Water is the most common reactant in the autotrophic core, indicating that the core arose in an aqueous environment. Seventy-seven core reactions involve the hydrolysis of high-energy phosphate bonds, furthermore suggesting the presence of a non-enzymatic and highly exergonic chemical reaction capable of continuously synthesizing activated phosphate bonds. CO2 is the most common carbon-containing compound in the core. An abundance of NADH and NADPH-dependent redox reactions in the autotrophic core, the central role of CO2, and the circumstance that the core’s main products are far more reduced than CO2 indicate that the core arose in a highly reducing environment. The chemical reactions of the autotrophic core suggest that it arose from H2, inorganic carbon, and NH3 in an aqueous environment marked by highly reducing and continuously far from equilibrium conditions. Such conditions are very similar to those found in serpentinizing hydrothermal systems.The metabolism of cells contains evidence reflecting the process by which they arose. Here, we have identified the ancient core of autotrophic metabolism encompassing 404 reactions that comprise the reaction network from H Au. Au51]. An undirected metabolic network showing the autotrophic core was generated in simple interaction format (sif) using a custom Python script. The resulting network consists of the given 404 metabolic reactions and 380 involved compounds. The bipartite network was visualized using CytoScape  v. 3.8.0Two additional reaction datasets were used to determine their intersection with the 404 reactions of the autotrophic core. The LUCA set, containing 355 genes, was identified via the phylogenetic approach , translap-values smaller than 0.05. One-tailed Fisher tests were performed using the package scipy.stats in Python 3.6 , or a piece of iron oxide magnetite (Fe3O4), can also catalyze the synthesis of pyruvate from H2 and CO2 [If we look at the cofactors required to get from Hhanogens ,62, we f pathway  seem dow and CO2  overnigh and CO2 , and it 420 and cobamide) to support their own synthesis plus the synthesis of four ribonucleoside triphosphates, four deoxyribonucleoside triphosphates, and 20 amino acids. The genetic code and polymers are not included in the autotrophic core. In total, that makes a list of 47 target compounds  synthesis pathway; the pyruvoyl cofactor is synthesized from serine residues in the polypeptide chain of the enzyme . Very su2 and CO2. Critics of autotrophic origins will be quick to point out that cyanide chemistry can readily give rise to amino acids and bases under laboratory conditions [2 as a substrate in either the forward or reverse direction [– to CO2 and NH3 or formate and NH3 for growth [2, formate, and NH3 readily enter metabolism, whereas cyanide does not. In other words, CO2 directly enters and exits the organic chemistry of the cell substance at 415 reactions, where cyanide puts up a zero. We interpret the fact that cyanide has nothing to do with modern metabolism as a clear metabolic fossil: no role for cyanide in modern metabolism indicates that cyanide had nothing to do with primordial metabolism either, or was at best <1/415th as important as CO2. In that sense, the main message of The starting point of nditions ,64, suchirection , but ther growth ,67, beca3 (NH4+ is very unreactive) with N atoms replacing O atoms in amino acids, either via an acyl phosphate intermediate in the glutamine synthase reaction or via reductive aminations of 2-oxoacids [3 reacts with carboxyphosphate to form carbamate in pyrimidine and arginine biosynthesis. Of course, NH3 is synthesized from N2 by nitrogenase to make it available for incorporation into organic compounds. However, N does not enter metabolism as N2; it enters metabolism as NH3, which is why we selected NH3 as the source of nitrogen in 2S in cysteine synthesis from serine, either via serine activation as O-acetylserine or O-phosphoserine [3 and H2S) via amino acid synthesis [2 and CO2, C, N, S, and electrons (H2) enter metabolism as gasses.That brings us to nitrogen. If carbon did not enter metabolism via cyanide, then the same must be true for nitrogen. If not via cyanide, how did N enter metabolism? All the amino acids, bases, and cofactors contain nitrogen (except coenzyme M). Nitrogen enters metabolism as NHoxoacids ,68. An ehoserine . N and Synthesis . In cell2, CO2, and NH3, but it does not depict the reactions themselves. To find out which, what kind of, and how many reactions are required to synthesize 18 cofactors, 8 nucleotides, and 20 amino acids from H2, CO2, NH3, and H2S, we turned to KEGG pathways using 2O. As stated above, water is the solvent of life’s chemistry and its most common reaction partner. Proponents of the RNA world generally view water as a poison for origins, because it promotes the hydrolysis of RNA [3O+, although they are not counted as water here. Protons arise in hydride transfer reactions involving NADH and NADPH which yield NAD+ and NADP+, respectively. The frequency of protons in the network mainly reflects the frequency of NAD(P)H-dependent redox reactions in the autotrophic core , CO2, glutamate, pyruvate, and 2-oxoglutarate. This suggests a more crucial role of these compounds in the origin of the core subsequent to later evolution in anaerobes, reflecting a process of carbon backbone elongation from CO2 at the heart of the core as a supply of precursors for cofactor and amino acid biosynthesis, the latter being the starting point for nucleotide biosynthesis [The comparison with 5994 anaerobic prokaryotic reactions (see S1A in ) tells uynthesis .It is human nature to wonder about the origin of life, which is an issue that is among the most debated of all scientific questions. However, in comparison to questions concerning the existence of dark matter or how consciousness works, the origins process lies in the ancient past, and its events are only accessible through inference. Debates within the origins field can be fierce and have a long history. They hinge upon definitions about what qualifies as being alive, what one assumes to be the habitat that brought forth the first biochemical reactions, what came first, small molecule metabolism and proteins or nucleic acids and genetics, what the nature of first energy source(s) was that the early life forms harnessed in order to grow, and what kinds of chemical compounds existed before the first energy-releasing reactions germane to modern metabolism started taking place . The litBiologists tend to hold that there are traces of early evolution preserved in metabolism itself ,6. While2 and CO2 using only solid-state metal or metal oxides as a catalyst [2 and CO2 and grew outwards from pyruvate while incorporating nitrogen from NH3. How complex the core could have become prior to the origin of enzymes is a question for future study. However, let us keep in mind that enzymes just accelerate reactions that tend to occur anyway. It is well known that many enzymatic reactions take place without enzymes [Here, we have identified 404 reactions that comprise the autotrophic core. It contains five small autocatalytic cycles in which cofactors participate in their own synthesis. The core represents a collection of reactions that underpin the synthesis of RNA and proteins. It was present in the first cells, but it can hardly have arisen all at once. The aqueous synthesis of pyruvate from Hcatalyst  indicate enzymes , althoug enzymes . However enzymes ,81 and a enzymes ,82 can b2O is the most common reactant in the autotrophic core, indicating an aqueous environment during its formation. That environment was not only aqueous but also reducing, as revealed by the abundance of redox reactions in the autotrophic core, the central role of CO2, and the circumstance that the core’s main products (amino acids and nucleic acids) are far more reduced than CO2. Furthermore, the number of central reactions depending upon the hydrolysis of high-energy phosphate bonds indicates that the core arose in the presence of a continuous and highly exergonic chemical reaction capable of continuously synthesizing high-energy phosphate bonds, both before and after the origin of enzymes; here, an H2-dependent CO2 reduction to acetate [H acetate  forming  acetate  is the p2, CO2, and NH3, was highly reducing, and harboring continuously far from equilibrium conditions. Those conditions are very similar to those found in serpentinizing hydrothermal systems [Thus, the chemical reactions of the autotrophic core suggest that it formed in an aqueous environment that supplied H systems ,77, and  systems ,83.Notwithstanding pyrrolysine , selenoc


Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol , one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.The thermophilic strain of the genus  Klebsiella, Enterobacter, Bacillus, Lactobacillus and Serratia have been studied so far as microorganisms which are able to synthesize 2,3-BTD [Klebsiella spp. strains are stable under a wide range of growth conditions and possess the highest product yield, the strain biosafety level 2 assignment restricts its biotechnological applications for the industrial-scale 2,3-BTD production. In a search for an alternative producer, a novel aerobic thermophilic Geobacillus strain excreting 2,3-BTD has been identified. The thermophilic properties of the strain provide additional cost-effective strategies for microbial fermentation of 2,3-butanediol [Biological factories designed for the production of bulk chemical and fuels are crucial components of the future manufacturing, focused on eliminating environmental pollution and reduction of fossil fuel and oil price dependencies. 2,3-butanediol  has a broad list of industrial applications and it represents one of the essential commodity chemicals . The pot 2,3-BTD ,2. WhileGeobacillus have optimal growth conditions at temperatures near 50–60 °C that promote extremely high growth and carbon conversion rates [Geobacillus are capable of utilizing various carbon sources [Geobacillus spp. became a source of thermostable proteins and enzymes [Geobacillus icigianus G1w1T is a new thermophilic strain of the genus Geobacillus that was firstly isolated from sludge of the hydrothermal vent located in the Valley of Geysers  [Geobacillus icigianus metabolism including whole-genome metabolic reconstruction and flux balance analysis.The majority of species of the genus on rates . Further sources ,5,6. Man enzymes ,5. The s enzymes ,5. The G Russia)  enables Geobacillus icigianus G1w1T strain to generate and excrete 2,3-BTD under certain growth conditions. Based on the analysis of the developed iMK1321 model by means of diverse evolutionary optimization algorithms we have found two feasible and alternative ways to enhance 2,3-BTD production from different carbon sources in this particular microorganism. The first mechanism is an operating mode of the culture growth in anaerobic or microaerophilic conditions, while the second one is related to genetic modifications of the TCA cycle leading to the reduction of succinate production.A genome-scale metabolic (GSM) modeling approach together with constraint-based flux balance analysis (FBA) have been developed as computational platforms to provide a holistic view of the cellular metabolism in pro- and eukaryotes and predict flux distribution in a global metabolic network both under a range of environmental conditions and genetic perturbations . Herein,Geobacillus icigianus strain (GenBank assembly accession: GCA_000750005.1) [Bacillus subtilis strain [G. icigianus. SEED [https://github.com/mkulyashov/Geobacillus_icigianus_supplementary).The complete genome of 50005.1) . The KBa50005.1)  was empl50005.1) . The vass strain , since tus. SEED  reactionus. SEED  using anus. SEED  to improus. SEED , which d−1 h−1 according to the published data for closely related species [+, PO43−, Mg2+, Ca2+, K+ and Na+) was set to −1000 mmol gDCW−1 h−1, while the upper bound was set as +1000 mmol gDCW−1 h−1 for them. The upper bound for the majority of cellular metabolic reactions was assumed to be +1000 mmol gDCW−1 h−1, unless stated otherwise. The lower bounds of the reversible and irreversible cellular metabolic reactions were set as −1000 mmol gDCW−1 h−1 and 0 mmol gDCW−1 h−1, respectively. To simulate a growth of the strain on glucose as a single carbon source the lower bound of the glucose uptake rate was considered as −16 mmol gDCW species . A lowerGeobacillus spp: glycerine, L-arabinose and D-xylose [Geobacillus icigianus genome was confirmed by BLASTP alignment [https://github.com/mkulyashov/Geobacillus_icigianus_supplementary), while GPR for ribulose-5-phosphate 4-epimerase (EC: 5.1.3.4) was based on the BioCyc annotation [G.icigianus. Additionally, the ability to grow on xylose as a single carbon source was experimentally shown [Geobacillus species and similarity of the pathway to the metabolic pathways of Bacillus subtilis in SEED, KEGG [G.icigianus as well. The uptake rates for other carbon sources  were proportionally calculated on the basis of the number of carbon molecules regarding the glucose since these rates were not measured in experiments yet. A set of carbon source exchange reactions were added for substrates known to support the growth of D-xylose . The prelignment  -butanediol transport and -butanediol exchange reactions were also added similarly to the iYO844 model [Reactions required for 2,3-butanediol production were manually evaluated. The metabolic routes for 2,3-BTD production to synthesize the substance comprises: acetolactate synthase (EC:2.2.1.6), acetolactate decarboxylase (EC: 4.1.1.5) and -butanediol dehydrogenase (EC:1.1.1.4). Only acetolactate synthase was originally presented in the draft model, the newly identified metabolic reactions were added using Cobrapy. It is noteworthy that the BLASTP analysis demonstrated that acetolactate decarboxylase (EC: 4.1.1.5) is not encoded in rganisms . However pathway . Given t44 model . The finhttps://github.com/mkulyashov/Geobacillus_icigianus_supplementary/tree/master/model_files. The model is also available online at BioUML web-service (https://ict.biouml.org/bioumlweb/#de=data/Collaboration/FBA%20models/) [The maximization of the biomass equation was used as an objective function of the model for parsimonious flux balance analysis (pFBA) which attempts to minimize overall cellular flux while maximizing the growth rate . Optfluxmodels/) . The lisTo identify genetic modifications which are essential to enhance 2,3-butanediol production a Biomass-Product Coupled Yield (BPCY) as the objective function of an evolutionary optimization approach has been used in the Optflux tool. To conduct this type of analysis, the following simulation algorithms have been selected: pFBA, Minimization of Metabolites Balance (MiMBL) , Linear Geobacillus, G.icigianus belongs to the Clade 1 while G. thermoglucosidasius strains are included in the second clade [Geobacillus, there are only a few published GSM models for this genus. https://memote.io/, [The genome-scale metabolic model was reconstructed using a semi-automatic pipeline including genome annotation with RAST  and subsnd clade . Althougote.io/, ), the cuote.io/, ,33,34, dA manual curation of the model enabled us to enhance the number of gene-protein-reaction associations  and incorporate reactions required for uptake and initial steps of metabolic conversion of diverse carbon sources as well as a set of metabolic reactions for 2,3-BTD production. Parsimonious flux balance analysis was conducted via the Optflux software . Figure G. icigianus metabolic map differs from the B. subtilis flux distribution, despite the fact that the B. subtilis metabolic pathways and biomass equation were used as a template for our draft model. For instance, G. icigianus mainly uses FRD3 reaction catalyzed by fumarate reductase as a reaction in the electron transfer system, when glucose is a carbon source and SUCDi reaction for other carbon sources, while B. subtilis employs SUCDi and NADH4 reactions. Furthermore, a metabolic reaction catalyzed by 6-phosphogluconolactonase  is absent in the pentose phosphate pathway of G.icigianus according to the reconstructed iMK1321 model, while this reaction is presented in iYO844 model for B.subtilis loaded from BIGG database (http://bigg.ucsd.edu/models/iYO844). Seemingly, it is due to the evolutionary elimination of the gene encoding 6-phosphogluconolactonase in the genus Geobacillus since an experimental verification indicated that this reaction can spontaneously proceed at room temperature and the reaction rate might be sufficient in thermophilic microorganisms for the functionality of the pentose phosphate pathway [Geobacillus spp. may have an alternative oxidation route for the production of ribulose-5-phosphate via reactions catalyzed by 6-phospho-3-hexuloisomerase and 3-hexulose-6-phosphate synthase [G.icigianus genome (see https://github.com/mkulyashov/Geobacillus_icigianus_supplementary). Further analysis of the flux distribution for different carbon sources shows that lactate and succinate are excreted compounds during the growth on xylose, glucose and arabinose and it is consistent with published experimental data [G.icigianus (0.5 mmol gDCWl−1 h−1), but growth on glycerine requires more oxygen (G.icigianus). A comparative analysis of the pFBA predictions  and slightly enhanced growth rate for B.subtilis comparing to G.icigianus if the glucose consumption rate equals to the used one in iMK1321 model (−16 mmol gDCWl−1 h−1) experimentally measured for Geobacillus thermoglucosidasius NCIMB 11955 [Bacillus subtilis while all published growth rates for strains of the genus Geobacillus are much higher than the estimation for B.subtilis [The observed distribution of fluxes through dictions  conducte demonstsubtilis ,36.G.icigianus and take into account the various strain optimization strategies that may predict different routes for metabolic engineering of the strain. Since the genome-scale metabolic modeling provides a phenotype prediction based on the studied organism genotype, this approach is a powerful computational tool for rational strain optimization within such a booming field as metabolic engineering. To develop a strategy for achievement of desired cellular behaviour using genetic manipulations, a myriad of in silico optimization methods have been proposed . Herein,Corynebacterium glutamicum [The LMOMA algorithm was able to discover 78 solutions for growth on glucose; 90 solutions for growth on both glycerine and xylose; and 72 solutions for growth where arabinose is a carbon source. Optimization results with the most benefit for 2,3-BTD production are presented in utamicum . Some otStrain optimization to increase the level of 2,3-butanediol produced by the cell using the MiMBL algorithm predicts 43 optimal solutions for glucose, 83 solutions for xylose, 100 solutions for arabinose and 26 solutions for glycerine, as substrates for bacterial growth. The best optimization results for this algorithm are presented in The pFBA algorithm was able to identify a significantly lower number of optimal solutions for 2,3-BTD production: 1 solution for cells grown on glucose, arabinose and glycerine and 2 solutions for growth on xylose G.icigianus in oxygen-limited conditions.Thus, in silico optimization algorithms indicate two possibilities to enhance the production of 2,3-BTD by the strain including genetic modifications of the TCA cycle which lead to decrease in succinate production and growth of Bacillus subtilis strain has been carried out with the LMOMA algorithm. Results of the optimization on different substrates demonstrate that Geobacillus icigianus gives more effective production of 2,3-BTD in most cases. However, Bacilus subtilis can be a more suitable microorganism for 2,3-BTD biosynthesis if arabinose is a carbon source for the growth G. icigianus has the highest level of 2,3-butanediol production on glycerine-dependent growth. The outcome proposes the biotechnological potential of the strain in conversion of the industrial by-product into value-added target compounds.In addition to that, a computational optimization of 2,3-butanediol production by Escherichia coli was revealed to increase the production of another related commodity chemical, 1,4-butanediol [iMK1321 genome-scale metabolic model.To verify the model predictions a thorough analysis of the published experimental data on mechanisms of strain improvements for accumulation of 2,3-BTD including closely related species has been undertaken. A transition of the bacterial growth to microaerobic or anaerobic conditions is one of the common experimental approaches to increase the production of 2,3-BTD in many biotechnological species ,41,42. FiMK1321 for a recently isolated thermophilic strain of the genus Geobacillus, Geobacillus icigianus has been reconstructed. The model has been built using Kbase web-service with consequent manual curation. The final version of the model represents the most comprehensive GSM reconstruction developed for this genus, enabling in silico interrogation of the bacterial growth on diverse substrates including glucose, glycerine, xylose and arabinose. Computational analysis of the model indicates glycerine and glucose as the most suitable carbon sources for G.icigianus cultivation. To validate the model predictions, continuous culture cultivation of the strain on different carbon sources is planned to be conducted in future studies. Furthermore, a comparative analysis of the model simulations with distribution of the fluxes predicted by the iYO844 model for closely related Bacillus subtilis demonstrates some differences in metabolic rearrangements between these microbial systems. The developed model has also been employed to predict optimal solutions for enhanced 2,3-butanediol production by this particular thermophilic strain. In silico predictions based on different optimization algorithms to accelerate biosynthesis of 2,3-BTD comprise genetic modifications related to some steps of the TCA cycle which result in a decrease in succinate production and cultivation of the strain in oxygen-limited conditions. Identified strategies of metabolic engineering for G.icigianus have been already experimentally demonstrated as capabilities for active 2,3-BTD biosynthesis in closely related species [With the development of industrial biotechnology, thermophilic microorganisms are considered as a unique and attractive platform in whole cell applications. In this study, a genome scale metabolic model,  species ,43,44,45


In systems biology, material balances, kinetic models, and thermodynamic boundary conditions are increasingly used for metabolic network analysis. It is remarkable that the reversibility of enzyme-catalyzed reactions and the influence of cytosolic conditions are often neglected in kinetic models. In fact, enzyme-catalyzed reactions in numerous metabolic pathways such as in glycolysis are often reversible, i.e., they only proceed until an equilibrium state is reached and not until the substrate is completely consumed. Here, we propose the use of irreversible thermodynamics to describe the kinetic approximation to the equilibrium state in a consistent way with very few adjustable parameters. Using a flux-force approach allowed describing the influence of cytosolic conditions on the kinetics by only one single parameter. The approach was applied to reaction steps 2 and 9 of glycolysis . The temperature dependence of the kinetic parameter fulfills the Arrhenius relation and the derived activation energies are plausible. All the data obtained in this work were measured efficiently and accurately by means of isothermal titration calorimetry (ITC). The combination of calorimetric monitoring with simple flux-force relations has the potential for adequate consideration of cytosolic conditions in a simple manner. Note that the product formation rate corresponds to the substrate consumption rate with an inverted sign:In systems biology, models are expected to describe enzyme kinetics with high precision at relatively low metabolite concentrations (in the lower micro- to millimolar range). Since all metabolic fluxes decay when reaching a thermodynamic equilibrium, the approximation to the state of equilibrium must also be well described. Often, only the kinetic data of enzyme-catalyzed reactions are published. Based on such data, various kinetic models for data analysis of enzyme-catalyzed reactions have been developed in the literature. −1) is a kinetic constant of first order. −1 s−1). E) binds reversibly to a substrate (S) and forms an enzyme-substrate-complex (ES) that releases the enzyme and the product (P) in an irreversible step. Here, an enzyme (er k−cat :(4) E+ SFor simplicity, the majority of published research focused on the initial steady state of reactions (no product formation), so that the backward reaction could be neglected . This siSystematic analyses of the published thermodynamic and kinetic studies of both reactions reveal two shortcomings. First, the kinetic data are mainly evaluated using the irreversible Michaelis-Menten model and therefore, disregard the reversibility of the reaction ,23,24,25Any of the above-described model still needs experimental data. Thus, new data are presented in this work. Calorimetry provides real time information about the investigated reaction, requiring neither sampling nor interacting with the reaction partners . Therefo−1) instead of molarity (mol L−1). The latter is more common in biochemistry and bioengineering but the use of molality is important for the link to thermodynamics. Molality only depends on the masses of solute and solvent, which are unaffected by variations in temperature, pressure, and density. From a practical point of view, the differences between molality and molarity under cytosolic conditions are small. For instance, the density of water decreases by only 0.6% in the temperature range between 25 and 40 °C. In the following, all concentrations are given as molality (mol kgr (mol kg−1 s−1) and the “conjugate” thermodynamic driving force Irreversible thermodynamics postulates a linear relation between flux or reaction rate reaction :(7)r = ∝α, the correlation factor, links the substrate consumption rate r with the thermodynamic driving force. −1), the activity and the stoichiometric coefficients, respectively. R and T are the universal gas constant and the temperature. In both investigated reactions we evaluated the opposite direction of the glycolysis; that is, F6P is considered as a substrate and G6P as a product of reaction 2, while PEP is considered as a substrate and 2PG as a product of reaction 9. Taking the equilibrium condition ):This model was refined by Noor in 2013  who expr−1), −1) denote the kinetic constant of the forward reaction, the substrate concentration at any time of the reaction, the product concentration at any time of the reaction, and the Michaelis constants for the substrate and product, respectively. Using Equations (10) and (12)–(14), the mass balance ).Both, forward and backward reaction use the same catalytic center of the enzyme and it is justified to assume a proportionality between the sum of nzyme cE ,37. Thus+r and −r is proportional to L is called a phenomenological coefficient or kinetic parameter in the following:Now, we introduce that the sum of L, which is a kinetic parameter that can be very suitable for further investigations aiming at comparing the influence of cytosolic conditions on the kinetics. Furthermore, it perfectly describes the approximation to the equilibrium state. For data analysis, a plot of the rate against L. Since the measured heat flow data at the beginning of the reactions are disturbed by the inertia of the ITC and the heat of dilution, this part of the curve will not be used for kinetic evaluation. This corresponds to The great biggest advantage of this model is that it correlates the rate with the substrate concentration as the frequently used Michaelis-Menten model does, but contains only one fit parameter L of the flux-force model follows the Arrhenius relationship : represents the heat production rate (in W). The corresponding rate For testing kinetic models, reaction rates as a function of the substrate concentration are required. The concentration of the substrate at each single measuring point was calculated with Equation (29) using the concept of Todd and Gomez :(29)cS. For the flux-force model, both an R2 and a p-value could be determined. The p-value is given as zero by Origin and at a 0.05 level slope is significantly different from 0. In every flux-force measurement the R2 value was greater than 0.993. The exact values can be found in For the data evaluation and statistics we used OriginPro, Version 2019 OriginLab Corporation, Northampton, MA, USA. A statistical evaluation was carried out for all the measurements. Measurements at every temperature were performed as triplicates. For each parameter the mean and the standard deviation were reported. Noor′s fit parameters provided an RSystems biology approaches aim for the simplest possible kinetic approaches with as few adjustable parameters as possible, while maintaining a good representation of the kinetics at different cytosolic conditions. The conventional irreversible Michaelis-Menten model has only two parameters but does not take the backward reaction into account. The reversible Michaelis-Menten equation and models derived from it capture the equilibrium correctly, but often have too many parameters to describe the influence of the cytosolic conditions in a simple manner. The Noor model and the one-parameter flux-force relations seem to be perfectly suited for this purpose and to describe the time course of at least reactions 2 and 9 of glycolysis. An advantage of flux-force approaches is, that they combine the thermodynamics and kinetics of metabolic processes in a single equation in order to quantitatively describe the approximation to the equilibrium perfectly. Both models tested, Noor and flux-force, show similar dependencies of the reaction rate on the temperature. The flux-force model could also be verified using an Arrhenius plot. Further work will be done to verify these statements under cytosolic conditions. In other glycolytic reactions or initial conditions at an equilibrium distance with very large thermodynamic driving forces, deviations from the observed linear flux-force relationship may occur. For reaction cascades with complex interactions even oscillations are described. This will be further investigated in future work.The ITC is well suited for a fast and easy quantitative analysis. Especially in the non-linear range with the possibility of oscillations, the heat production rate as a real time signal could be very useful. Time-consuming and failure-prone sampling is no longer necessary. However, the reaction time should be chosen long enough to assure that the kinetics of the reaction rather than the thermal inertia of the instrument determine the signal. This can be achieved by wisely selecting a suitable enzyme concentration, for example.


Chinese hamster ovary (CHO) cell culture has a major importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Mathematical modeling of biological systems can successfully assess metabolism complexity while providing logical and systematic methods for relevant genetic target and culture parameter identification toward cell growth and productivity improvements. Most modeling approaches on CHO cells have been performed under stationary constraints, and only a few dynamic models have been presented on simplified reaction sets, due to substantial overparameterization problems. The hybrid cybernetic modeling (HCM) approach has been recently used to describe the dynamic behavior by incorporating regulation between different metabolic states by elementary mode participation control, with sets of equations evaluated by objective functions. However, as metabolic networks evaluated are constructed toward a genomic scale, and cell compartmentalization is considered, identification of the active set becomes more difficult as EM number exponentially grows. Thus, the development of robust approaches for EM active set selection and analysis with smaller computational requirements is required to impulse the use of cybernetic modeling on larger up to genome-scale networks. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis (PSYA) was compared to other reported elementary mode selection approaches derived from Common Metabolic Objective Analysis (CMOA) used in Flux Balance Analysis (FBA), Yield Space Analysis (YSA), and Lumped Yield Space Analysis (LYSA). For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the HCM approach with selected elementary mode sets. The yield space elementary mode- and the polar space elementary mode- hybrid cybernetic models presented the best fits and performances. Also, a flux reaction perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets. Mammalian cell culture has significant importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb)  introduces regulation using two dynamic vector variables, which modify the participation of minimal and unique reaction flux subsets, or elementary modes (EM), obtained from the stoichiometric matrix analysis. These cybernetic variables are regulated by objective functions evaluated with sets of matching law equations. This approach has been proved useful to describe dynamic metabolic state fluxes and regulation even with little information on the mechanistic particularities, allowing reasonable dynamic flux distribution modeling  to an angular space or polar description, to determine a reduced and feasible EM active set. This novel PSYA was compared to other EMA approaches derived from optimization around CMOA often used in FBA  media containing 28 mM glucose (GLC) and supplemented with 8 mM glutamine (GLN). Cultures were kept for 144 h and samples were taken every 24 h. Samples were analyzed for viable (Xv) and dead (Xd) cell concentrations by cell counting with the trypan blue exclusion. Major dissolved components, GLC, lactic acid (LAC), GLN, and glutamic acid (GLU), were determined on culture supernatant, with a YSI 2950D biochemical analyzer equipped with specific membranes for each compound. Specific growth, uptake and production rates approximations were obtained by linear regressions during mid-exponential growth phase and mid stationary/cell-death phase and used as initial values for model parameter determination. Biomass units were transformed from cells/mL to mM with a conversion established by Nolan and Lee  was set to be the sum of viable (Xv) and dead (Xd) biomass and described by a classical logistic growth equation (Equation 1). Xd was described by Equation (2). Finally, Xv was obtained by the difference between both previously described equations, such that:Models were constructed with differential mass balance equation sets for culture macroscopic characterization. Total biomass  models were constructed starting from the following simple differential equation for any external metabolite (Mi) concentration:where μqi refers to its specific rate of consumption/production. To better describe the behavior during cell growth, viable cells can be segregated into two states: a growth metabolic state and a stationary metabolic state (before cell death). Therefore, the two populations with different metabolic characteristics can be described as:where Xv as:where e and Ψs represent fractional allocations of cells on the different stages, such that Ψe + Ψs = 1  to (8) into Equation (4) allows the expression the external metabolite differential equations as:qi for each phase can be defined as Michaelis–Menten equations sets with maximum specific rates and saturation constants for each Mi regarding the key metabolite(s) consumed for its production/consumption. As this equation is difficult to resolve analytically, a numeric approximation for the differential can then be proposed. Therefore, the extended model equations for the CHO-S external metabolites production/consumption constructed for the metabolites measured on this report were:Finally, the specific rates g, n, l, u, and h refer to GLC, GLN, LAC, and GLU, respectively. The q parameters refer to the maximum specific production/consumption rates and are specific for exponential (e) or stationary (s) phases. The K parameters refer to the Michaelis–Menten saturation constants for each key metabolite. Note that the Michaelis–Menten sections of the presented model can be extended for many key metabolites, but on this report only the main contributions  were selected in order to reduce the number of parameters. Equations (10) to (13) were numerically integrated with Δt = 0.1h, which is at least three orders of magnitude below from those where sensible changes on growth and metabolite concentration occur. Production and consumption rates were approximated by Sum of the Square Error (SSE) against experimental data. Models constructed were evaluated by the Mean Absolute Percentage Error (MAPE), the Prediction Error (PE), and a Mean Prediction Percentage Error (MPPE) calculations  and angles θi describing its directions across the polytope, as follows:where j is the number of reactions yields that are being addressed for a θ angle, up to the total nth reaction. Therefore, the convex solution hull can be transformed from a Cartesian convex hull space to multiple Polar convex spaces depending on different θi subsets, derived from different yield compositions and orders used for calculations. Each subset of angles does not only reflect the information about yields, but their ordered relationship and ratio. As a result, a set of calculated angles can be thought of as a pathway description through the n-dimensional polytope of the solution space.where The modules and angles from any set can then be conformed into two vectors that define the polar convex hull description Λ and Θ. From which also a parallel subset can be calculated containing only the known reaction yields, such that:where super-index ϵ refers to the vectors calculated using only the EM values for reactions with known experimental yield data. Experimental yields can also be located on the solution space with similar vectors, Φ for the angle information and Γ as the scalar module. This allows an easy comparison of all EM, where at least one can be selected by finding the minimum difference with the experimental data, given by:EMι can be used to describe the metabolic behavior. However, this happens seldom, so a minimal active set should be constructed around the experimental data. The next EM are selected consecutively by maximizing the distance to the middle point between the previously selected EM while minimizing the distance to the experimental data point. This procedure allows the construction of a polyhedral structure composed of the minimal set of EMact containing the polar space experimental data point. Also, this EMact can be lumped by a linear combination of the selected EM. The algorithmic implementation diagram of the presented approach can be found in If Υ for the selected EM equals 0, then a single ϵ, and Φ sets can be calculated as stated before, as different starting angle calculation orders can be performed, each one describing a particular path inside the polytope toward the point it is describing, and therefore, representing different information about yield ratios between different reactions. This arises a particularly interesting property of the solution space: if all permutations constructing all the Λϵ sets for a particular EM or destination Φ, given they are parallel , gives the same results for the closest selected EM, then the solution space may be orthogonal for all its coordinates. However, if the results are different, the solution space must be a non-Euclidean space. In fact, for the data presented on this work, various permutations were performed, and slightly different solutions were found . However, extended experiments and calculations to this possible conjecture will be presented elsewhere, as it is not in the scope of this report. In this work, yields used for calculation vector definitions were Yg/x, Yn/x, Yl/x, and Yu/x, and used in that order as chosen by the following ordering rules: (1) substrates before products, (2) higher consumption/production participation. This definition allowed to select a minimal set of EM with PSYA and Lumped Elementary Mode Analysis (LPSYA) to describe the metabolic behavior.It's essential to notice that multiple Θ, ΘTo analyze and compare PSYA and LPSYA approaches, different EM selection approaches were also used for the construction of dynamic exponential growth metabolic models. An EMA selection approach was derived from the minimization analysis of metabolic objectives (CMOA) commonly used in FBA . The dynamic exponential metabolic models constructed were evaluated for their experimental data approximation performance by SSE, PE, MAPE, and MPPE and their CPU usage times. All calculations were performed on MATLAB with a Lenovo, Intel Core i7, 2.5 GHz computer with 16.0 GB RAM, and Windows 10 running only the operative system and MATLAB. CPU usage times were calculated considering only the EMA sections without network loading, any plotting, displaying or other processing, and normalized against EM mode number for each constrained system.where Si) catalyzed by a critical enzyme (Ei) to produce biomass and other products. Recursively, Ei synthesis is upregulated by the presence of its specific substrate Si. Therefore, it can be written :HCM approach was used for the final dynamic characterization of the metabolic behavior of CHO-S cells during the complete culture coupled to YSA, LYSA, PSYA, and LPSYA. Mathematical description of the cybernetic approach can be found on Ramkrishna et al. reports  selected were described with rate equations similar to the physiological model.In this work, each cybernetic approach was implemented using two EM selected families constructed by YSA, LYSA, PSYA, and LPSYA performed with yields calculated at the start of the culture  and time of maximum LAC consumption rate (second family). Cybernetic modeling was then performed as reported by Ramkrishna et al. , kι refers to the maximum consumption rate on each fermentation process section. Kι refers to the saturation constant for each selected EM. Flux rate equation parameters were approximated to experimental data using a genetic algorithm programmed by Martinez et al. . This inter-crossed set passed onto the next generation, where another round of individual parameter perturbation was made. The algorithm was cycled until either a constant SSE was obtained or more than ten cycles were performed without finding a smaller SSE value. Finally, these parameters were subjected to a final SSE non-linear numeric minimization to model the flux rates of each EM and the final metabolic dynamic flux model for each fermentation.where z et al. . BrieflyEMι and select a path of new EM, which has, at the same time, the closest distance to it and minimize or maximize an individual θy. This path can be calculated on an iterative process replacing EMι by the new EM determined up until a certain arbitrary or set parameter such as EMi = ι+1…n and EMι, and finding the next EM by:The most difficult assessment for metabolic models constructed around EM selection by experimental data, is their prediction capabilities, specifically regarding genetic modifications. This difficulty arises from the fact that no metabolic objective was used for the selection of the principal EM used on the metabolic network. This characteristic makes the straightforward selection of a new EM with reaction perturbations  difficult. In this work, an approach to generate new EM active sets with modified reactions was constructed with the use of the previously described polar solution space. This approach is based on the assumption that cells have redundant and highly regulated metabolic reactions, which translate to the minimal metabolic modification possible but efficient enough to adjust to new conditions, either abiotic or biotic, as used on the Minimization Of Metabolic Adjustment (MOMA) approach . The results for the concentration, rates, specific rates, and yields against X for Xv, GLC, GLN, LAC, and GLU are presented in Xv rate equal to zero (≈106 h), and having LAC consumption rate decreased to approximately its half. This death process was nearly immediate to substrate exhaustion and suggested high cell maintenance cost. All parameters for specific rates and statistical data from equation models can be found in Result observations and error estimations suggested that all models were able to describe, with acceptable accuracy, the physiological behavior of the cultured CHO-S cells. Therefore, all parameters derived and calculated from them can be used as descriptors to characterize cell line behavior. Since models were numerically integrated, approximate rate values were calculated for the biomass and the main dissolved extracellular metabolites at every ΔThe metabolic network constructed , is a si1 refers to the angular calculation between GLC and GLN biomass yields, which means that as angle reaches 90° a higher ratio of GLN consumption is preferred and in the limits of 0 degrees only GLC consumption is preferred. θ2 represents the angular proportion for the yield of LAC production and the distribution between GLC and GLN consumption. Therefore, it shows the LAC production EM possibilities from no LAC production at 0° to the maximum output at 90°. As it can be observed, there are EM with high production of LAC on every θ1 substrate consumption composition, but there are more EM which relate to LAC production with GLC. More than 90 percent of EM found with LAC higher than 20° have less than 45° on θ1, which represents a higher GLC consumption reliance as expected. Meanwhile, θ3 represents the proportion of GLU production distribution to the ratio of LAC production over GLC and GLN consumption. Therefore, with higher values of θ3, higher distribution toward GLU production will be obtained. The EM distribution shows that higher GLU production is possible with higher GLN consumption proportion, as expected, but a tendency of reduction of GLU production with higher LAC production was also observed. Furthermore, at low LAC production (lower than 20° on θ2), very few EM are found.In Within the convex solution spaces, the experimental yield position was calculated and located, as shown in Dynamic metabolic profiles were calculated with Equation (26) for the best EM selected by each EMA in each constrained network, marked as a sub-index . It can The MPPE and CPU usage times for each approach and EM set are presented in To better evaluate and compare the proposed EMA approach, hybrid cybernetic models were constructed for the whole culture process with the YSA, LYSA, PSYA, and LPSYA methods. Results to be presented correspond to the best performing HCMs. Presented results for YSA-HCM and LYSA-HCM were calculated using the metabolic network with eight constraints, whereas for PSYA-HCM and LPSYA-HCM, the 6-constraints system is shown. For this matter, different EMA were performed on different culture stages to obtain multiple EM to be used by the cybernetic modeling approach, as described in the section 2. Instantaneous apparent Yields and effective rates from the physiological modeling analyses, allowed the identification of two apparent mean metabolic states during culture growth: the first based on GLC consumption favoring growth and LAC production, and a second one composed on a smaller growth rate and using LAC as primary substrate (with GLC co-utilization). Yield data for these two states were calculated at the start of culture and maximum LAC consumption  and used for EMA-HCM construction. Constraints for the 92.1 h changed on the LAC reaction, where it was modified to its consumption  was not only approximating to the experimental data, but also modeling plausible metabolic flux distributions, their biomass normalized flux distributions along time were calculated. As GLC started to diminish, less glycolytic flux was observed accompanied by an increase of pyruvate transformation to alanine coupled to GLU transformation to AKG (reaction v63) from 72 h and onward . The lat2, which would reduce LAC production and an increase in θ1 and θ3, meaning an increase on the proportion of GLN consumption and concomitant GLU production. The latter could be related to a higher need for TCA anaplerotic reactions derived from glutaminolysis as the carbon flux entering the mitochondria as PYR increases. EMPA selected 92 alongside EM that increase the flux of R39 consecutively up to approximately ten times the initial value and reduced LAC production almost by 42% on the best scenario.As PSYA showed good metabolic modeling performance, prediction capabilities for this approach were analyzed, as presented in the methods section. Selected reactions behavior along the R39 perturbation are presented in E. coli) by Martinez et al.  when the experimental or normalizing values are lower than one . This suggestion is derived from the observation that glucose consumption rates follow the Biomass rates, presenting their maxima at the same time with both values being |0.4| mM/mM*h. Even though Biomass continues to accumulate after the GLC consumption rate diminishes and other metabolites are being consumed more rapidly , its rate decays after this point. Biomass rate reduction becomes more evident as LAC re-consuming viable cells increase their contribution to the culture metabolism up to the maximum LAC consumption rate found at 92.1 h, just before GLC exhaustion. It is also essential to notice that GLN yield was reduced along with the LAC consumption, which means that LAC production may be correlated to this compound. The fact that GLC apparent yield and rate do not diminish up to ≈72 h, with already having LAC consumption metabolism activated, and furthermore the GLN apparently beginning its rate reduction (probably due to limiting concentrations), suggest that the LAC metabolic shift may be partially regulated or correlated to GLN concentration or its consumption rate. This correlation may be possible due to the known high glutaminolysis mitochondrial metabolism for TCA activation found in CHO cells  with similar results in different ranges of constrained systems. EM and experimental solutions on PSYA are described by angles and modules instead of Cartesian coordinates, making it easier to determine similarities between EM and known yields. It is important to notice that θa priori. As it has been reported, all minimal active sets on the yield space that contain within the experimental condition point will provide similar internal flux distribution and similar dynamical simulation results  can be summarized as a derived approach from the Yield Analysis presented by Song and Ramkrishna . With thIn the case study considered in this report, PSYA-HCM and LPSYA-HCM produced dynamic metabolic models that follow with reasonable accuracy the experimental behavior of the CHO-S cell culture. Moreover, flux distributions obtained with the PSYA-HCM were in agreement with the metabolism knowledge available in the literature, presenting low pentose phosphate flux, high LAC production, and low PYR on cytosol to PYR on mitochondria conversion, low TCA activation on isocitrate dehydrogenase and high recirculation toward mitochondrial PYR by malic enzymes, while catabolism of many amino acids remained low.The presented polar solution space also allowed to perform a perturbation analysis based on finding the closest EM sequentially to the previous parental EM, which maximizes or minimizes a particular reaction. With EMPA analysis, we were able to find and propose attractive modification reaction targets that could reduce LAC production in CHO-S cells culture. The findings regarding mitochondrial metabolism were found to agree with previous reports from other research groups, while some other not so evident targets were proposed. It is important to stress that these analyses were performed as proofs of concept for the further development of the presented PSYA, LPSYA, and EMPA approaches, being this report the first to propose a shift from Cartesian to Polar coordinates and tools for their analysis, resulting as an apparently reliable and useful approach for metabolic modeling by EM analysis.https://github.com/JuanAndresMtz/PSYA. Other requests can be obtained from the corresponding authors on reasonable request.The data generated or analyzed during this study are included in this published article [and its supplementary information files]. PSYA MATLAB example codes and data matrices are available at JM, DB, MC, LP, and OR conceived the study and designed the experiments. DB and MC carried out all culture experiments. JM constructed models, parameter calculations, and model implementations and programming. JM, DB, LP, and OR collaborated with strain and results analysis and characterization. All authors collaborated on manuscript construction and critically revised it. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod ( Induced by the availability of next-generation DNA sequencing as well as high-throughput genomic, proteomic, and metabolomic data, genome-scale metabolic models (GEMs) have become fundamental tools in the systems biology of metabolism. To develop a GEM, metabolic genes and their product enzymes are assembled into a network of metabolic reactions carried out by an organism Palsson, . Such neA comprehensive bottom-up protocol to generate high-quality reconstructions was presented a decade ago  is a key species in the North Atlantic marine ecosystem. Its genome was first published in 2011  gene from their genomes  MATLAB toolbox , medaka (Oryzias latipes), Takifugu (Takifugu rubripes), Tetraodon (Tetraodon nigroviridis), and stickleback (Gasterosteus aculeatus). Here, cDNA sequences from those species were analyzed and the corresponding list of metabolic genes was formed based on gene ontology. This list was then used to identify enzymes based on their orthology to human genes. Metabolic reactions were integrated from EHMN, the human metabolic network at UCSD (BiGG) . The Venn diagram in https://doi.org/10.15490/fairdomhub.1.assay.1365.2).The current version of the RAVEN toolbox adds a reaction to the draft if at least one of its catalyzing enzymes is mapped between species. Hence, the number of reactions and metabolites as produced by the printModelStats function in the toolbox does not reflect the actual gene mapping between iHepatocytes2322 and each of the draft models. One way to properly examine different cod annotation outcomes, given the known fish genome duplication, is to compare the number of mapped genes from iHepatocytes2322 with each draft, with strictness s3. But the current RAVEN only generates the mapped genes included in the draft model, we thus added this feature to the getModelFromHomology function and pulled the request to RAVEN's GitHub page (https://doi.org/10.15490/fairdomhub.1.datafile.3892.1. The pathways of benzo[a]pyrene (BaP) and 1,2-Dibromoethane from this subsystem are shown in A draft reconstruction typically contains missing genes and reactions which are gaps to be filled later. In cases where a template model is used as input, such gaps result from unmapped genes, and subsequent reactions, between the template and the target organisms. In order to examine the degree of gaps in our draft model, we considered the “Metabolism of xenobiotics by cytochrome P450” subsystem which is highly relevant to our case study. A map showing the gap filling of this subsystem was visualized by Escher , they were assigned to this family even though the specific gene homolog was not identified.ex vivo exposure of Atlantic cod liver slices to benzo(a)pyrene (BaP) pyrene-7,8-dihydrodiol-9,10-epoxide . SimultaIn this paper, we presented our experience in reconstructing a draft metabolic network for Atlantic cod liver. As our genome-scale reconstruction work is still on-going, the main intention in this paper is to highlight possible challenges of working on non-model less-annotated organisms. Draft reconstruction for model species is typically the fastest phase in the process, due to the abundance of biochemical knowledge and annotations. However, it becomes less straightforward with a lack of such data. Thanks to enabling computational tools, an alternative is to use existing generic or tissue-specific GEMs that fall within the context of the target reconstruction. In our case, a human liver model was used as a template to generate a draft cod liver model. The fact that it is a well-annotated consensus liver GEM compensated for known physiological and metabolic differences between cod and human, which could be resolved in the next model curation phases.in silico to identify essential genes for various biological conditions using the constraint-based model of the reconstructed network.Thus, insights can be gained into how important physiological processes in the organism, including energy metabolism, growth, and reproductive processes, can be affected by environmental toxicants.As demonstrated for the “Metabolism of xenobiotics by cytochrome P450” subsystem, gap filling is an essential part of model curation that need to be applied to all the included subsystems. We survey main gap filling approaches and then describe subsequence reconstruction steps. Several tools were presented to help automate this step. Among those approaches is Meneco  can be found at: in-silico work. TF generated the updated Atlantic cod genome assembly. DCZ proposed the idea of the case study and helped XZ with the subsequent analysis and visualization of the results. ME, FY, and AG provided input on all aspects related to biology and environmental toxicology and performed the experimental work of the case study. SF identified and summarized potential tools for gap analysis and helped formatting the manuscript. AG and IJ supervised the work. EMH and XZ worked on the final version of the manuscript which was reviewed and proofread by all authors.EMH designed the study and wrote the manuscript draft. EMH and XZ performed the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Synthesis gas, which is mainly produced from fossil fuels or biomass gasification, consists of C1 gases such as carbon monoxide, carbon dioxide, and methane as well as hydrogen. Acetogenic bacteria (acetogens) have emerged as an alternative solution to recycle C1 gases by converting them into value-added biochemicals using the Wood-Ljungdahl pathway. Despite the advantage of utilizing acetogens as biocatalysts, it is difficult to develop industrial-scale bioprocesses because of their slow growth rates and low productivities. To solve these problems, conventional approaches to metabolic engineering have been applied; however, there are several limitations owing to the lack of required genetic bioparts for regulating their metabolic pathways. Recently, synthetic biology based on genetic parts, modules, and circuit design has been actively exploited to overcome the limitations in acetogen engineering. This review covers synthetic biology applications to design and build industrial platform acetogens. Over 100 acetogen species from 20 different genera have been isolated from diverse habitats, making the bacteria phylogenetically and physiologically diverse with a wide range of optimal growth conditions . Th. ThDeltaA. woodii strain was constructed to enhance acetate production by expressing both pta and ack genes encoding a phosphotransacetylase and acetate kinase from C. ljungdahlii, respectively, in a pJIR750ai backbone vector  to obtain one molecule of ATP . During e vector  50]. In. InA. wo1.7-fold  30]..A. woodiC. ljungdahlii, C. ragsdalei, and C. autoethanogenum strains can produce ethanol as a native product, of which the Lanzatech process is a representative example of the successful commercial production of bio-ethanol from C1 gases .,91.C. lj ethanol  49,92].,92.C. ljC. ljungdahlii, C. ragsdalei, and C. autoethanogenum genomes encode genes for metabolic pathways that produce 2,3-BDO from pyruvate through acetoin. In particular, C. autoethanogenum could produce about 0.2 g/L of 2,3-BDO under batch culture conditions from CO steel mill gas . Th. ThC. ljng NAPDH . To prod-butanol  93]..C. ljungC. autoethanogenum and C. ljungdahlii, respectively, and isopentenyl pyrophosphate isomerase (encoded by idi) and isoprene synthase (encoded by ispS) were additionally expressed ..RalstoniC. acetobutylicum, C. ljungdahlii, or C. autoethanogenum. Since these promoter/UTR parts cannot quantitatively control the level of transcription or translation of target genes, the production of biochemicals using acetogens has limitations in the expansion from the lab scale to the commercial scale. Therefore, it is necessary to develop a regulatory system that can optimize the transcription or translation of each gene consisting of artificial pathways to efficiently synthesize biochemicals from C1 gases using acetogens.So far, we have demonstrated the industrial potential of acetogens as biocatalysts through examples of various biochemicals produced from C1 gases. However, the low productivity of non-native biochemicals is not suitable for commercial scale production despite the production potentials . To imprIn order to apply synthetic biology in the development of industrial platform microbes, synthetic parts , which are the most basic synthetic biology units, must be developed first. To develop these parts, multi-omics information obtained from the genome, transcriptome, translatome, interactome, proteome, and metabolome of the acetogens of interest are required . Based oMany synthetic biology approaches for strain engineering are being attempted in acetogens, but research efforts are still limited compared to other industrial microbes. First, this is due to the limited understanding of the C1 fixation pathways of the acetogens and the genetic regulation system of many genes involved in their carbon and energy metabolisms. Second, there is a lack of efficient genetic manipulation tools. Third, high-throughput screening is not possible due to the lack of an efficient reporter system such as fluorescent proteins. Therefore, if the three limitations of acetogens are solved, more synthetic biology approaches will become applicable, and they can overcome the low productivity of using acetogens to convert C1 gases to biochemicals.A. woodii and E. limosum as well as Clostridium species such as C. ljungdahlii, C. autoethanogenum, and C. drakei (Table. 4). Through transcriptome analysis, it was possible to reveal the level of transcriptional expression of genes related to the WL pathway and the energy conservation system in acetogens under autotrophic conditions [C. ljungdahlii or E. limosum revealed that translational regulation of genes in the carbonyl-branch or energy conservation system is critical to their autotrophic growth [C. ljungdahlii, C. autoethanogenum, and C. drakei (With the rapid development of next-generation sequencing (NGS) technology, various omics data have been produced, including the transcriptome in nditions ,109,110.c growth ,112. Rec. drakei . GEMs cothl) or phosphotransacetylase/acetate kinase operon promoter (Ppta-ack), the transcription level of the target gene in the plasmid was maintained constitutively high [C. ljungdahlii and C. autoethanogenum, inducing the transcriptional expression level along with the lactose concentration [Genetic promoter/UTR parts, which are a basic synthetic biology unit, allow the balance between growth and chemical production by quantitatively controlling the transcription and translation levels of target genes or pathways. However, the development of these genetic parts has been limited to acetogens. Using a robust native promoter such as thiolase A  of genes related to acetogenesis have strongly conserved “TTGACA”, “TATAAT’”, and “GGAGG” motifs [In addition, information on the native promoter and 5’-UTR was revealed through differential RNA-seq in ” motifs ,110. ThiC. ljungdahlii. The promoter (Pthl) of the acetyl-CoA acetyltransferase gene in the C. acetobutylicum genome was used as a backbone and a promoter/UTR library was constructed by randomization of sequences between −35 (TTG) and −10 (TATAAT) motifs, RBS (AGGAGG), and the start codon (ATG). The promoter library was connected upstream of the CatP-LacZ dual reporter, introduced into C. acetobutylicum, and screened according to the concentrations of chloramphenicol and LacZ to find synthetic promoter/UTR parts with 10-fold higher transcriptional activity than native Pthl. After applying the genetic parts to C. ljungdahlii, it succeeded in increasing the productivity of isopropanol 19-fold compared to wild-type Pthl [To develop synthetic parts, high-throughput screening methods using efficient reporter systems are required A. Howeveype Pthl . Indeed,E. coli, biosensors that can recognize specific chemicals or environmental stimuli have been developed, and many of these have been employed for high throughput screening of enzymes or mutant libraries according to their activities using flow cytometry [In model microbes, including ytometry ,134,135.E. coli to suppress competing genes that could divert the carbon flux towards the production of 1,4-Butanediol. A similar approach has been shown in C. ljungdahlii, where they redirected acetyl-CoA flux from acetate to increase the production of 3-hydroxybutyric acid (3HB) [adhE1) was repressed with dCas12a instead of dCas9, and the redistributed carbon flux improved the titer of butyric acid [Repurposing the CRISPR-Cas system for transcription regulation is a genetic tool to knockdown or activate gene expression . CRISPR id (3HB) . Likewisric acid .E. limosum and showed that the number of cells harboring sgRNA, which target essential genes and hence decrease cell viability, was reduced from the initial population [CRISPRi and CRISPRa techniques can be scaled up to the genome level. By introducing pooled gRNA libraries that can target nearly all genes, genome-wide CRISPR screening has enabled high-throughput functional genomics studies in bacteria ,148,149 pulation . This stE. coli. In addition, Cress et al. [E. coli. Employing these strategies in acetogens will allow the generation of better microbial cell factories that can control their regulatory and metabolic pathways in response to changing environments such as pH fluctuations and substrate availability.Recently, CRISPRi-based synthetic genetic circuits have been designed and constructed to control cellular behaviors to fulfill the goals of synthetic biology D. Nielses et al.  construcThe use of acetogens as biocatalysts in refining C1 gases needs to overcome several limitations in order to adapt them to industrial-scale applications. These limitations include low growth rates, toxicity by C1 gases and other substrates, and low productivity. The synthetic biology approach is expected to be a very powerful way to overcome these limitations. Along with the development of diverse bioparts and efficient genetic manipulation tools to build the targeted platform strains, the engineered acetogens should be further optimized under the conditions of interest. To this end, the adaptive evolution method is straightforward and robust. Mutations are naturally generated during adaptive evolution when a microbial strain is continuously exposed to a specific stress condition. If a genetic mutation effectively relieves a specific stress, the strain with the mutation shows fast growth under the specific stress conditions. There are some examples in which tolerance and growth rate of acetogen have been improved through multiple passages under growth conditions such as methanol or CO ,152. If Currently, there are not many examples of synthetic biology approaches for acetogens. However, due to the recent increase in data availability for systems biology approaches and efficient genetic manipulation tools such as CRISPR-Cas being developed, many obstacles to the synthetic biology approach are being resolved, and therefore these tools are now rapidly being adopted to discover gene modifications for strain optimization. Furthermore, if optimized acetogen genomes capable of highly efficient biochemical conversion of C1 gases are built, it is expected that the currently low productivity will be overcome, and industrial applications will increase.


Cancer cell metabolism is dependent on cell-intrinsic factors, such as genetics, and cell-extrinsic factors, such nutrient availability. In this context, understanding how these two aspects interact and how diet influences cellular metabolism is important for developing personalized treatment. In order to achieve this goal, genome-scale metabolic models (GEMs) are used; however, genetics and nutrient availability are rarely considered together. Here, we propose integrated metabolic profiling, a framework that allows enriching GEMs with metabolic gene expression data and information about nutrients. First, the RNA-seq is converted into Reaction Activity Score (RAS) to further scale reaction bounds. Second, nutrient availability is converted to Maximal Uptake Rate (MUR) to modify exchange reactions in a GEM. We applied our framework to the human osteosarcoma cell line (U2OS). Osteosarcoma is a common and primary malignant form of bone cancer with poor prognosis, and, as indicated in our study, a glutamine-dependent type of cancer. A widely recognized hallmark of cancer is reprogramming of cellular metabolism, which aims at promoting rapid cell proliferation and long-term maintenance, thus facilitating the uptake and conversion of nutrients into biomass ,3,4,5,6.Understanding the regulation of the metabolic pathways by genetic factors provides new insights into the required metabolic aspect of tumorigenesis and can also potentially provide therapeutic metabolic targets. Cancer cell metabolism similar to any other cell in living organisms is also determined by cell-extrinsic factors, including interactions with the environment ,10. TherHere, we aim to contribute to the recently growing understanding of how certain cancer types respond to various nutrient availability and how the access to and utilization of nutrients by cancer cells affects the rate of proliferation. In this study, we showcase our approach on the human osteosarcoma cell line (U2OS); however, it could be further applied to other carcinoma and sarcoma cell lines. We provide a tool based on a genome-scale metabolic model (GEM) ,15,16,17in silico modeling. Therefore, we state the following research questions:Do nutrient availability changes affect metabolism of the U2OS cells?Do changes in glucose and/or glutamine availability impact the growth rate and metabolic requirement of U2OS cells?Does simultaneous integration of the gene expression and nutrient availability data into a GEM provide accurate prediction of cell growth and metabolite consumption/production?In this paper, we focused on the human osteosarcoma cell line (U2OS). Osteosarcoma is a common and primary malignant form of bone cancer. It generally affects children and adolescents, where it represents the eighth most common form of childhood cancer. With its poor prognosis, it is the second most important cause of death related to cancer in both children and adolescents . This paTo achieve our goals we performed integrated metabolic profiling of the U2OS cell line that includes experiments and genome scale modeling. By gathering transcriptomic data from these cells in varying microenvironments, the effect of nutrient availability on cancer cell metabolism was studied. We focused on the questions whether this cell line is glucose-dependent, and whether U2OS cells require glutamine in order to grow and survive. Finally, we addressed the question whether diet  affects the transcription level of the cells and can alter the expression of the metabolic profile. As a modeling platform we used the genome-scale metabolic models ,15,16,17In our study, we utilized the most comprehensive human metabolic network model to date, Recon3D , and a nWe provide an integrated GEM-based metabolic profiling framework for identification of the nutrient requirements that necessitates using RNA-seq data specific for each condition.We prove that genome-scale models like RECON3D provide a great platform for integration of transcript and nutrient availability data in order to create specific models for studying cell metabolism.We prove that translation of the relative importance of gene expression and nutrient availability data into the fluxes based on observed experimental feature(s) is a reliable method to build specific models.We identified the impact of glucose and glutamine availability on the metabolism and proliferation rate of the U2OS cells.We identified that the U2OS cell line is a glutamine-dependent cancer type.The contribution of this paper is five fold:To study the influence of nutrient availability on the growth rate and the metabolic behavior, we used the U2OS cell line and seven Dulbecco’s Modified Eagle Medium (DMEM) derived media containing different concentrations of glucose and glutamine . The U2OS cell line consists of epithelial adherent cells and exhibits a fast growth rate. We were interested in the growth, nutrient requirements, and expression of the metabolic genes of the U2OS cells in optimal and not-optimal growing conditions; thus, the seven conditions were chosen see .We cultured them altering the metabolic microenvironment until the growth rate achieved a steady state. Further, we performed the proliferation assays and isolated mRNA from the cells and metabolites from the medium. We performed RNA-seq analysis using the NextSeq 550 Sequencing System (Illumina) and generated transcriptomic data after proper quality control and filtration. The RNA sequencing data were then processed see  and usedA central challenge in understanding and treating cancer comes from its multi-scale nature. To gain insight into the impact of the access to nutrients on metabolism and the rate of biomass synthesis, we integrated both transcriptomic and nutrient availability data into the most extensive genome-scale model of human metabolism RECON3D  and studi is equal to i is reversible, its forward flux is bounded by i is irreversible, the flux bound in the impossible direction remains zero, and the flux bound in the possible direction is set to j in the GEM as the absolute value of a difference in concentration of the substance In the GENSI framework the flux bounds of the reactions that are associated with genes are set proportionally to RAS scores that are computed based on RNA-seq data. In our work, the flux bound Following the GENSI framework, we found the value of the factor In the absence of glutamine, the cells were unable to survive and started dying after hours of culturing e. Since To examine whether only gene expression level has impact on the biomass synthesis, we formulated five RECON3D-based models via constraining reactions associated with genes as a function of RAS without changing exchange reactions (GEM-RAS). We did observe the impact of mapping the gene expression; however, there was no value of the factor rrelated a. This rNext, we decided to test whether integration of only nutrient availability changes the prediction of the fluxes (GEM-MUR). We built seven GEM-MUR models via constraining exchange reactions by MUR calculated for each condition. We did not change the bounds of reactions associated with genes. As we expected, the results again did not meet the experimental observations, leading even to an experimentally unobserved situation of a high growth rate for Glc10Gln0 and Glc2Gln0 b. The prTo test the prediction of the GEM-RAS-MUR models we performed FVA analysis for allowed exchange reactions while supporting biomass production rate. We further explored the range of uptake (if negative) or secretion (if positive) fluxes of glucose, glutamine and lactate by plotting the range of fluxes for corresponding exchange reactions .Our main observation was that glutamine uptake was essential for all conditions. The minimum flux of the glutamine exchange reaction was always less than zero c; howeveIn silico predictions were then verified experimentally by measurement of the glucose, glutamine and lactate consumption. It turned out that all of the available glutamine was consumed by cells in all cases. While the excess glucose in the conditions Glc10Gln4 and Glc25Gln4 was not utilized by cells. Interestingly, the production of lactate was similar for Glc10Gln4 and Glc25Gln4, while cells cultured in Glc25Gln4 consumed two times more glucose than cells growing in Glc10Gln4 did.Cancer is one of the world’s most serious health problems. Recently, cancer metabolism and nutrition have gradually become important topics in theoretical and clinical research. In contrast to traditional chemotherapy, where the goal is to kill cancer cells mainly via blocking the transcription, the nutrition-based approach assumes that cancer cell metabolism is influenced by metabolic constraints imposed by a diet. Perturbations to dietary compositions contribute to changes in plasma metabolite levels that, in turn, influence metabolite levels in the tumor microenvironment and thereby alter cancer cell metabolism and therapeutic responses. Nutrient levels in the tumor/cancer cell microenvironment can have profound impacts on cell metabolism, growth and drug sensitivity ,10,11,12Here, we follow this line of research and propose a framework for exploring the impact of the diet on cancer cell metabolism and progression using the cancer cell line and medium compositions as an experimental setup. We performed integrated metabolic profiling through enriching genome-scale modeling with metabolic gene expression and nutrient availability data. In our study, we focused on the metabolism of bone osteosarcoma, which is one of the most common primary malignant bone tumors . As a GEHere, we proposed a general framework of the integrated metabolic profiling that incorporates information about gene expression and nutrient availability to a GEM that could be further used to accurately predict cell growth rates and nutrient/metabolite consumption/production. For this purpose, we utilized the GENSI framework to scale reaction bounds using RAS, and then MUR to introduce information about nutrient availability to external reactions. In our experiments, we considered seven different conditions in which various amounts of glucose and glutamine (mM) were available, see Next, we showed that our integrated metabolic profiling allowed us to obtain a GEM that provides highly accurate predictions. For this purpose, we first verified whether a combination of RAS and MUR (GEM-RAS-MUR) is crucial to match model predictions with observed quantities and compared them against a GEM with information about RAS (GEM-RAS) and a GEM with MUR (GEM-MUR). The results in Lastly, we applied flux variability analysis to the GEM obtained within the proposed framework (GEM-RAS-MUR) to compare uptake/secretion fluxes of selected metabolites with measured quantities. In in silico models like GEMs. Lastly, our study also highlights the huge potential of GEMs as a platform to incorporate big data like RNA-seq.In conclusion, the results obtained in our experiments indicate that the GEM-RAS-MUR model for the U2OS cell line obtained within our framework could be used to accurately predict cell growth and metabolite consumption/production. This answers our last research question positively and opens new research perspectives for applying our framework to different types of cancer, but also other illnesses. The combination of transcriptomic data together with information about nutrients seems to be essential for developing accurate U2OS (human osteosarcoma) cells were cultured in Dulbecco’s Modified Eagle Medium , supplemented with lutamine . Six of The total number of cells in the consequent supernatant was determined by hemocytometer counting. Mean growth rate was determined by counting cells in four non-overlapping sets of sixteen corner squares selected at random.Samples from each medium were collected at 0 and 48 h, repeated three times for a total of three independent experiments for every medium composition. Metabolites were isolated using the protocol described in Sapcariu et al.  with minhttps://www.sigmaaldrich.com/technical-documents/protocols/biology/tri-reagent.html), after which all samples were treated with DNase using the TURBO DNA-free Kit from Invitrogen. Nanodrop was used for determining RNA concentrations when needed. With the Agilent 2100 Bioanalyzer System the quality of the RNA was checked, as well as the RNA Integrity Number (RIN), using RNA Pico Chips. rRNA depletion and library preparation for sequencing was completed with the NEBNext protocol (‘Protocol for use with NEBNext rRNA Depletion Kit (Human/Mouse/Rat) (NEB #E6310) and NEBNext Ultra II Directional RNA Library Prep Kit for Illumina ’) from New England BioLabs. Using a 2200 TapeStation System with Agilent D1000 ScreenTapes , and the size distribution of the libraries with indexed adapters was assessed. Quantification of the libraries was performed on a QuantStudio 3 Real-Time PCR System , which was done using the NEBNext Library Quant Kit for Illumina  according to the manufacturer’s directions. On a NextSeq 550 Sequencing System (Illumina), the libraries were clustered and sequenced (75 bp) utilizing a NextSeq 500/550 High Output Kit v2.5 (75 Cycles) (Illumina).A total of 5,000,000 cells from each medium condition were taken for RNA analysis, also repeated three times for a total of three independent experiments for every cell condition. The RNA extraction was done using the TRI Reagent Protocol from Sigma Aldrich (https://sourceforge.net/projects/bio-bwa/). The outputs of this step were files in .sam format.Sequences were mapped against the human reference genome using Burrows-Wheeler Aligner .The .sam files were transformed to sorted bam files using Samtools (https://anaconda.org/bioconda/subread).Gene occurrences were calculated using featuresCounts from the Subread package (Homo sapiens (human) genome assembly GRCh38 (hg38) from Genome Reference Consortium Build 38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/) was used as the reference genome.In all cases, the RNA-seq data were available in .fastq file format. There were three measurements per condition. The RNA-seq data were processed according to the following pipeline:Mapping transcriptomics data to the fluxes with Recon3D included two steps: (1) conversion of RNA-seq into RAS, (2) RAS to fluxes of reactions with gene-protein-reaction association rules.RNA-seq data were converted to RAS, for each condition and each reaction, based on protocol outlined by Graudenzi, A. et al.  accordini having GPR association rules as follows:v is the vector of fluxes through all reactions having a gene rule, i.In the next step, j based on NA data for each condition. In the next step i as follows:Mapping NA to fluxes with Recon3D includes two steps: (1) conversion of NA to Maximum Uptake Rate (MUR), (2) conversion of MUR to fluxes of exchange reactions. We define a j in a GEM as the absolute value of the difference in the concentration of the substrate In our case, MUR is defined for the exchange reaction S is the stoichiometry matrix indicating how many molecules of each metabolite are produced or consumed in each reaction, v is the vector of fluxes through all reactions including exchange reactions with the environment of the system, c is a vector of weights generating the linear combination of fluxes that constitutes the objective function Z.In the simulations of maximal biomass flux we applied the computational technique of Flux Balance Analysis (FBA)  to the hZ), v is the vector of fluxes through all reactions, and In the simulations of maximal and minimal fluxes for defined exchange reactions we applied the computational technique of Flux Variability Analysis (FVA) . FVA is https://github.com/e-weglarz-tomczak/GEM-RAS-MUR.All related data sources such as gene featureCounts, RAS scores, and GEM models can be found at