production has been thought to sustain the hydration needs of cold desert soil-dwelling microbesG. dulcis cyanobacteria exhibit a multifaceted metabolic response to desiccation. Specifically, our genome-scale modeling analysis revealed significant metabolic shifts that may occur during desiccation, particularly during the initial limitation of water. These shifts involved the reconfiguration of metabolism from ATP production/consumption to a greater emphasis on ATP conservation, as well as a reduced emphasis on carbon fixation in favor of glycogen accumulation. Furthermore, our results highlight the potentially critical role of metabolic water production for G. dulcis survival during desiccation. The significant changes in reaction fluxes between metabolic states imply a tight regulation of specific pathways under water limitation. Finally, the closing and annotation of the G. dulcis genome led to the identification of several stress response genes that are likely to be upregulated during desiccation. Collectively, these findings advance our understanding of how microorganisms may survive extremely arid environments.Our findings suggest that Supplementary File S1.Supplementary File S2.Supplementary File S3.Supplementary File S4.Supplementary File S5.Supplementary File S6.Supplementary File S7.Supplementary File S8.Supplementary File S9.Supplementary Figures.Supplemental Files and Tables: Table of Contents.Supplemental Results and Discussion.Supplementary Table S1.Supplementary Table S2.