Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and COStudies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae  Microalgae are unicellular photosynthetic organisms. They are capable of capturing gases such as COo oxygen . Furthere source ,4.A large number of strains of microalgae have been studied, finding several metabolic pathways involved in the synthesis of many secondary metabolites. However, the production rate of these metabolites varies from one species to another or even in the same species, according to different environmental and metabolic conditions. The production of the secondary metabolites depends on many factors, such as the type of microalgae and the culture conditions, temperature, pH, lighting, and nutrient sources .So far, metabolic models built from genomic sequences allow a quantitative view of the transport and metabolism of compounds within a target organism. In addition, these metabolic models have long been used to generate optimized design strategies for an improved production process .v is the flux vector across the reactions. The stoichiometric matrix i involved in reaction j [Most metabolic models of microalgae focus on studying a single objective function, e.g., biomass. For the particular case of metabolic networks in steady-state, Flux Balance Analysis (or FBA) is the most commonly used optimization method for their study ,8. Equataction j , and the by Sv=0 .(1)FBAmSynechocystis sp. PCC 6803 [Synechococcus sp. PCC 7002 [Cyanothece sp. ATCC 51142 [C. reinhardtii [Anabaena sp. UTEX 2576 [Chlorella vulgaris UTEX 395 [Chlorella variabilis [Chlorella protothecoides [Arabidopsis thaliana [The solution space for FBA is defined by Equation , and witPCC 6803 ,11, SynePCC 7002 ,13, Cyannhardtii , AnabaenTEX 2576 , Chlorelriabilis , Chlorelhecoides , and Arathaliana  to estimHowever, despite the widespread use of FBA to predict fluxes in large-scale networks, it is not always accurate in predicting fluxes in vivo . Moreoveinterest ,22,23. Iinterest . There, interest  extend FMulti-objective optimization has been exploited in a wide variety of fields in science and engineering ,26. MOFBThe main goal for multi-objective optimization is a good approximation of the Pareto frontier, cf. ; Figure An overall view of the previous analysis indicates that the motivation for using multi-objective optimization in FBA lies in improving the prediction capability of FBA. However, the revised approaches do not adequately exploit the versatility of metaheuristics to approximate the Pareto frontier under a moderate consumption of computational resources. In other words, using a metaheuristic can better approximate the Pareto frontier, and provide a greater diversity of solutions than the previous approaches ,26.OptKnock  as fitnChlamydomonas reinhardtii [The conducted experimental design demonstrates the validity against a glyclolysis module of a reduced metabolic network for microalgae nhardtii . Moreovep-values were The solution quality was statistically compared between NSGAII and the distinct solutions reported by FBA for each configuration, and each objective took the lead. The null hypothesis The results in A closer numerical look at the differences between NSGAII and FBA under configuration Some additional insights arise from the previous results. Let us begin with the variety of configurations used in the comparison; this demonstrates the versatility of NSGAII to adapt to different circumstances and its capacity to improve the analysis of the metabolic network given the larger number of solutions produced for each of them.After that, evolutionary approaches require fewer resources than FBA when dealing with multiple objectives; for example, it has the advantage of spending less time and memory. Approaches such as NSGAII allow a greater power of choice in the decision-making process also due to the variety and number of solutions, and the possibility of an easier recognition of the most important fluxes in a network and their influence and impact rather than not having a methodology.Finally, by analyzing sets of several tens or hundreds of solutions simultaneously instead of just one through the classical approach, it is possible to have a better perspective of what is happening in cell metabolism.Chlamydomonas reinhardtii metabolic network  is higher than the number of metabolites (denoted by m), there is a large number of combinations of reaction fluxes that satisfy such systems. This section presents the proposed multi-objective FBA and a novel evolutionary approach that solves it, efficiently producing a proper approximation of the Pareto frontier with a quite diverse set of Pareto solutions that can improve decision-making in metabolic engineering.Traditionally, the optimization of metabolic networks considers a steady-state approximation in which the metabolite concentrations are assumed to be constant. Since in the majority of the real-world metabolic networks the number of reactions (denoted by The novel features included in this research are: (1) a novel encoding scheme for an FBA solution; (2) an original fitness evaluation based on classic FBA that guides the approximation of the Pareto front on MOFBA; and (3) a novel adaptation of the NSGAII framework to solve the particular MOFBA defined under the previous characteristics, so that feasible solutions can be achieved.S, the fluxes vector v, the steady-state condition Equation  shows thEquation  also conMOFBA, as defined in Equation , cannot This work proposes the use of a novel ensemble encoding the solution of MOFBA (as defined in Equation ) and itsw is of size w applied to a case with w will results in the new bounds i uses its initial values for Proposed Encoding Scheme w, and taking as leading bioproduct w.Fitness Evaluation Function (or FEF). Given that the information required on the bio-products is associated with specific reactions, the aptitude of a solution obtained by NSGAII on MOFBA is evaluated considering their fluxes values. Hence, the criteria or objective functions to be optimized will be the fluxes of the reactions corresponding to the chosen bioproducts of interest in Genetic Operators. These operators create new solutions by dynamically and randomly varying the values of decision variables on existing solutions. Their selection was due to their success in solving problems involving real-valued decision variables . The choConstraint Handling Scheme. The ensemble for NSGAII considers a feasibility constraint. This constraint is violated whenever FBA in the fitness function reports unfeasibility. The latter arises because the bounds defined by an encoded solution might cause no feasible solution exists. This work uses the constraint handling method proposed in  to overcInitialization method. A randomly generated initial population was considered as the input for NSGAII.v in FBA, will dynamically evolve in the algorithm, and those with better aptitude  are kept for future generations. The final set of solutions provided by NSGAII represents a wider, more spread, and more promising combination of the reaction fluxes values that a decision maker can take as an advantage for their research.In general, the novel idea behind the previous ensemble is that NSGAII exerts selective pressure toward the Pareto frontier using the genetic operators, the ranking based on non-domination, the diversity control established by the crowding distance, and an adequate constraint handling strategy. The solutions, which indirectly define the flux values for vector The following section details the validation process for this proposed ensemble of NSGAII.Chlamydomonas reinhardtii. This work analyzes the FBA of an essential module of the microalgae The experimental method evaluates the performance of NSGAII and compares it against the classical FBA. The hypothesis under consideration was that the decision-making on metabolic analysis can be improved using the evolutionary approach. The considered indicators that assess such improvement were the quality of the solutions, their diversity Besides the set of bioproducts of interest, the configuration Ideal Point. Each configuration defines the ideal point and the best flux value resulting from FBA using each bioproduct of interest. For example, with the configuration The quality of a solution, denoted The diversity of solutions, or PolynomialMutation, a mutation probability of SBXCrossover a crossover probability of The development of the proposed NSGAII for MOFBA was done by combining the FBA implementation from COBRAPY , and theChlamydomonas reinhardtii. The results obtained were compared with FBA analysis, achieving a similarity between the maximization of FBA when only a single objective function is maximized, but it also shows a broader perspective of the study of metabolic fluxes when there are multiple objective functions, NSGAII provide not only solutions closer to the ideal point in quality but also a better diversity and spread of them. Moreover, as far as we know, this is the first approach using evolutionary algorithms for metabolic analysis.This study carried out the maximization of multi-objective functions of the production of proteins, carbohydrates, and The use of the NSGAII algorithm to facilitate the understanding of the behavior of the metabolism when using multi-objective analysis must take into consideration that a possible decision-maker in the case study is interested in a broader set of combinations of fluxes in the reaction, by maximizing or minimizing different objective functions simultaneously, to explore a wide set of solutions of which those that may be more convenient for the investigation of a species are more easy to extract; in addition, we obtained a broader perspective of the study of metabolic fluxes by knowing how the different settings assigned.For future work, the NSGAII evolutionary algorithm will be applied to a much larger microalgae metabolic network, using different configurations by maximizing different objective functions and comparing the results with FBA, observing the different solutions according to the requirements, and taking the most suitable solutions according to the needs of the model. In addition, the use of the NSGAII algorithm compensates production between the fluxes of the metabolites of interest and the fluxes of biomass production.


Tisochrysis lutea with different possible organic carbon excretion mechanisms. These models—based on the DRUM  methodology—are calibrated with an experiment of nitrogen starvation under day/night cycles, and then validated with nitrogen-limited chemostat culture under continuous light. We show that models including exopolysaccharide excretion offer a better prediction capability. It also gives an alternative mechanistic interpretation to the Droop model for nitrogen limitation, which can be understood as an accumulation of carbon storage during nitrogen stress, rather than the common belief of a nitrogen pool driving growth. Excretion of organic carbon limits its accumulation, which leads to a maximal C/N ratio (corresponding to the minimum Droop N/C quota). Although others phenomena—including metabolic regulations and dissipation of energy—are possibly at stake, excretion appears as a key component in our metabolic model, that we propose to include in the Droop model.We propose metabolic models for the haptophyte microalgae  Microalgae are unicellular eukaryotic microorganisms, playing a key role in the ocean. How nutrient stress affects microalgal growth is a central issue, particularly in the context of climate change  was performed on the whole set of data, including both nitrogen replete and nitrogen deplete conditions. A better fit was found, with lower error  and lower standard deviations on parameters  exopThe four modified models include excretion of a metabolite , at different loci of the metabolism . ParametThe models for each excretion hypothesis correctly fit the experimental data for both nitrogen replete and nitrogen starvation conditions , showingT. lutea. The four models predict different metabolic modes depending on light and nitrogen availability. What differs is the glycolysis direction, the carbon storage direction (consumption or accumulation of lipids and carbohydrates) and the relative distribution between carbon storage sources, photosynthesis and excretion. First, as expected, the excretion rates dictated by the intracellular metabolite concentrations increase during nitrogen starvation (see The clear fit improvement when considering excretions (compared to the w/oEx* model) gives some insights of the impact of nitrogen starvation on the metabolism of tion see . AlthougThe intracellular fluxes also change, depending on the locus of excretion. Metabolic fluxes show that, during the day, the four models present different allocations of the assimilated inorganic carbon between functional biomass, carbon storage and excretion. During the night, excretion is almost null in nitrogen replete condition, while it continues—at a lower level—during nitrogen starvation . Night eDuring nitrogen starvation, the flux maps differ greatly from the nitrogen replete conditions. Except the Calvin cycle whose fluxes  remain almost unchanged, all the other fluxes are much lower and, as expected, some parts of the metabolic network are not activated see .Our reduced model was developed with a focus on C and N fluxes, without constraining ATP and NADPH balance which should be a model outcome. Some subnetworks such as MR6 are perfectly balanced (so ATP and NADPH do not appear in the macroscopic reaction), but others are not, meaning that should exchange cofactors with other subnetworks. To validate the energy balance in the model, we computed the production and consumption rates of ATP and NADPH from the reactions of the whole metabolic network. It results that for w/oEx*,Ex CARB and Ex G6P, the ATP and NADPH are globally well balanced see : the relLipid and carbohydrate quotas reach an almost periodic regime at day 3 . These v−1 and Q0 = 0.04 gN.gC−1), although the saturation is less pronounced with the latter. In addition, the models also predict an almost linear decreasing relationship between the specific growth rate and carbon storage  to the specific growth rate . For the storage . For allConsidering at the same time calibration and validation steps, the models considering EPS excretion (ExCARB and ExG6P) give the best results. The ExPEP model presents a lower performance in validation, while the NADPH was not balanced with the ExGAP model in the day-night simulation. Additionally, the ExPEP and ExGAP models must be discarded due to their unlikely high rate of excretion see .Prochlorococcus of Ofaim et al.  have been tested ed cases , 7, it iT. lutea, Claquin et al. , might take place to dissipate the excess of incoming energy.While excretion clearly improves model predictions and is likely to play a central role, other mechanisms – not considered herein – are known to mitigate nutrient stress. The most important one is probably the dissipation of light energy. The synthesis of photoprotective pigments, such as carotenoids, can help to dissipate the excessive light energy received by the cell during nitrogen starvation  on phytoplankton specific growth rate μ(q), see Equation (1).Our results now provide new insights about the Droop function, which is largely used to describe phytoplankton growth Droop, , 1983. TDroop model is generally justified by the necessity for the cell to have stored enough limiting element to grow see . Note thq:There is a growing body of literature showing that excretion increases with nutrient limitation . Given the linear trend observed between the excretion and the growth rate in q = Q0. This choice of kinetics becomes more straightforward when the quota is flipped: the excretion rate η actually increases linearly with the C/N ratio, i.e., with carbon accumulation. A comparison of this model with the output of the ExCARB model is given in where The Net Primary Production can now be estimated by summing biomass growth and excretion:Additional experiments at different levels of nutrient limitation are now necessary to further validate this coarse grain model. It will eventually contribute to propose a more accurate picture of the carbon fluxes at the global scale.In conclusion the set of considered metabolic models gives a mechanistic view on the Droop model during nitrogen stress, and explains why this simple model is very efficient, as demonstrated by Mairet et al. . SurprisT. lutea  was grown in duplicate chemostat in 5 L cylindrical vessels at constant temperature (22°C) and pH . For model calibration  and simultaneously added in the incoming media. Since growth is hindered during nitrogen starvation, the dilution rate was decreased accordingly in order to avoid washout. Nitrate, particulate carbon and nitrogen, chlorophyll, total carbohydrates and neutral lipid concentrations were measured throughout the experiment  were carried out. Different levels of nitrogen stress were obtained through a succession of dilution rate changes. Steady-states values of nitrogen quota and carbon storage quota were used for model validation.For model validation , accumulating metabolites A , functional biomass B and excreted molecule P (in mol/L):Sin the incoming substrate concentration and D the dilution rate. The composition of functional biomass is determined from experimental data , is computed using a mass balance:with CA and CB correspond to the number of carbon atoms per molecule of A and B and NA and NB correspond to the number of nitrogen atoms per molecule of A and B. Metabolic fluxes of the cell can be computed using the elementary flux mode matrices that were obtained during the reduction of the 162 reactions of metabolic network to macroscopic reactions. That is, the flux of reaction j is given by:where aij is the contribution of reaction j in the macro-reaction i, given by the elementary flux mode matrices. The consumption and production of ATP and NADPH are computed from these fluxes to check a posteriori their balance, e.g., for ATP:where K is the stoichiometric matrix of the core network. For further details on this model construction, the reader is referred to Baroukh et al. . The simulation outputs as shown on kMR1 to account for the quantum limit of photosynthesis. Using an absorption coefficient of 16 m2/g Chl , as already done in Baroukh et al. , as follows:yi(t)±1.96σyi(t).The 95% confidence intervals on model outputs are finally given by To compare models with different parameter numbers, the corrected Akaike Information Criterion (AICc) was computed as follows  was fitted to the experimental data using the least-squares routine leastsq in Scilab.Steady-state chemostat cultures under nitrogen limitation at various dilution rates  and by the FMJH Program PGMO (and from the support to this program from EDF-THALES-ORANGE). Work of CB is supported by the French Laboratory of Excellence project TULIP .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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Motivation: The complement pathway plays a critical role in innate immune defense against infections. Dysregulation between activation and regulation of the complement pathway is widely known to contribute to several diseases. Nevertheless, very few drugs that target complement proteins have made it to the final regulatory approval because of factors such as high concentrations and dosing requirements for complement proteins and serious side effects from complement inhibition.Methods: A quantitative systems pharmacology (QSP) model of the complement pathway has been developed to evaluate potential drug targets to inhibit complement activation in autoimmune diseases. The model describes complement activation via the alternative and terminal pathways as well as the dynamics of several regulatory proteins. The QSP model has been used to evaluate the effect of inhibiting complement targets on reducing pathway activation caused by deficiency in factor H and CD59. The model also informed the feasibility of developing small-molecule or large-molecule antibody drugs by predicting the drug dosing and affinity requirements for potential complement targets.Results: Inhibition of several complement proteins was predicted to lead to a significant reduction in complement activation and cell lysis. The complement proteins that are present in very high concentrations or have high turnover rates  were predicted to be challenging to engage with feasible doses of large-molecule antibody compounds (≤20 mg/kg). Alternatively, complement fragments that have a short half-life  were predicted to be challenging or infeasible to engage with small-molecule compounds because of high drug affinity requirements (>1 nM) for the inhibition of downstream processes. The drug affinity requirements for disease severity reduction were predicted to differ more than one to two orders of magnitude than affinities needed for the conventional 90% target engagement (TE) for several proteins. Thus, the QSP model analyses indicate the importance for accounting for TE requirements for achieving reduction in disease severity endpoints during the lead optimization stage. The complement system forms an important component of innate immunity by providing the first line of defense against infections. It is composed of several proteins produced mainly by the liver or expressed on the surface of the cells. The complement proteins participate in a cascade that forms large protein complexes on the cell surface, opsonizing and killing the target cell. The tight regulation of the pathway through various regulatory proteins and cell surface receptors protects the host cells against complement-mediated cell death, and there exists a fine balance between complement activation and regulation. However, dysregulation of the complement pathway due to protein deficiencies or genetic mutations can unfavorably tip the balance  toward oThe complement system is widely known to contribute to the pathology of several diseases and is an active area of drug development. However, despite a wealth of knowledge on the complement cascade and its components, very few drugs have made it to the final regulatory approval . The prein silico modeling approach that combines the knowledge of disease processes with drug mechanisms to evaluate targets and drug candidates for modulating disease severity and biological pathways  is an pathways . It is npathways . To takein vitro to in vivo activation of the complement system in humans. The first attempts at mathematical descriptions of the pathway included simplified descriptions of the complex dynamics in key aspects of complement activation such as the amplification loop , clusterin, vitronectin, CD59, complement receptor type 1 (CR1), CD55 or decay-accelerating factor (DAF), factor H (FH), and factor I (FI). For refining the model dynamics and estimating the kinetic parameters, several published and in-house in vitro datasets were used for validation of the dynamics of alternative and terminal pathways.The computational complement pathway model or simply the “complement model” or “QSP model” developed in this study comprehensively describes the dynamics of complement activation through the alternative and terminal pathways. The tick-over and amplification of the alternative pathway have been described in plasma and on the surface of erythrocytes, respectively. An via reduction in FH and CD59, reflect the mechanistic basis of complement activation in diseases such as atypical hemolytic uremic syndrome (aHUS) and paroxysmal nocturnal hemoglobinuria (PNH). These fundamental representations of simulated disease states have been used for evaluating the effect of potential complement targets on reducing disease severity via downstream biomarkers  and the lysis of erythrocytes.The model simulations, A number of drug candidates are currently in development across the pharmaceutical industry  for targvice versa, an assessment of both the dose levels and drug affinities for target engagement has been done. These evaluations have been used to guide the drug development for complement therapeutics during early target validation and lead optimization phases to support assessments such as modality selection, dosing tractability, and effects of target engagement on complement pathway endpoints.There are also short-lived intermediate proteins in the complement pathway  formed due to proteolytic cleavage and conformational changes. These cleavage products have not been fully explored in the pharmaceutical industry but represent promising drug targets due to their low concentrations and the potential of engaging them with lower drug doses. Therefore, an assessment of the dosing tractability was performed for these novel targets as well. Additionally, since higher doses of drug candidates may be compensated by lower, that is, more potent drug affinities to attain the required level of target engagement and in vitro assays that were used for validating the QSP model dynamics. in vitro data, the model has been further validated by predicting the effect of deficiency in regulatory proteins on the pathway activation in vivo in humans which have been observed in several complement-driven disease pathologies. Furthermore, an analysis of the effect of inhibiting potential drug targets on reducing complement activation is shown to identify promising targets for drug development. In addition to reducing the disease severity, the tractability of the targets due to their drug dosing and affinity requirements has been analyzed to provide a comprehensive evaluation of developing drugs targeting complement proteins and intermediates. In via the alternative and terminal pathways. The model consists of 311 state variables and 139 kinetic parameters. The processes included in the computational model are detailed in the following sections and presented in The complement model describes pathway activation in plasma and on the surface of erythrocytes 2O) in the plasma or “fluid” phase. C3(H2O) can then bind with FB, generating C3(H2O)B. There are conflicting reports (2O) and C3b. Because of this discrepancy, the model assumes that C3(H2O) and C3b bind with FB with the same binding rate constants. FD cleaves C3(H2O)B producing the tickover convertases C3(H2O)Bb and Ba. Once activated, C3(H2O)Bb can cleave C3, producing C3a and C3b with a reactive thioester denoted as C3b(meta) in the model. The kinetic efficiency of the fluid convertase C3(H2O)Bb is assumed to be half of C3bBb . Since on  by first binding with FB and then FH (The metastable thioester of C3b(meta) either binds to the cell surface or gets inactivated in plasma. If the metastable thioester decays before it binds to the cell surface, it can still participate in the fluid-phase reactions described for C3(H then FH . The pro then FH  for the  then FH ) which i then FH . The flu then FH . The iso then FH ; thus, t2O) can bind to FH, generating C3(H2O)H. This represents the first point of regulation in the alternative pathway as the binding to FH competes with the binding to FB and further activation. FI can cleave C3(H2O)H, releasing FH and the inactive product iC3(H2O). FH also binds with C3b, creating the complex C3bH in the fluid and on the cell surface. The model assumes that FH binds to C3b with a slightly higher affinity than that of C3(H2O) based on the parameter fitting with internal data (data not shown). Unlike the FH reactions in the fluid phase, the cell surface reaction depends on the quality of the surface, usually attributed to the presence of sialic acid (SA) residues, and studies have shown  generated near the surface has a higher probability of binding that surface. C3b(meta) also interacts with C3bBb to form the C5-convertase, C3bBbC3b. Furthermore, C3b(meta) can bind to the α-chain of the surface-bound C3b , p. 199,de novo assembly of the C3-convertase. The quantitative implementation for the role of Properdin in the activation of the alternative pathway is based on the model proposed by Properdin or factor P (FP) is the only known positive regulator of the alternative pathway. In plasma, FP exists as a mixture of head-to-tail dimers, trimers, and tetramers . It is and and 3rd binding events, or alternatively, the cross-linking effect on C3bBb binding to multiple Properdin-binding sites may progressively increase the stabilization of the convertase  and is approximately 3 ((22*2 + 52*3 + 28*4)/(22 + 52+28) = 3.06). The oligomer, denoted as FPn in the model, binds to surface-bound C3b, C3bBclose, C3bBopen, C3bBb, or iC3b, and generates two additional binding sites (P*) on the cell surface for convertase assembly . C3b andnvertase , these eThe availability of multiple binding sites on Properdin oligomers for the assembly of the C3-convertases as well as reactions with several plasma and surface-bound proteins leads to rapid amplification, and it is a key component of complement activation.The terminal pathway is initiated by the cleavage of C5 to C5a and C5b by the alternative pathway C5-convertase. C5a is a potent, short-lived anaphylatoxin and C5b can bind to C6 and perpetuate the formation of the MAC that causes cell lysis. However, only a portion of the newly generated C5b binds to C6 forming C5b6 before its binding site degrades. Since C5b6 is a stable complex, the model assumes the formation of this complex as irreversible. The newly generated C5b6 can bind to C7, creating the C5b-7(meta) complex that possesses a meta-stable binding site for the surface of the target cell.i (where 1 ≤ i ≤ 16). The binding rate of C9 molecules to C5b-8 and C5b-9i is assumed to be equal based on literature evidence (The metastable binding site of C5b-7(meta) can either bind to the target membrane or become inactive in the solution. C5b-7(meta) deposition on the surface is inhibited by clusterin (Cn), vitronectin (Vn), and C8 in the plasma. The C5b-7 complexes that have lost the ability to bind to the cell surface are collectively denoted here as soluble MACs or SC5b-9 complexes. The formation of C5b-7 on the surface of a target cell represents a key point of regulation in the terminal cascade. Once bound to the target surface, C5b-7 can bind to C8 and up to 16 C9 units, forming MACs of various sizes denoted in the model as C5b-9evidence . The acci, thus competing with and inhibiting the binding of subsequent C9 units. The formation of C5b-9 complexes on the surface is inhibited by the membrane-bound regulator CD59, and it acts by inhibiting the C5b-8-catalyzed insertion of C9 into the lipid bilayer . CD59 isi both in the fluid and on the cell surface to prevent binding to C9 and inhibit C9 polymerization (i has been adopted based on a previous modeling work (Clusterin and vitronectin bind to C5b-7(meta) and prevent its binding to the cell surface inhibiting MAC formation and subsequent cell lysis. C5b-7(meta) bound to Cn and Vn in the fluid phase, denoted as C5b-7:Cn and C5b-7:Vn, and can continue to bind to C8 and C9 to assemble soluble MACs. In addition, Cn and Vn bind to C5b-8 and C5b-9rization . The affing work . In the FH and FI are prominent negative regulators of the alternative pathway. In addition to binding with C3b to prevent the C3-convertase assembly, FH accelerates the decay of C3-convertases of the alternative and classical pathways . Based oSimilar to FH, the surface receptor CR1 serves as a cofactor for the FI-mediated cleavage of C3b while also accelerating the decay of the alternative pathway convertases. Low levels of CR1 are associated with autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis. Furthermore, the different CR1 isoforms appear to contribute to the decay acceleration and FI-mediated cleavage to a similar degree ; therefoThe alternative pathway convertases are also degraded by DAF or CD55. Unlike CR1, DAF does not serve as a cofactor for FI-mediated cleavage. The effect of DAF on the decay of C3-convertase has been included based on the literature studies  on both in vivo “healthy state” without any disease pathologies and captures the healthy levels of major complement proteins within the known ranges. The levels of complement proteins and their serum half-lives are summarized in The baseline simulation of the complement model represents a human in vitro assay using purified alternative pathway components . C3 , C3b , factor B , factor D , factor H , and factor I  were mixed in an assay buffer (PBS/14 mM MgCl2) in the presence or absence of Properdin (1.25 μg/ml) and placed in a 37°C water bath. Samples were removed at each time point  and placed in a fresh tube containing 2 μl of 10x stopping solution  and placed on wet ice. The samples were diluted 1/5 with PBS and stored at −80°C until cleavage fragment analysis. C3a was measured using the Complement C3a Human ELISA Kit , and the samples were further diluted 1/75 in the kit sample diluent. Ba was measured using the MicroVue Ba fragment EIA  and the samples were further diluted 1/500 in the kit sample diluent. A custom sandwich ELISA  for this and other assays were sourced ethically, and their research use was in accordance with the terms of the informed consents under an IRB/EC approved protocol.2O/0.01% Triton-x-100 (Sigma-Aldrich) or 100 μl PBS only were used as 100% lysis and background lysis control, respectively. After incubation, the plates were centrifuged for 5 min at 1000xg, the 100 μl supernatant was transferred to a microtiter plate, and the absorbance was read at 540 nm. For analysis, the background lysis was subtracted from all samples, and the percentage lysis relative to the 100% lysis control was calculated.The titration of purified C5b6, C7, C8, and C9 was performed in a reactive lysis system using guinea pig erythrocytes. For each assay plate , 2 ml of guinea pig erythrocytes  were washed in PBS and pelleted . After aspiration of the supernatant, 120 μl of the cell pellet were suspended in 6 ml C5b6 in PBS (1 μg/ml) and incubated for 5 min at 37°C in a water bath. For the C8 and C9 titrations, C7 (1 μg/ml) was added directly to the C5b6-coated erythrocytes to generate C5b7-coated erythrocytes and incubated for 15 min at 37°C, followed by two washes with PBS. C8 (or C9) was serial-diluted in wells of a round-bottom polypropylene plate (50 μl per well), and C9 (or C8) was diluted to 3 μg/ml (or 1 μg/ml) and 50 μl added to the relevant wells in the serial-dilution plates for C8 (or C9). 50 μl of C5b-7-coated erythrocytes were then added to each well and the reactions incubated on a plate shaker for 30 min at 37 °C. For the C7 titration, C7 was diluted to 10 μg/ml, serial-diluted in the wells of a round-bottom polypropylene plate and 200 μl of suspended C5b6-coated erythrocytes added to each well, and the plates were covered and incubated for 15 min at 37°C, followed by two washes in PBS and the resuspension of the pellets in 150 μl PBS. 50μl of the cells were then transferred to a fresh plate containing 100 μl of C8 and C9 , and the reactions were incubated on a plate shaker for 30 min at 37°C. Cells mixed with 100 μl Hin vitro assay data for the alternative and terminal pathways as well as patient disease state data. A “unit testing” approach was used for validating the QSP model dynamics as model sub-components were compared with different datasets separately. A schematic for model simulations for in vitro and in vivo data is shown in in vitro assays can be conducted using purified complement proteins with or without target cells where the activation of the pathway is measured using proteolytic cleavage products such as C3a, Ba, iC3b, and cell hemolysis. To simulate the in vitro assays, the synthesis and degradation reactions in the MATLAB SimBiology model were set as “inactive” to create a “closed system.” If the cells were not used in the assay, all the reactions for cell surface variables were also set as “inactive,” resulting in a simplified “fluid phase tickover” model. The initial conditions in the model were set as the concentrations of the purified proteins or cells used in the in vitro assays, and the rest of the state variables were set to 0. After any washing step in the assays with cells, the fluid compartment concentrations are set to 0, while the cell surface concentrations are unaffected before the next protocol step is simulated. The model was simulated for the assay protocols, and the time-course of cleavage fragments or cell hemolysis was compared against experimental data.The complement model dynamics were validated using in vivo system, supported model validation across several data types with parameter values kept the same across all the representations.The overall validation of the model dynamics has also been performed against data for disease pathologies and drug treatments for completeness. Thus, varied representations of the same complement pathway model, for example, fluid-phase model, closed system, or human The dynamics of tickover reactions in the complement model was assessed based on the literature data  for the in vitro data for the pathway activation via the formation of cleavage products C3a, Ba, and iC3b. There is a significant increase in C3a, Ba, and iC3b due to complement activation in the assay, and the model predictions match with the observed data for C3a and Ba for both the experimental conditions with and without FP  observed in terminal pathway assays due to the formation of the MAC on the cell surface by the action of terminal pathway proteins derived from human plasma. The y assays . The celin vitro terminal pathway lysis is a rapid lysis system using GPE. The guinea pig cells are used instead of human erythrocytes as they are void of cell surface regulators that would control human terminal pathway proteins. Healthy human erythrocytes would not lyse effectively. As the experimental system is very sensitive, variation between experiments can be derived from cell batches, small differences in cell number, or time of the cells in storage. The experiments shown were also performed on different days.The experimental system used for the in vitro data, the overall model simulations were also compared against human in vivo data for the validation of the model dynamics.In addition to unit testing of the complement pathways with via an increase in complement fragments, soluble and surface MACs, and lysis of cells. The cell surface regulators were reduced by 99%, representing almost the complete loss of function, and FH was reduced by 50% of their baseline values to simulate their deficiencies or mutations observed in autoimmune diseases  and FH in plasma were reduced in the complement model to assess the effects on pathway activation diseases .Model simulations show that reduction in CR1 leads to a significant increase in the surface and plasma levels of iC3b  because Reduction in CD59 leads to two to three orders of magnitude increase in surface MACs  and soluA reduction in FH causes a significant increase in several complement fragments, MACs, and cell lysis . Modelinetc.) which drive inflammatory processes such as immune cell attraction, opsonization, and phagocytosis of host cells, leading to worsening of autoimmune diseases in addition to lysis of host cells.It is interesting to note that CD59 and FH deficiencies have the strongest effects on cell lysis, which is seen in PNH and aHUS patients where CD59 deficiency and FH deficiency, respectively, have been indicated. FH deficiency also causes an increase in complement fragments Bb, thus still maintaining the production of C3a in plasma but causing a reduction in C5a which is formed by the cleavage of C5 by C5-convertase on the cell surface downstream of surface C3-convertase.As expected, reduction in terminal pathway proteins C6, C7, C8, and C9 does not lead to a reduction in alternative pathway biomarkers such as C3a, C5a , iC3b, ain vivo, limits on drug doses due to toxicity effects, and inconvenience of frequent IV/SC dosing. We have evaluated the feasibility of developing drug modalities targeting specific complement proteins by assessing the dose levels and affinities needed for small molecules (SMs) and large-molecule antibody (LM-Ab) compounds to maintain reduction in free complement target levels and pathway activation. The PK parameters and affinities are assumed the same as a GSK’s tool FB inhibitor compound for the SM modality and for a typical “good” LM-Ab compound based on published PK parameters for known mAbs  were predicted to be challenging or infeasible to engage with SM compounds . These sSeveral complement proteins  are predicted to have dosing tractability by both small and large molecules with appropriate ranges for target concentrations, turnover, and half-lives. C5 target has the only approved antibody drug, eculizumab, as well as SM assets in development, indicating the tractability by SM/LM-Ab modalities as also predicted by the complement pathway model . AlthougA nominal drug affinity has been assumed for SMs and LM-Ab compounds for dose assessments in The drug affinity requirements for 90% target engagement (TE) may be different from the affinity required for 90% reduction in the disease severity because of the pathway dynamics. This is because the level of TE required for reduction in disease severity may be much higher or lower than the customary 90% TE assumption. Thus, affinities needed for a 90% reduction in cell lysis have been estimated separately. For example, the SM affinity required for 90% inhibition of FD at the highest feasible dose of 100 mg is ∼0.1 μM . But theThe drug affinity requirements for complement targets vary significantly due to differences in half-lives, concentrations, and competition of drug binding with other proteins in the complement pathway. For SM modality, complement proteins C3, FB, C5, C6, and FD required affinities around 10–100 nM for 90% TE, but affinity required for 90% reduction in cell lysis was almost an order of magnitude more potent for C5, FD, and C6 . Additiovia C7 inhibition is 100 times more potent than the affinity required for just 90% TE indicating C7 inhibition of 99.9%, or more is required to reduce disease severity. C3bBclose/open forms need ∼0.1 p.m. affinity for TE; however, the impact of cell lysis seems to be prominent at even lower affinities for the “closed” form of C3bB.For LM-Ab modality, C3/C5-convertases, C5, and C7 are on the lower end of the drug affinity range of ≤1 nM for 90% TE . But theA comprehensive QSP model describing complement activation through alternative and terminal pathways has been developed in this work. The complement model differentiates between the pathway activation dynamics in plasma and on the cell surface. The model also includes a detailed description of several plasma and cell-surface regulatory proteins such as Properdin, clusterin, vitronectin, CD59, CR1, DAF, FH, and FI. The effect of Properdin has been described on the stabilization of the C3 and C5-convertase, on promoting the association of C3b to FB as well as its binding to the surface-bound C3b or other ligands, and using the unoccupied Properdin oligomer-binding sites as receptors for nascent C3b and preformed C3-proconvertase/convertase. This work provides, to date, the most comprehensive mathematical implementation for Properdin stoichiometry and its effect on alternative pathway activation based on available experimental evidence.in vitro assays of alternative/terminal pathways and patient data on disease states. A “unit testing” approach was utilized where separate validation was done for different components of the complex complement model such as alternative pathway, terminal pathway, fluid-phase tickover, a closed in vitro system with cells, and overall in vivo dynamics. This provided an integrated computational and experimental workflow for the utilization of a variety of preclinical and clinical data for model predictions. It also allowed better management of model complexity and higher confidence in the predictions of the combined model as several datasets were used for calibration and validation of model dynamics.The complement model was validated using published or in-house GSK data from To assess the effect of drug targets in disease pathologies, a reduction in FH and CD59 was used as a proximate mechanistic description of complement activation in aHUS and PNH, respectively. The effect of complement targets on downstream biomarkers and cell lysis was assessed for these simulated disease states in the QSP model. One limitation of the complement model developed in this study is that it considers the lysis of RBCs only, but in autoimmune diseases such as aHUS, other host cells such as platelets would also be lysed in additional to RBCs . NeverthFor reducing the complement activation due to CD59 reduction in aHUS, 90% inhibition of C3, FB, FD, C5, C6, FP, C3b, C3Bb, and C3-convertase (C3bBb and C3bBbP) was predicted to lead to a significant reduction in cell lysis. For inhibiting complement activation due to FH deficiency, drug compounds targeting C3, FB, C5, FP, C3b, C3bB, and C3-convertase at 90% inhibition are predicted to be efficacious in reducing disease severity. Several of these complement proteins, such as C5, FB, FD, C6, and FP, have either approved treatments targeting them  or drugs in preclinical or clinical stages of development . The modA few other targets were also predicted to inhibit complement activation driven by CD59 and FH deficiency, however at a sustained target engagement requirement of 99% inhibition which posed uncertainty around feasibility of dosing high drug amounts to maintain high target inhibitions. Thus, an assessment of the dosing requirements for complement proteins using small/large-molecule modalities was also warranted. Small- and large-molecule Ab compounds, with PK properties that lie within the feasible range for these modalities, were simulated to provide estimates for the feasibility of dosing with these modalities. These assessments were initiated around the target validation phase of drug development to support target selection based on not only the reduction in disease severity but also dosing feasibility. The modeling results were also used to inform lead discovery efforts for pursuing the right modality for a target.Key trends emerged from the QSP assessment, which were used to guide the selection of target–modality pairs for drug development. Complement proteins that are present in very high concentrations or have high synthesis/turnover rates  are challenging to block in the plasma with LM-Ab compounds as the drug doses in molar amounts needed for target engagement compared with the total target would be higher than their feasible dose range (≤20 mg/kg). Because of high dosing requirements for target inhibition in plasma, strategies for localized dosing in the tissues, for example, intravitreous delivery of FD-targeting mAb lampalizumab in the eye , have beThe complement model simulations also predict that SM compounds are ideal for maintaining sustained target engagement for high concentration complement proteins due to the low molecular weights and high molar amounts per mg of SM doses. However, the complement proteins/fragments that have a short half-life  were predicted to be challenging or infeasible to engage with SM compounds due to a high drug affinity requirement to “catch” the target before consumption in the pathway. LM-Ab compounds which have higher affinities were able to engage these targets within their feasible dose ranges and are ideal candidates for targeting short-lived complement fragments or complexes.Another limitation of this study is that the early dose estimates are based on “typical” PK and affinities for drug modalities. These preliminary estimates should be refined during lead optimization as the specific PK properties of drug candidates become available. Modalities such as peptides were not assessed here for simplicity; however, the principles discussed in this work can be applied for the assessment of several modalities. Also, only systemic dosing was considered; however, it may be feasible to attain target engagement in a specific organ/tissue with localized dosing even though significant target engagement overall in plasma may not be feasible due to high target amounts.D required for 90% reduction in cell lysis was almost an order of magnitude higher for C5, FD, and C6. Similarly, for LM-Ab modality, drug affinity requirements for C7 are on the lower end of the drug affinity range of ≤1 nM for 90% TE, but the KD needed for cell lysis is 100 times more potent. These analyses indicate the importance for accounting for both TE and biomarker/severity endpoints for affinity predictions at the lead optimization stage to prevent the termination of the compounds in later stages of development and improve their probability of success.In addition to feasible doses for modality selection, another aspect explored in this work was the estimation of drug affinities needed for engaging the complement proteins. During lead optimization, attaining the right level of drug affinity is extremely critical for success in later studies to demonstrate the required level of target engagement. In addition, the drug affinity requirements for 90% target engagement (TE) may be different from affinity requirements for 90% reduction in disease severity if the level of TE required for disease modulation is much higher or lower than the usually targeted 90% level. Thus, an assessment of affinity requirements for TE and cell lysis was done separately, and significant differences in the affinity requirements were observed for targets. For SM modality, complement proteins C3, FB, C5, C6, and FD required affinities around 10–100 nM for 90% TE, but the KThe kinetic parameters in the complement model have been set based on literature references, and for parameters without direct literature evidence, the values were assumed based on similar biological processes or proteins. An assessment of the impact of parameter variability in the QSP model using virtual patients and virtual populations  is beyon


Biosensors were tested on samples containing four heavy metal ions , two phenols (phenol and p-nitrophenol), and three natural water samples. The «ferrocene- Escherichia coli» and «ferrocene-Paracoccus yeei, E. coli association» systems showed good operational stability with a relative standard deviation of 6.9 and 7.3% (14 measurements) and a reproducibility of 7 and 5.2% using copper (II) ions as a reference toxicant. Biosensor analysis with these systems was shown to highly correlate with the results of the standard method using Chlorella algae as a test object. Developed biosensors allow for a valuation of the polluted natural water’s impact on the ecosystem via an assessment of the influence on bacteria and yeast in the receptor system. The systems could be used in toxicological monitoring of natural waters.Microbial mediator biosensors for surface water toxicity determination make it possible to carry out an early assessment of the environmental object’s quality without time-consuming standard procedures based on standard test-organisms, and provide broad opportunities for receptor element modifying depending on the required operational parameters analyzer. Four microorganisms with broad substrate specificity and nine electron acceptors were used to form a receptor system for toxicity assessment. Ferrocene was the most effective mediator according to its high rate constant of interaction with the microorganisms (0.33 ± 0.01 dm Many chemical compounds present in water can have a negative impact on the natural ecosystem. The main polluting components of aquatic environments are solutions of heavy metals and organic compounds. Water toxicity early warning is performed by biotesting and physicochemical methods of analysis.Physicochemical methods, such as high-performance liquid chromatography  and atomBiosensor express analysis is possible due to the application of microorganisms that have physiological reactions with pollutants similar to higher organisms. Compared with relatively high-level organisms, such as fish and daphnia, microorganisms are easily cultured and enable observation of the reaction to toxic substances over short times. Due to these advantages, it has become a tendency over the past 20 years to develop microbial methods for assessing the general toxicity of water . The mosE. coli bacteria  is cell titre, mg/dm3; ν is scan rate, V/s; n is the number of transferred electrons; F is the Faraday constant, C/mol.At the registration of the cyclic voltammogram in the presence of glucose, there occurs an increase of anodic current  due to tThe obtained constants of the interaction of mediators with investigated microorganisms, as well as the rate constant of the heterogeneous transfer of electrons to the electrode, obtained earlier for the studied mediators , are givP. yeei and yeast S. cerevisiae).In general, it can be noted that mediators of the phenazine and phenathiosine series, which can penetrate into the cell , interacThe choice of biomaterial for the formation of bioreceptor elements of a mediator biosensor was based on the sensitivity of microorganisms to harmful environmental factors. These include solutions of copper (II), zinc (II), lead (II), and cadmium (II) ions, as well as phenol and 4-nitrophenol, which are used as model toxicants for test objects in biotesting methods ,16. The 50) that caused a 50% decrease in the activity of the biosensor receptor element. The inhibition index is given in Lemna and Vibrio fischeri [Chlorella vulgaris [In the data obtained, we found concentrations of toxicants  ions and phenol. The concentration of toxicants was selected based on the degree of their inhibition of microorganisms, so that, in the presence of a model toxicant, the metabolic activity of cells decreased by 20% . The choIt can be concluded that the chosen toxicants act reversibly on receptor systems based on single microorganisms and associations; therefore, they can be used as reference toxicants for assessing the quality of the formed working electrodes.P. yeei and ferrocene–E. coli biosensor systems, the Michaelis constants were determined  and the maximum rate of the enzymatic reaction  proceeding from the calibration dependence parameters  ion is observed in the ented in ,42,43. Cented in . In addiented in . Considerogenase . A phenorogenase , data we20). Between measurements, the electrode was kept in a phosphate buffer solution (pH 6.8). If the sensor response decreased by less than 20%, the electrode was considered unfit for work. The obtained dependences are shown in 2+ ions. The results are given in When operating electrodes, it is important to evaluate reproducibility, as well as operational and long-term stability. Long-term stability characterizes the duration of biosensor operation over a certain period of time, which is ensured by the preservation of the vital activity of microorganisms on the surface of the electrode. Long-term stability was determined by measuring the degree of inhibition of microbial activity. For this, we daily measured the biosensor response at the concentration of the toxicant that caused a decrease in the respiratory activity of microorganisms by 20% . A quantitative characteristic of operational stability is the relative standard deviation in repeated measurements (15 times) of the standard sample (95% confidence probability). The results obtained are presented in P. yeei–Cu2+ system.The system based on the ferrocene mediator and P. yeei–Cu2+ system.The reproducibility of the signals of the receptor elements was evaluated by the magnitude of the relative standard deviation of responses from a series consisting of seven electrodes at the introduction of a model solution (95% confidence probability). Based on the results obtained, the best reproducibility indicator can be said to be the ferrocene–Chlorella, the reference method). Three samples of river water were used. The registered parameter was the toxicity index defined as the degree of a decrease in the activity of the biosensor bioreceptor element and the degree of inhibition of chlorella growth. The results obtained are presented in TDF).Three wastewater samples were analyzed using the developed mediator biosensors based on microorganisms and the method of biotesting  and (4) are given in The E.coli and ferrocene–association P. yeei/E.coli biosensor systems. The ferrocene–P. yeei biosensor system is not suitable for TDF determination due to its rather low sensitivity to toxicants that can occur in analyzed samples. At the same time, the ferrocene–S. cerevisiae biosensor system possesses a high sensitivity to toxicants of the sample, due to which the results do not correlate with the data of the standard method. The results obtained in the experiment suggest that the ferrocene–E. coli and ferrocene– association P. yeei/E. coli biosensor systems can be successfully used for determining the general toxicity of aqueous media. The developed bioanalytical systems can serve as prototypes for commercial rapid-assay toxicity analyzers. The main limitation of wide application of the developed receptor elements is long-time stability: 3 days for E. coli/ferrocene system and 4 days for P. yeei, E. coli/ferrocene.According to the data obtained, it can be concluded that Sample 1 is highly toxic. The best correlation with the results of the standard method in the analysis of natural waters was achieved using ferrocene–50 were found for four heavy metals  and two phenols (phenol and p-nitrophenol). The bacteria E. coli and P. yeei were the most sensitive to the action of the model toxicants: Cu2+, Pb2+, and phenol. Therefore, the use of these microorganisms as biomaterials in the development of an amperometric biosensor for integral toxicity determination is more preferable. As the growth parameters of these microorganisms are close, a stable association formed on their basis can be assumed. This study established that the analytical signal of the biosensor in a rapid assay could be adequately treated as a degree of inhibition of the receptor-element respiratory activity as the result of a glucose oxidation rate decrease in the presence of an inhibitor. This enabled an accounting for the effects of substances that could occur in the sample and be oxidized by the biomaterials of the receptor system. The values of ECE. coli and ferrocene–P. yeei/E. coli systems have high sensitivities to solutions of heavy metals and organic compounds, as compared with other analogs presented in other publications. Mediator biosensors based on an association of E. coli and P. yeei bacteria for integral toxicity assays are reagent-free. The method enables much faster results than other biotesting methods; the assay results are consistent with those of the standard biotesting using Chlorella vulgaris algae as a test object. The developed ferrocene–The developed biosensor allows for the evaluation of the impact of polluted natural water to the ecosystem via assessment of impacts on bacteria and yeasts in the receptor system. These systems can be used in toxicological monitoring of natural waters.


Haloferax mediterranei. The effects of acetate (Ac), propionate (Pr), butyrate (Bu), and the short-chain carboxylates derived from food waste were examined on the microbial growth and PHBV production. Results showed that a mixture of carboxylates provided a 55% higher PHBV yield than glucose. The food-waste-derived nutrients achieved the yields of 0.41 to 0.54 g PHBV/g Ac from initial loadings of 450 mg/l to 1,800 mg/l Ac of total carboxylates. And the consumption of individual carboxylate varied between different compositions of the carbon source. The present study demonstrates the potential of using food waste as feedstock to produce PHBV by Haloferax mediterranei, which can provide economic benefits to the current PHA industry. Meanwhile, it will also help promote organic waste reduction in landfills and waste management in general.Polyhydroxyalkanoates (PHA) are a family of microbial polyesters that are used as biodegradable plastics in replacement of conventional plastics for various applications. However, the high production cost is the barrier for PHA market expansion. This study aimed to utilize food waste as low-cost feedstock to produce poly (PHBV) by  Plastic products are important commodities, with a global production scale of approximately 370 million tons per year, of which 99% are derived from fossil carbon feedstocks . The usee.g. packaging films and containers [Known for the renewable origin and naturally decomposable property, biodegradable plastics are an emerging substitute for conventional ones. As one major type of biodegradable plastics, PHA is a family of high-value polyesters with thermoplastic properties and is used for the manufacturing of various products, ntainers . PHBV isntainers . The PHAntainers , and is ntainers . One of ntainers , and thentainers . To reduHaloferax mediterranei is a halophilic archaeon capable of synthesizing PHBV from inexpensive carbon sources [As intracellular polymers of cells, PHA is produced through microbial cultivation by pure strain or mixed microbial consortium. Pure culture biotechnology using freshwater microbes are commonly seen in industrial scale productions , where s sources . The higH. mediterranei: hydrolyzed whey [H. mediterranei excretes extracellular polymeric substances (EPS) when fed with sugar substrates [H. mediterranei [The majority of recent studies have been using sugar-based substrates to produce PHBV by zed whey , extrudezed whey , ethanolzed whey , high-sazed whey , molassezed whey  and macrzed whey  etc. Howbstrates , 17, whibstrates . Short-cterranei . The strterranei .Food waste is a potential feedstock for PHA production because large quantities of carboxylates can be generated through anaerobic fermentation process, where the production of carboxylates can also be controlled and optimized . At presH. mediterranei and (2) to examine the production of PHA using food waste as feedstock through a two-stage process. Food waste was firstly degraded into carboxylate-rich nutrients through anaerobic fermentation; then the nutrients were utilized by H. mediterranei to produce PHBV in the controlled aerobic process. The results of this study can demonstrate the feasibility of using food waste as feedstock for PHA production. The two-stage bioconversion process could be potentially employed as an innovative and more environmentally friendly approach in the PHA industry.The objectives of this research were: (1) to understand the effects of various VFAs as the substrate for the cell growth and PHA production by H. mediterranei (ATCC 33500) was used throughout the study. The cells were firstly cultured in ATCC 1176 medium [2.6H2O, 13 g/l; MgSO4.7H2O, 20 g/l; CaCl2.6H2O, 1 g/l; KCl, 4 g/l; NaBr, 0.5 g/l and FeCl3, 5 mg/l) with trace metal solution  [The wild-type strain 6 medium  at 37°C.30 mg/l) , 23 and 4Cl was added at 3.06 g/l to give a C/N ratio around 5, which provided excessive nitrogen [2PO4 was added at 0.5 g/l to obtain a C/P ratio about 35, which supplied sufficient phosphorous [Two types of pure carbon sources, VFA mixture  and glucose were used respectively to give the same initial carbon concentration of 4 g/l. NHnitrogen . KH2PO4 sphorous . MSM andA simulated food waste mixture containing (% w.b.) 28% cooked rice, 32% cooked ground beef and 40%chopped raw cabbage was firstly blended into a slurry using a food processor. The food waste slurry was then anaerobically fermented in 1-L batch reactors with an organic loading of 32 g volatile solids per liter (VS/L) and a food to microorganism (F/M) ratio of 8. The headspace was purged with argon gas to remove air prior to the start of anaerobic fermentation. All the reactors were housed in an incubator controlled at 37°C and mixed twice per day. The pH of fermentation broth naturally dropped from 6 to 5 and remained around 5 afterwards. The fermentation lasted for three weeks until the concentrations of carboxylates were stabilized. After that, the broth was centrifuged at 5,000 rpm for 30 min; the supernatant was then filtered by using 0.2 μm membrane to obtain a particle-free nutrient solution.H. mediterranei. The nutrient solution was loaded with four levels: 1.06, 2.12, 3.18, and 4.24 g soluble chemical oxygen demand per liter (sCOD/L), with corresponding contents of total carboxylates as 450, 900, 1,350, and 1,800 mg Ac/l. The base saline medium containing MSM and SL-6, along with sufficient N and P nutrients loadings same to that used in previous experiments. 30 mM NaHCO3 was used as buffer to maintain pH at 7.0 for the entire culturing period. The 250-ml bioreactors with 200-ml working volume were used for PHA production. The strain was cultured at 37°C with 100 ml/min aeration for 144 h until the growth reached stationary phase.The nutrient solution, with carboxylates contents as shown in To monitor cell growth status, 2 ml of cell broth was collected at different time points during cultivation period and centrifuged at 10,000 rpm for 10 min; the cell mass was resuspended in MSM solution, and the optical density (OD) was analysed at 520 nm . The cel-1); µm is maximum specific growth rate (h-1); S is total carboxylate loading (g Ac/l); Ks is the half velocity constant (g/l).Where, μ is specific growth rate (h2SO4), with 1 g/l benzoic acid as internal standard. The 12-ml KIMAX culture tubes with rubber lined caps were used for this preparation. The tubes with solution were placed in Hach DRB200 digital reactor block at 105°C for 4 h and cooled to room temperature. The solution was mixed with 1 ml deionized water and settled until phase separation was observed. The organic phase was then transferred to a clean vial for quantification. PHBV with 12% hydroxyvalerate (HV) (Sigma-Aldrich) was used as the standard PHBV chemical. The samples were analysed using a gas chromatograph (GC) method developed based on the methods reported in the literature [2 flow and 450 ml/min of air flow.The strain samples were subjected to transmission electron microscopy (TEM) analysis to observe the cell interiors following a previous method . The extterature -31 with Based on the measured results, the PHBV yield was calculated as the mass of PHBV produced divided by the mass of substrate consumed. Two types of substrate were used in the calculations: glucose and total carboxylates (g Ac/l).The PHBV content of cell was calculated as the percentage of PHBV accounts for the total CDM, described as g VSS/L:The percentage of HV unit in PHBV polymer, which indicates the elasticity of the plastic material, was determined based on the mass ratio between HV unit and the sum of HV and HB units as measured by the GC method described previously:To quantify the consumption of substrates, 2 ml of cell broth was collected at different time points during cultivation and centrifuged at 10,000 rpm for 10 min; the supernatant was then filtered through a 0.22 μm membrane. The filtrate was measured for the contents of short-chain carboxylates and glucose by an analytical method described previously .H. mediterranei has a pinkish appearance formed by a group of red carotenoids which naturally exist in the cell membrane of the extreme haloarchaea [oarchaea , 33. Duroarchaea . The PHBInfluence of carbon sources on cell growth and PHBV production. As described in section 2.2, two different types of substrates, pure VFA mixture and glucose, were used individually as the sole carbon source with the same initial carbon concentration of 4 g/l. The maximum concentrations of cell mass and PHBV obtained from pure VFA mixture were 2.99 ± 0.29 g/l and 1.57 ± 0.05 g/l, respectively. In comparison, glucose as carbon source gave slightly higher results: 3.59 ± 0.25 g/l of cell mass and 2.17 ± 0.20 g/l of PHBV as shown in -1; whereas the growth rate in VFA mixture was 0.024 ± 0.0007 h-1. A previous study has reported the μ of H. mediterranei by using one or two VFA species as carbon source to be 0.010 to 0.035 h-1 [.035 h-1 , so the e.g. glucose, starch and microalgae hydrolysates [H. mediterranei. In addition to sugar-based carbon sources, the species can also accumulate PHBV by consuming VFA under normal growth conditions. This can be an advantage over some other PHA producers which require necessary growth-limiting factors to stimulate PHA accumulation. This could also be beneficial to large-scale industrial production since PHBV production will not be limited by cell mass in this case.The time course monitoring of PHBV production and substrate consumption is shown in olysates , 18, 24.H. mediterranei during cell cultivation. Based on the current results, it would also be worthwhile to investigate the PHBV production under limited growth conditions, to see if that can lead to more PHA accumulation.Particularly when VFA mixture were used as carbon source, the curve of PHBV generation was mostly parallel to that of cell mass production during the entire culturing period. It indicates that the PHBV content of cell dry mass also remained approximately constant when carboxylic nutrients were consumed. The constant PHBV content may be due to the sufficient supply of nutrients and the preferable C/N ratio that maintained the normal growth of The influence of carbon source on PHBV yield and HV content. As shown in 2 loss in tricarboxylic acid (TCA) cycle. Meanwhile the metabolic pathways from glucose to PHBV involve more reaction steps than that from carboxylates [The higher PHBV yield from VFA than glucose may be due to short-chain carboxylates are the more direct precursors for the synthesis of PHBV than glucose. Short-chain carboxylates (C2 to C5) can be utilized as the sole carbon source for the polymerization of PHBV . In the oxylates , which roxylates . Anotheroxylates .Carbon source did influence the HV content of PHBV during cultivation process. Under the same carbon loading, the HV content using pure VFA mixture remained stable between 15% to 18% of PHBV during the entire culturing period; meanwhile the HV content using glucose increased from approx. 12% to 24% of PHBV. This may be due to that glucose can be metabolized into HV precursor with the same molar concentration, while only Pr, accounting for part of VFA mixture, was the HV precursor. Since a higher HV content results in more elasticity of the plastic material, it would be feasible to direct the HV content through manipulating the VFA composition for future research interest.Substrate consumption. There was a small portion of VFA leftover when the concentrations of cell mass and PHBV reached maximum and remained stable. As shown in H. mediterranei for growing and synthesizing PHBV in VFA mixture. The cell growth rate also varies using different VFA species as the single carbon source [H. mediterranei, which could be a further research interest.n source . The resCarboxylates and other nutrients from anaerobically fermented food waste. Around 95% of total carboxylates were obtained after two weeks of anaerobic fermentation of food waste and the production remained almost stable during the week 3. The average total carboxylate yield was 0.35 g/g VS, which was in a reasonable range (0.1 to 0.6 g/g) as previously reported for organic waste [2 and 62% (v/v) CO2 were produced and the production of CH4 gas was minimum, suggesting the process had been well controlled for acid production with minimum methanogenesis process occurred. The liquid phase of fermented food waste slurry contained over 9 g/l Ac of total carboxylates. The fermentation broth had over 20 g sCOD/L, of which about 76%was from carboxylates.ic waste . Gases cThe soluble nutrient composition of the fermentation liquid is shown in The influence of substrate loading on cell growth. The cell growth curves with different substrate loadings  are illustrated in -1 and increased with higher loadings of total carboxylates. The cell growth rate from waste-derived carboxylic nutrients was very similar to that from pure carboxylic nutrients. As shown in 2 of 0.983. The modelled µm was 0.0296 ± 0.0007 h-1; the Ks was 383 ± 34 mg/l. The modelled reaction kinetics suggested higher substrate loadings would be possible to obtain higher specific growth rate, which would be beneficial for the steady-state and other continuous production scales. Previous studies have reported the possible inhibition effect of VFA on cell growth of some PHA-producers at high concentrations [H. mediterranei.Based on the results of OD from 24 h to 96 h when the most rapid growth happened, the μ was calculated to be from 0.014 to 0.025 htrations ; therefoThe influence of substrate loading on PHBV production. Like cell mass production trend, the concentration of PHBV increased with higher loadings of fermented food waste permeate from 450 to 1,800 mg Ac/l. As shown in H. mediterranei under balanced growth conditions by other studies, as shown in e.g. when nitrogen source is limited and C/N ratio is high [ is high . Similar is high . Some ge is high , 42.H. mediterranei.The PHBV yield remained in the range of 0.41 to 0.54 g PHBV/g Ac, with 450 mg/l to 1,800 mg/l Ac of total carboxylates loadings. The results suggested that short-chain carboxylates, either pure chemicals or derived from food waste fermentate, can be a good option of the carbon source that achieves high PHBV yield by The average HV contents from waste-derived carboxylic nutrients were in the range of 6% to 11% and decreased with higher total carboxylates loadings. Based on current literature, glucose, Pr and Va were found to be the precursors of HV units in the PHBV synthesis , 34. TheSubstrate consumption. The total carboxylate  consumption with different substrate loadings during the entire culturing period is shown in e.g. when the composition of carboxylic mixture varies, and other soluble nutrients exist in the medium.


Bacillus subtilis to enhance -2,3-butanediol  production from glycerol. OptKnock is another powerful technique for devising gene deletion strategies to maximize microbial growth coupling with improved biochemical production. It has never been used in B. subtilis. In this study, we aimed to compare the use of single-level FBA and OptKnock for designing enhanced 2,3-BD production from glycerol in B. subtilis.Flux Balance Analysis (FBA) is a well-known bioinformatics tool for metabolic engineering design. Previously, we have successfully used single-level FBA to design metabolic fluxes in B. subtilis to enhance 2,3-BD production from glycerol. Single-level FBA indicated that deletion of ackA, pta, lctE, and mmgA would improve the production of 2,3-BD from glycerol, while OptKnock simulation suggested the deletion of ackA, pta, mmgA, and zwf. Consequently, strains LM01 (single-level FBA-based) and MZ02 (OptKnock-based) were constructed, and their capacity to produce 2,3-BD from glycerol was investigated. The deletion of multiple genes did not negatively affect strain growth and glycerol utilization. The highest 2,3-BD production was detected in strain LM01. Strain MZ02 produced 2,3-BD at a similar level as the wild type, indicating that the OptKnock prediction was erroneous. Two-step FBA was performed to examine the reason for the erroneous OptKnock prediction. Interestingly, we newly found that zwf gene deletion in strain MZ02 improved lactate production, which has never been reported to date. The predictions of single-level FBA for strain MZ02 were in line with experimental findings.Single-level FBA and OptKnock were used to design metabolic engineering approaches for B. subtilis. Further, while this approach predicted the phenotypes of generated strains with high precision, OptKnock prediction was not accurate. We suggest that OptKnock modelling predictions be evaluated by using single-level FBA to ensure the accuracy of metabolic pathway design. Furthermore, the zwf gene knockout resulted in the change of metabolic fluxes to enhance the lactate productivity.We showed that single-level FBA is an effective approach for metabolic design and manipulation to enhance 2,3-BD production from glycerol in The online version contains supplementary material available at 10.1186/s13036-022-00320-w. For decades, genome-scale metabolic models (GEMs) have been recognized and used as a powerful bioinformatics tool for biological systems. GEMs are computational reconstructions of metabolic networks that can be applied to a wide range of living cells, such as microorganisms, plants, etc. GEMs are constructed based on basic genome annotation and experimental data, and reflect the gene–protein–reaction relationships and the mass, energy, and proton balance of all reactions for the entire set of metabolic genes in an organism . Many soScheffersomyces stipitis [R,R)-2,3-butanediol  production in Bacillus subtilis [Lactobacillus plantarum WCSF1 and Lactobacillus reuteri JCM 1112 [Escherichia coli [Flux Balance Analysis (FBA) is a mathematical optimization technique for the simulation of metabolic fluxes in GEMs using single-level linear programming , 4. FBA stipitis , -2subtilis , lactateJCM 1112 , and shihia coli .E. coli. As another example, Pharkya et al. [E. coli. Further, Feist et al. [E. coli GEM (iAF1260 model) to identify patterns for gene deletion solutions, with optimization for 3- to 5-gene deletions, to improve the production of multiple native products in E. coli from various types of substrates.OptKnock has been developed as a guide for gene deletion strategies to maximize microbial cell (biomass) production together with an improved biochemical production . OptKnoca et al.  used Optt et al.  used OptR,R)-2,3-butanediol , a useful chemical compound that has several applications, such as in printing ink, antifreeze agents, foodstuffs, and pharmaceuticals [Glycerol is a promising low-cost, renewable, and non-food competitive substrate compared to glucose, which is the main raw material used in the biological fermentation process. The price of high-purity crude glycerol (80–90%) is about 0.00–0.11 USD/kg, whereas the price of glucose is comparatively more expensive (3.37 USD/kg) . Thus, geuticals . Furthereuticals . Furthereuticals .B. subtilis is accepted as a “generally recognized as safe” organism that grows in the presence of high levels of fermentation products, such as volatile fatty acids (VFAs), and does not generate endotoxins, unlike gram-negative bacteria, such as E. coli [ E. coli . It is aB. subtilis [ackA gene encoding acetate kinase; pta gene encoding phosphate acetyltransferase; lctE gene encoding l-lactate dehydrogenase; and mmgA gene encoding acetyl-CoA acetyltransferase) to enhance the production of 2,3-BD from glycerol in iYO844 GEM. The deletion resulted in a 2,3-BD yield increase by 43.75%, compared to that from the wild-type strain. However, another strategy, OptKnock, has never been applied for improving bio-production in B. subtilis.Previously, we successfully used single-level FBA to design and evaluate the effect of gene knockouts for enhanced 2,3-BD production from glycerol in subtilis . The preB. subtilis for improved 2,3-BD production from glycerol. We used B. subtilis GEM (iYO844 model) constructed by Oh et al. [B. subtilis for 2,3-BD production from glycerol that showed the most reliable performance with glycerol as a sole carbon source in M9 medium. We then constructed B. subtilis deletion mutants following the predictions of single-level FBA and OptKnock, and evaluated changes in the fermentation profile of the resultant strains and wild-type B. subtilis strain (W168). We also inspected the accuracy of in silico predictions with the fermentation profile.In the current study, we investigated the use of single-level FBA and OptKnock to manipulate the metabolic flux network of h et al.  to desigB. subtilis to enhance 2,3-BD production from glycerol. We created a metabolic pathway of glycerol dissimilation following flux distribution based on the results of single-level FBA and OptKnock in iYO844 GEM with glycerol as a sole substrate , PTAr (pta), LDH_L (lctE), and ACACT1r (mmgA), would enhance 2,3-BD production (Table ack) and PTAr (pta) reaction was predicted to reduce acetate production. The removal of LDH_L (lctE) reaction was predicted to inhibit lactate production. Finally, the removal of ACACT1r (mmgA) reaction was predicted to block the production of acetoacetate. According to single-level FBA, inhibition of these four reactions would improve the 2,3-BD production from 0 to 2.54 mmol/gCDW/h , compared with the flux in the wild-type strain.We used single-level FBA and OptKnock to redesign metabolic pathways in ate Fig. . Single-ack), PTAr (pta), ACACT1r (mmgA), and G6PDH2r (zwf) reactions. Considering the flux distribution, the biomass production would increase to 0.35 mmol/gCDW/h, with 2,3-BD production at a level of 2.10 mmol/gCDW/h, with the acetate production decreased to 0.15 mmol/gCDW/h (Table lctE) reaction was not removed. To the best of our knowledge, the knockout of the zwf gene to enhance 2,3-BD production has not been reported to date. Hence, the analysis suggested that deletion of these genes would impede lactate production and improve 2,3-BD production.OptKnock suggested a different strategy, with the improvement of growth (biomass production) coupling with the improvement of 2,3-BD production set as the objective functions. The modelling suggested the removal of the ACKr (B. subtilis based on the gene deletions suggested by single-level FBA (strain LM01) and OptKnock (strain MZ02) predictions, and investigated 2,3-BD production during fermentation, with the wild-type strain (W168) as a control. The results of these analyses are described below.We subsequently constructed mutant strains of B. subtilis strains was evaluated using batch fermentation. The deletion of multiple genes in strains LM01  and MZ02  did not notably negatively affect bacterial growth  [ydap gene  and yflL gene , which convert pyruvate to acetyl phosphate, and then acetate. These missing reactions should be analyzed and the information gap filled to improve the iYO844 GEM.Considering the deletion of rvations . Similarrvations  suggesteGenomes) , acetatelctE and mmgA genes. Knocking out lctE suppressed lactate production during fermentation. The product of this gene is the main competitor of the 2,3-BD production reaction, competing for NADH consumption [lctE or ldh gene improves 2,3-BD production in different bacteria [mmgA knockout was shown in a previous study [In addition to the above, the LM01 strain lacks the sumption . Accordibacteria , 23, 24.us study . In the ackA and pta, the mmgA gene was also deleted to promote 2,3-BD production. However, together with the deletion of the zwf gene, this did not improve 2,3-BD production , which inhibited the production of 2,3-BD [B. subtilis, modifications of some genes in E. coli were tested for improved lactate production, e.g., by removing reactions competing for pyruvate, such as the ethanol/acetate metabolic pathway, or precursor reactions converting phosphoenolpyruvate into succinate [zwf gene, which is related to glycerol dissimilation to the pentose phosphate pathway, enhances the production of lactate. Considering the single-level FBA predictions, the increase in lactate production might be driven by a decreased metabolic flux from dihydroxyacetone phosphate (dhap) to d-fructose 1,6-bisphosphate (fdp) as a result of the zwf gene deletion, with lactate produced by the conversion of dihydroxyacetone phosphate (dhap) to glyceraldehyde 3-phosphate (g3p)  would enhance the biomass and 2,3-BD production in lctE) reaction was not considered for removal because the initial flux of this reaction was zero. Whereas the flux of zwf gene was 0.85 mmol/gCDW/h, which OptKnock selected this reaction as the priority of targeted genes for deletion. This strategy might have led to a bias when assessing biomass and 2,3-BD production, and contributed to the erroneous identification of reactions for removal.Two-step FBA simulates metabolic distribution before gene deletion. We set objective functions similar to those of OptKnock, by maximizing biomass production as the first target and maximizing 2,3-BD production as the second target Table  and 3. HB. subtilis strain should be constructed to allow the conversion of the entire intracellular pyruvate pool to 2,3-BD, not lactate.We analyzed the MZ02 strain by single-level FBA, with the biomass production as the only objective function. The predictions corresponded to the experimental data, especially for the logarithmic phase of growth Tables . The proE. coli model, was shown in many studies [Many factors might impact the predictions of OptKnock, such as the degree of completion of the entire metabolic pathways in GEM, the use of solvers for problem function, and so on. Nonetheless, the successful use of OptKnock to optimize biochemical production of native products, especially in the  studies , 13, 14.B. subtilis. We have previously successfully used single-level FBA to design and evaluate the effect of gene knockouts to enhance 2,3-BD production from glycerol in B. subtilis via iYO844 GEM. Single-level FBA suggested the deletion of four genes , while OptKnock suggested the deletion of a different gene set . We detected the highest 2,3-BD production in strain LM01, with the 2,3-BD production in MZ02 strain similar to that in the wild-type strain. Consequently, we conducted two-step FBA to examine the reason for the erroneous OptKnock prediction. We observed no flux distribution of lactate after maximizing two objective functions . Surprisingly, lactate production in strain MZ02 was significantly higher than that in the wild-type strain, which we linked, for the first time, to zwf deletion. Predictions of single-level FBA for the MZ02 strain corresponded to the experimental data, as also the predictions of single-level FBA for the LM01 and wild-type strains. Consequently, we propose single-level FBA as one of the reliable methods for the design and evaluation of metabolic pathways of microbial GEMs. We also recommend evaluation of OptKnock predictions by using single-level FBA to confirm the accuracy of the designed metabolic pathway.We here investigated the use of OptKnock and single-level FBA simulations for designing metabolic fluxes to enhance the production of 2,3-BD from glycerol in B. subtilis 168, constructed by Oh et al. [B. subtilis were set to the lower bounds of necessary metabolites to allow limitless uptake by the exchange reactions. In all simulation approaches, the maximum uptake rate of glycerol was set at –10 mmol/gCWD/h, with glycerol as the sole carbon source, and the oxygen consumption was limited to –10 mmol/gCWD/h to provide facultative anaerobic conditions. The reactions involving alcohol dehydrogenases (ALCD19_D and ALCD19_L) were removed from GEM, based on Kalantari et al. [In the present study, the iYO844 GEM of h et al.  and avaih et al. , was usei et al. . The COBi et al.  was usedB. subtilis in the current study. Based on the single-FBA predictions, a multiple-gene deletion mutant of the wild-type strain (W168) was constructed (see below) to increase the production of 2,3-BD .Single-level FBA was conducted by using the solver of the commercial GNU Linear Programming Kit (GLPK) package . The solB. subtilis wild type for use in this approach have been determined [B. subtilis genes, and not concerning the exchange reactions and transporter reactions in the iYO844 model, were set as the available gene targets for deletion by OptKnock  for enhanced growth coupling with 2,3-BD production. A solver for MILP problem is required for OptKnock simulation; consequently, the Gurobi Optimizer package  was usedtermined , 33. The68 for en−5. The GLPK package was used as a solver. Two-step FBA was used to predict the major fermentation products of the wild-type strain by using two objective functions. The predictions were compared with those of single-level FBA and OptKnock.To inspect the erroneous predictions of OptKnock, two-step FBA was performed by defining two objective functions for maximization in the wild-type strain, similar to OptKnock simulation. Biomass production was set as the major product and 2,3-BD production was set the minor product. Specifically, the element corresponding to 2,3-BD production in a column array containing the objective function coefficients was set to 10Finally, single-level FBA was used to evaluate the metabolic flux distribution in MZ02 strain, with the biomass production set as an objective function, to compare the predicted fermentation profile with the experimental results.B. subtilis wild-type 168 (W168) was obtained from the Microbe Division at the RIKEN BioResource Research Center, Japan. CRISPR-Cas9 system was used for gene knockout in B. subtilis [E. coli NovaBlue  was used for plasmid construction. The strains and plasmids used in the current study are listed in Table subtilis . E. coliB. subtilis [B. subtilis colonies in M9 medium (5 mL) and incubating at 37 °C, 180 rpm, overnight. After that, 1% (v/v) of overnight culture was placed in M9 medium and incubated at 37 °C, 180 rpm, for 18 h. Finally, 0.5% (v/v) of the culture was used as the seed culture for the experiment. The cells were grown under micro-aerobic conditions in 120 mL of M9 medium, incubated at 37 °C and 100 rpm for 96 h. The liquid medium was periodically sampled and bacterial growth, and the concentration of glycerol and fermentation products  determined, as described below.Luria–Bertani (LB) liquid medium, containing (per liter) 10.0 g tryptone, 5.0 g yeast extract, and 10.0 g sodium chloride, was used for the maintenance of all strains and plasmid construction. All strains were preserved at –80 °C and re-activated on LB agar plates (LB medium supplemented with 1.5% (w/v) bacto-agar). M9 medium for subtilis , which cB. subtilis cells was determined by monitoring the culture optical density at 600 nm (OD600) using a UVmini-1240 spectrophotometer . CDW was calculated based on the OD600 readings by using the 1 g CDW/L per OD600 ratio of 0.325 [5H7O2N, with the molecular weight of 113 g/mol [2SO4) was set at 0.5 mL/min. The chromatography data were compared to the standard curves of glycerol, acetate, lactate, pyruvate, succinate, diacetyl, acetoin, 2,3-BD, and ethanol.The growth of of 0.325 . The bio13 g/mol , and wasAdditional file 1. Non-essential reactions in iYO844 GEM available for OptKnock prediction.