We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells. The attribute “emerging” reflects some ambiguity, being neither core, nor enabling. Nonetheless, the unique metabolic features of cancer have been a driving force for many important and thought-provoking research in cancer therapeutics in recent years9. Unfortunately, translating much of this knowledge into a major therapeutic breakthrough remains a critical challenge. Mathematical modelling is now recognized as a valuable tool with which to elucidate the various mechanisms that underlie a growing tumour’s response to treatment10, and is therefore in a unique position to identify novel therapeutic targets that exploit our knowledge of cancer metabolism. Indeed, there exists a vast body of literature on mathematical and systems biology models of cancer growth and response to treatment .Cellular metabolism has long been recognized to play an important role in cancer progression and response to treatment31. In this top-down approach, the results of “omic” investigations  inform flux-balanced metabolic pathways. Although these models have contributed greatly to our understanding of metabolic pathways within cancer cells, they suffer from some limitations. For instance, this approach does not adequately capture the effects of cellular heterogeneity within a tumour, or of interactions between cells and the tumour microenvironment. Furthermore, these models are static and fail to capture key system dynamics such as temporal and spatial heterogeneities that arise due to environmental fluctuations. Finally, flux balance analysis (FBA) requires not only the stoichiometry of the network, but also an appropriate objective function and, possibly, further constraints. For instance, in the growth of microorganisms, the most consistent optimal criteria are maximising biomass yield per flux unit or maximising ATP yield per flux unit. However, in the case of cancer growth, the objective functions are typically more complex, including multi-objective optimization problems (optimization involving more than one objective function). We remark that some of these constraints may be overcome by application of 13C metabolic flux analysis36.In particular, a commonly used framework for mathematical modelling of cancer cell metabolism is based on flux balance analysis48 in microorganisms and cell cultures. Experimental methods essential for identifying the in vivo kinetics that inform these models, follow a stimulus–response methodology wherein cells grown in culture are disturbed by fast changes in extracellular glucose concentration. The resultant dynamic responses of intra- and extracellular metabolites are then measured. Two different approaches have been used to infer dynamic models from this data. In the modular approach, the metabolic network is decomposed into manageable subunits, and experimental measurements determine the functional forms and parameters of the kinetic equations38. The second approach utilizes optimal control methods for model simplification, such as lin-log approximations51. Examples of the application of lin-log kinetics for the simultaneous estimation of model parameters include the whole cell metabolic network dynamics of E. coli52 and a dynamic model for the central metabolism of HEPG2 liver cancer cells53.Equally, bottom-up approaches based on the kinetics of individual reactions have been used to generate testable predictions at the macro-scale from dynamic models of pathways and networks55, but attempts to develop the necessary quantitative framework have not been previously reported.Our objective here is to better understand how known metabolic processes occurring in individual cancer cells inform tumour-level metabolic dynamics. Specifically, we propose integrating experimentally validated dynamic models of the central metabolism of HEPG2 cancer cells within spatially resolved multiscale models of hepatocellular cancer growth. This will allow us to predict the spatio-temporal dynamics of key metabolites within a growing vascular tumour. Such models can, in the future, inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells. Indeed, specialized articles on cancer cell metabolism often acknowledge the need for such an integrated approach56 and Perfahl et al.57). These model couple blood vessel formation or angiogenesis in response to a growing tumour’s nutritional demands with blood flow, nutrient transport, and the nutrient-dependent processes of cellular proliferation, quiescence and apoptosis. We supplement this 3D framework with external glucose balance, including the transport of glucose from the blood vessels, its diffusion through the interstitium, and its uptake by cancer and healthy cells. The metabolism of internalized glucose is explicitly included by integrating within this framework, a dynamical model of the central metabolism in HEPG2 cells, developed by Maier et al.53. This model has been experimentally validated by quantitative measurements of metabolite concentrations under dynamic conditions (stimulus–response experiments) and metabolic flux distributions obtained from transient 13C flux analysis33. Additionally, we propose a new functional form for the probability of successful anastomoses during angiogenesis in 3D, which is based on actual experimental observations61. Specifically, we allow this probability to be a function of distance between sprout tips, rather than simulating anastomosis in a more phenomenological fashion where it occurs when two sprout tips (or a sprout tip and an existing vessel) meet simply due to motion on the grids of the cellular automaton model.We begin by extending previously published hybrid, multiscale models of vascular tumour growth . Intracellular behaviour is described by ordinary differential equations (ODEs) for intracellular VEGF production, p53-, CDH-, p27-activity, and progression through the cell cycle. These, in turn, govern rule-based cellular behaviour such as division, quiescence and apoptosis. In our formulation, each cell is a stochastic agent whose behaviour is simulated via a cellular automaton model, which describes cell–cell interactions and cell movement. The extracellular concentrations of diffusible species—VEGF and oxygen—are determined by partial differential equations (PDEs) of the reaction–diffusion type, solved to quasi steady-state. In57. A crucial observation was that successful anastomoses during angiogenesis are less probable in 3D, since the moving sprout tips have an additional degree of freedom. Without anastomoses, circulation loops in new vasculature cannot be completed, and blood supply cannot be established. One limitation of the 2D/3D model is that the frequency of anastomoses is a function of grid size. This simplistic assumption permitted enough successful anastomosis encounters in two dimensions. However, the corresponding probability of successful encounters in three dimensions is reduced, leading to less efficient angiogenesis and inhibited tumour growth. We remark that reducing the grid size in three dimensions, or assuming a higher initial vessel density did not entirely ameliorate this limitation.Challenges in extending the 2D framework to three dimensions were examined by Perfahl et al.60. Further, Moreira-Soares et al.61 demonstrated how vessel sprouts are guided towards each other even at larger scales of distance. These authors argued that such mechanisms are particularly relevant in three-dimensional space where “without it the network has a reduced number of anastomosis”. Practically, a successful anastomosis event is implemented as follows. A random number 57. The algorithm for the random number generator is based on63.In contrast to the aforementioned approach, we instead propose the following formulation wherein the probability of anastomosis is taken to be a function of the distance –6 cm2/s, see64); max = 3.3 (mmol/l h for cancer cells), this is an estimate from measurements in mmol/(106 cells min) with hepatic cells carried out with standard six-well tissue culture plates32. For normal cells rmax = 0.66 (mmol/l h). For cancer cells as well as normal cells KM = 0.2 mmol/l65.In order to integrate the dynamic metabolic model of HEPG2 cells within this framework, we need to explicitly include glucose as the primary metabolite. Extracellular glucose is taken as a diffusible species, that is supplied by the vasculature and taken up by tumour and healthy cells. The PDE governing the spatial distribution of extracellular glucose concentration , products (P), activators (A) and inhibitors (I). Superscripts (0) in Eqs.   S), prodThe intracellular models are coupled to the extracellular environment through the concentration of the diffusible substances. Extracellular oxygen acts directly on the cell-cycle and the intracellular VEGF/p53 model. The feedback from the intracellular to the diffusible scale is implemented via secretion of VEGF (as the source term for the VEGF-PDE). Glucose is coupled to the metabolic network via a frozen steady-state and a one-way coupling (from extracellular to intracellular). Therefore, the local concentration values of glucose at the cell position are used to update the intracellular ODE model and to proceed in time. The coupling is also visualized in Fig. 57. In57 the model was extended from 2D to 3D and it was necessary to adapt the computational algorithm to the additional degree of freedom. Simulating large tumours is challenging due to the added computational demands created by the third dimension. Issues of memory allocation arise due an increase in the number of agents (cells) in model simulations. The inherent stochasticity of our hybrid model would require averaging over multiple realizations to extract robust conclusions. The latter aspects were neglected in the simulation results presented below, due to the large computational effort. The results presented are therefore representative results that show the underlying mechanisms of our model.The computational framework used to simulate the hybrid multiscale model of vascularized tumour growth is described in detail in67, thereby utilizing the strengths of diverse computational hardware. A closer look at the structure of the model , GPU-based methods are optimal. An adaptation of CUDA-FORTRAN was required to implement the modified Bulirsch-Stoer method on parallelized GPUs.The system of ODEs describing cell-cycle dynamics at the subcellular level was solved using the modified Bulirsch-Stoer method69, and solved on parallelized CPUs. The force-balance equations describing blood flow in the vascular network were solved on the CPU using the Liebmann successive displacement method70. The biased random walk of sprout tips resulting in angiogenesis was also implemented on a single CPU. Model simulations were visualized using the Coin3D implementation of the Open Inventor Application Programming Interface (API) and OpenGL71.PDEs governing the diffusive transport and reactions governing extracellular oxygen, VEGF and glucose concentrations were discretized using the second order Alternating Direction Implicit method72. Since the time constants of the reactions in the metabolic network model ))72. Sinc57 and Meier et al.33.All simulation parameters, applied here, are given in Perfahl et al.functional vascular network formation in three dimensions.We first simulated the growth of a vascular tumour in three dimensions, to illustrate the effect of the new approach for capturing anastomosis. A typical model simulation is shown in Fig. The central metabolism model was integrated into the 3D model of vascularized tumour growth as described in the methods section. Since reaction rates in the metabolic network model (Eqs.  and 5)))5)) are 73The spatio-temporal distributions of 2 nutrients, glucose and oxygen, mirror the increase in vessel density caused by tumour-induced angiogenesis. Briefly, hypoxia and hypoglycemia in poorly vascularized areas of the tumour induced VEGF secretion by tumour cells. The resulting angiogenesis largely restored nutrient supply, leaving only a smaller outer region that was nutrient deficient, as reflected by a thin layer of quiescent tumour cells. Visual validation of our model comes from the predicted concentration profiles of lactate within the tumour. Elevated lactate levels closely mirror high glucose levels  and pyruvate are predicted to be highly spatially heterogenous. In contrast, the citric acid cycle input, Acetyl-CoA, demonstrates less variability across the tumour. The relatively uniform Acetyl-CoA concentration is explained by the conversion of excess pyruvate (the precursor of Acetyl-CoA) into lactate, so that spatial gradients of lactate vary even though those of Acetyl-CoA do not. We remark that a relatively constant Acetyl-CoA concentration will lead to a steady rate of flux through the citric acid cycle, assuring ready availability of important precursors e.g. for the synthesis of fatty acids, in cancer cells75. The predicted heterogeneity in glucose concentration in our model simulations is in good qualitative agreement with PET scan measurements of 18F-FDG taken from a xenograft model of non-small cell lung cancer75. 18F-FDG uptake is directly related to glucose metabolism in lung cancer76.Visual validation for our model comes from comparing predictions of glucose concentration profiles with experimental data reported in53. Our simulation results reflect this increased flux by the 6-phosphogluconate concentration, which provides biological support to our model because NADPH produced by this pathway is used by cancer cells to scavenge reactive oxidative species (ROS). Additionally, rapidly dividing cancer cells require NADPH/reducing potential for anabolic reactions such as lipid and cholesterol synthesis74.We also plot the spatial distribution of 6-phosphogluconate, an intermediary of the pentose-phosphate pathway, in Fig. To illustrate the potential of our model in elucidating cancer metabolism, we plot three-dimensional distributions for selected metabolites in Fig. 54. Therefore, our over-arching goal is to bridge the conceptual gap between tumour-level metabolic processes, and the known metabolic processes occurring within individual cancer cells. To this end, we extended a computational framework here wherein a dynamic metabolic model was integrated with a hybrid 3D multiscale model of vascularized tumour growth.Variability in oxygen and glucose concentrations drive heterogeneity in the metabolic phenotype of cancer cells during tumour progression. This can significantly impact tumour response to anti-neoplastic agents, especially when drug response is dependent on the metabolic state of individual cancer cellsWe reported here the first three-dimensional model of intracellular metabolism dynamics as they respond to microenvironmental cues within a heterogeneous growing tumour. Model simulations predicted a high degree of spatial heterogeneity in key metabolites within the growing tumour. Such models, once validated, can be employed in the future to optimize targeted metabolism-based therapies.53. The need for experimentally verified dynamic models of cancer metabolism on which to test novel therapeutics has been highlighted in one of the first reviews focused on this topic81. In addition to identifying the main controlling steps for targeted drug development, our approach for linking the 3D vascularized tumour model with the dynamic metabolic model permits simulation and optimization of different therapeutic application of such drugs. The objective of these simulations could be the development of treatment strategies as a function of the progression of the tumour, repeating therapies and /or design of combination therapies in the case of treatments that combine more than one therapeutic agent. In spite of problems in clinical trials for treatment with metabolic inhibitors there are promising results in the literature for improving the application of mathematical tumour models for personalized tumour growth prediction. A recently published review82 focusing at the expert opinion on the topic of clinical application of these inhibitors reveals that although there is an increasing number of publications on the subject of metabolic inhibitors, the majority of these approaches are restricted to preclinical studies (particularly cell cultures) and only a few of them have been successfully transformed into clinical applications of cancer treatment. The important conclusion of the authors: “Toxicity of normal cells and high dosage required for the current inhibitors remain the showstopper”. As such, the necessary data from clinical trials are not available and we are unable to compensate for this deficit. Our concept for the application of the model for clinical application aims at simulations of alternative strategies to solve the above- mentioned problems of toxicological effects on healthy cells. A promising possibility for liver tumours would be the application of direct injection into the arterial inflow, as is already practiced with TACE therapy. We have previously used the tumour model for satisfactorily simulations of this therapy62. To improve the model-based predictions, we are currently working on expanding the modeling of the hexagonal blood supply of the liver module. This significant improvement in the imaging of vascularization is currently being incorporated in a further manuscript. Finally, it is planned to couple these model extensions with the dynamic model of the metabolism and to use them for the simulation of clinical therapies for the utilization of the inhibitors.For instance, the module for the dynamic model of metabolism can be used to carry out a model-based analysis ) to identify suitable targets for drug development14. This is a complex problem, not least since the mapping from genes to phenotype is not one-to-one but is probably many-to-one or even many-to-many. Sophisticated mathematical modeling could potentially help elucidate this “genotype-to-phenotype mapping”. Consequently, planned future work aims to connect the models developed here, with additional genetic sources of intra-tumour heterogeneity and extend the model to genetically and metabolically diverse tumour types. Most experiments and integrated computer models jointly tackling metabolism and genetics are carried out in microorganisms where parameter values are easier to acquire88. However, the results reported here can motivate targeted experiments on metabolism and cancer cell genetics, which can in turn be used to refine the model.An important – and yet often overlooked – aspect in cancer models is the huge information gap between phenotype and genotype. That is, how gene expression and molecules influence the behaviour of a (cancer) cell90 have noted the challenges in using clinical imaging data for this purpose. Nonetheless, significant advances in experimental methods to image metabolic processes in three-dimensions93 render validation of the simulation results of the comprehensive model feasible.Complementing these advances at the cellular level, tissue-level experiments are needed to validate the vascular component of these hybrid models. Yankeelov et al.


Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with  Computer models are invaluable tools in capturing and systematizing new knowledge, especially for the complex phenomena found in biology. Genome-scale metabolic models (GEMs) are computational models that compile information about the entirety of known metabolic functions in a given organism or cell type. GEMs typically contain a listing of genes, enzymes, and reactions, and relationships of dependence between these. For biochemical reactions, information about substrate, product, and stoichiometry is included in their model representation, i.e. the consumption and production rates for the involved compounds. Based on how compounds participate in different reactions, it is possible to infer a metabolic network: a bipartite network connecting the reactions and metabolites . An addi−1 gCDW−1) [Computational models help identify inconsistencies in our current understanding of the phenomena they model. They also predict novel system behavior or connections, and aid in the design of experiments , 7–10. AIn the years since its inception, FBA and related approaches have given rise to a number of derivatives and modifications . Two modWhile first introduced in 1994 , dFBA waSeveral software packages have been built that enable the performance of simple dFBA –21, but A notable exception is the modeling framework COMETS, which hosts a range of functionalities and provides interfaces for several popular programming languages to its open-source code, although most of that code is implemented in Java , 21. OveMost biochemical reactions need to be catalyzed by enzymes to proceed at a physiologically relevant rate . As an enzyme both has a finite rate of catalysis and a mass, this places a constraint on the possible productivity of a given amount of cells. While there are a few different approaches to incorporate this as a limitation in FBA , 13, 22,A common application of FBA is to study the interaction between a given organism and its environment over time, such as in the case of a batch fermentation . While i−1. As a model of this type operates in a gCDW−1 capacity, this becomes the transient amount of different metabolites needed for 1 gCDW to produce 1 gCDW h−1, without explicitly considering the compounding effects of exponential growth [Also required for accurate simulation is the BOF, which is defined as a pseudo-reaction, listing the amount of different (pseudo-) metabolites needed per hour to produce a growth rate of 1 hl growth –6. Yet, l growth  before wIn the following, we expand decFBA using the SOA approach by explicitly adding constraints on how rapidly the mass fraction occupied by enzymes can be reallocated, named enzyme change constraints (ecc). We demonstrate how this has significant effects on modeling dynamics by showing its impact on GEM behavior during simulation of generic diauxic growth on glucose. Finally, we proceed to test the impact of the ecc on a GEM’s ability to recreate a specific fermentation experiment, and how the ecc can help to explain observed behavior.E. coli BW25113 (from the Keio collection [E. coli), was carried out in a 3 L Eppendorf NewBrunswik BioFlo 115 bioreactor in batch setup, using minimal glucose media as specified in −1 at 500 rpm stirring until equilibrium. Stirring was then coupled to DO, ensuring an oxygen saturation in the medium of ≥ 40%, with 200 rpm and 1000 rpm as lower and upper bounds. Analysis of the off-gas composition and flow-rate was performed using a ThermoScientific Prima BT Benchtop MS, which was calibrated before each run.A carbon-limited batch fermentation of llection )  measurements and medium analysis, and every 30–60 minutes for dry weight. The OD was measured using a spectrophotometer at a wavelength of 600 nm by sampling 5 mL. An aliquot of the sample was diluted with purified water such that the measured OD was between 0.2 and 0.6. The medium samples were taken as 2 mL aliquots from the OD samples. They were spun at 6700 RCF for 2 min, the supernatant was sterile filtered (0.2 μm polyethylensulfon syringe filters) and stored at −20°C until it was analyzed. For dry weight measurements 10 mL were sampled, washed (centrifuged and redissolved) once with 5 mL 0.9% NaCl before being centrifuged again and redissolved in purified ion-free water, and added to a dried and weighed aluminum pan. The pans were completely dried (for ≥ 36 h at 120°C) before being weighed again.et al. [The quantification of medium constituent concentrations was performed by NMR, using ERETIC2  in the Bet al.  and was 2O with 0.75% TSP (purchased from Sigma), before 600 μL of the resulting solution was added to a 5 mm 7 inch NMR tube. It was then analyzed using a 600 MHz (14.1 T) Bruker NMR spectrometer running with the H2O + 10% D2O solvent setting and water suppression . The acquisition parameters were: 4 dummy scans, 32 scans, SW 20.8287 ppm, O1 2820.61 Hz, TD 65536, TE 298.0 K, D1 4 s, AQ 2.621 439 9 s. P1 was calibrated for each sample to ensure accurate quantification. A creatine solution (70 mM in D2O-TSP solution) was used as an external standard, quantified using the singlet at ∼ 3 ppm. Compounds responsible for peaks in the spectra were identified based on reference 1H-NMR spectra available in the Human Metabolome Database (HMDB) [α-Glucose doublet at ∼ 5.2 ppm, as these peaks can be affected by the water suppression sequence [The samples were thawed and mixed 1:10 De (HMDB) –37, as we (HMDB)  and publsequence . InsteadE. coli iJO1366 [iJO1366* as presented by Bekiaris [https://www.gnu.org/software/glpk/). Using the glpk solver, the feasTol parameter had to be lowered from the default 1e − 6 to 1e − 9 to prevent numerical issues with the algorithm placing limits on enzyme reallocation.The GEM used for all modeling was the  iJO1366  model, oBekiaris . The codBekiaris , and thehttps://github.com/emikar/FermentationSim. Anyone is permitted and encouraged to freely copy the code and implement their own modifications as needed. Details for the software can be found in The code produced and used in the paper is available in iJO1366* E. coli model, was therefore considered suitable for this work. Before tuning, all exchange reaction bounds were set to ±1000 mmol gCDW−1 h−1. As specified in section GEM, COBRA and code availability, the Matlab program used for the simulation is detailed in The batch fermentation performed as part of this work displays the well-known phenomenon of overflow metabolism . ReproduIn order to produce a dFBA scenario corresponding to the batch fermentation, the following tuning steps were performed:As micronutrients are not supposed to be limiting in the given situation, exhaustion of the various ions and trace minerals was disabled , in the listed order.−1 h−1 .The central parameter in constraining the change in enzyme composition is implemented in the enzyme change constraint (ecc). This parameter limits the total change in enzyme composition with time, and is given in g gCDWBefore the ecc can be applied, a method for attaining the enzyme mass distribution is needed. In order to infer enzyme compositions, the flux through each reaction is multiplied by the corresponding bottom element of the sMOMENT stoichiometric matrix (S-matrix) and then multiplied by −1. The bottom element gives the molecular weight of the corresponding enzyme divided by the enzyme’s turnover number. We get the total amount of mass currently dedicated to that enzyme per gCDW by multiplying this bottom element with the flux through the corresponding reaction. Multiplication by −1 corrects for the fact that the bottom row is all-negative in the sMOMENT formulation .vi is limited by the amount of enzyme gi dedicated to the enzyme, and its effective catalytic rate kcat,i. The amount of enzyme gi is in turn limited by the amount of mass that can be allocated to that enzyme, and the enzyme’s molecular weight MWi. Expressed mathematically  catalyzing reaction i in g mol−1, kcat,i is the turnover number of enzyme (complex) i, and gi is enzyme “concentration” in mmol gCDW−1.Any given enzyme-catalyzed flux  & Klamt ), we getIf we assume the cell utilizes all its available enzyme in an optimal manner, we get:gi is purely implicit during computation. The mass for each enzyme is calculated directly from the reaction flux multiplied by the molecular weight of a given enzyme (complex) divided by its turnover number.Using this equation, it is possible to keep track of the enzyme composition of the cell. Note that in this formulation, −1 h−1), grams per gram of cell dry weight per hour) dedicated to each reaction. Subsequently, the ecFBA problem (without ecc constraints) is solved once, before the resulting enzyme composition is checked against the former composition. The difference in enzyme composition is calculated by multiplying the number of hours in the time step by the enzyme change limit, given in g gCDW−1 h−1. If the difference is smaller than or equal to the amount of change permitted for that time step, the enzyme composition vector is updated to match the solution. If, however, the difference is greater than the limit for that time step, the enzyme composition is calculated by weighted linear interpolation. This interpolation occurs between the former enzyme composition and the optimal one, and a new ecFBA problem is solved with upper bounds on the flux values corresponding to this new interpolated enzyme composition.We apply an ecc rate limit by storing the enzyme composition as a vector. This vector has a length equal to the number of reactions, and specifies the amount of enzyme , and finally added enzyme change constraints (decFBAecc). We selected a simple test scenario: a 1 L reactor with an initial biomass of 0.1 gCDW growing on 10 mM glucose. The bounds on glucose and oxygen uptake rates are specified in iJO1366* with the protein-pool constraint increased to allow arbitrarily high fluxes. This effectively inactivates the enzyme constraint, resulting in standard dFBA. Second, we ran a version of the same GEM where the protein-pool constraint remained at its default setting, thus performing standard decFBA. Third, and finally, we ran a version of the model where the rate of enzyme redistribution was constrained to a plausible rate value. This results in what we have called decFBAecc.First, to get a regular dFBA equivalent, we used a version of −1, while the terminal biomass values were measured to 8.2 gCDW L−1. The experimental results, including sampling history and raw data from the fermentor’s monitoring software and off-gas analyzer are given in the During the exponential growth phases, growth rates were measured to 0.66 hE. coli on glucose [The fermentation data are plotted in Figs 2 and 3, where they are compared with different simulation results. As can be seen, the fermentation follows a typical timeline for diauxic growth of  glucose , with thIn order to get stronger signatures for the NMR medium analysis, high concentrations were used during the fermentation. As a side effect of this, the concentration of acetate seemed to reach saturation earlier than predicted by the computational simulations. This is potentially linked to thermodynamic feedback in the Pta-AckA pathway .In 2 maximum uptake rate to 15 mmol gCDW−1 h−1 and the glucose maximal uptake rate to 10.5 mmol gCDW−1 h−1. The constraints are listed in For these simulations, we set the O−1 of enzyme. Here, glucose is rapidly depleted as the amount of biomass grows and acetate is excreted, before the model fully depletes the glucose and then also rapidly depletes the acetate.The first parameter set “dFBA”,  correspoThe second parameter set , and logarithmic. We found that a logarithmic function provided an excellent fit, but that the parameters from our regression model did not allow our model to reach the experimentally measured concentration of external acetate.E. coli used, or due to differences in medium or exact growth environment. The expression we devised to fit our data is given in ExAc,max is used as the upper bound on acetate excretion, and cAc is the concentration of acetate in the environment. VAc,max = 2 is the max excretion rate for acetate, while cAc,s = 13 is the external acetate concentration at saturation.The difference could owe to differences in the strains of iJO1366 model. It depletes glucose too rapidly and produces an acetate peak that is too high. Terminal biomass is not too far off, however. In The base dFBA computational modeling, as seen in −1 and 7.9 mmol gCDW−1 h−1 respectively, while on acetate (after the diauxic shift) they are 0.25 h−1 and 10 mmol gCDW−1 h−1.After all tuning steps are done, the growth rate and carbon source consumption on glucose are 0.65 hFinally, we investigated the effect on the diauxic shift, from glucose to acetate, of applying a constraint on the rate of change in the cell’s enzyme composition, as formulated in the decFBAecc method see . Here, oThe resulting simulation outputs are plotted in The more constrained the reallocation of enzymatic mass becomes, the slower the rate of acetate depletion approaches its optimum. This has a consequence of lowering the terminal biomass, as more acetate is consumed for energy by the non-growth associated maintenance sink function before it can be used as an energy and carbon source to fuel growth. This is to be expected, as the optimal enzyme distribution for utilization of glucose is not the same as the optimal enzyme distribution for utilization of acetate.In this particular case, a rate of replacing 2% of total biomass worth of enzymes per hour produces the best fit with experimental data according to our analyses, with a root mean square deviation (RMSD) of 1.04. The entire interval 1.8–6.1% for the value of the ecc appeared to fit experimental data quite well, with an RMSD below 1.10.It should be noted that the ecc rate is likely to be highly dependent on rate-limiting components in the enzyme reallocation process, i.e. gene expression, which should place a lower bound on how rapidly this can occur. A more accurate model would also have the ecc rate represented as a vector of separate values for different reactions, though a lack of appropriate data makes this infeasible at present.With the general approach used for decFBAecc, one would expect a similar effect for diauxic shifts involving other substrates, such as glucose/glycerol or glucose/lactose. In their paper from 2006, Bettenbrock et al. modeled diauxic shifts on a number of substrate combinations, with a focus on the signal transduction processes involved . This moE. coli as a case study, which we attempt to recreate using our computational simulation framework.In this work, we have presented a computationally robust and flexible framework for simulating fermentation with dynamic flux balance analysis. We use this framework to make iterative changes in computational constraints applied to a simple example of diauxic growth on glucose. This approach illustrates the consequences of adding constraints on the rate of reallocation of enzyme. Based on these findings, we perform a simple fermentation experiment for We have two primary findings. First, we find that applying constraints on the rate of enzyme mass reallocation, in addition to applying constraints on total mass allocation to enzymes, produces markedly different modeling results for key processes of interest. The new constraints have a sound rationale, and allow the model in this case to better capture experimental observations. One key consequence is that they markedly change the timing at which maximum biomass is reached during diauxic growth. They do not, however, markedly change the terminal amount of biomass in the considered case, meaning their utility in verifying a BOF may be limited. Another key consequence is that they drastically alter the rates and timings of substrate depletion during a change in growth environment, such as a diauxic shift. This could have important implications for industrial production of added-value compounds. And as the method is relatively straightforward to apply, at least for organisms for which high-quality GEMs exist, it can be readily employed for such purposes.E. coli in environments with high initial concentrations of glucose. This agrees with the findings of Enjalbert et al. [Second, we strengthen the notion that non-linear feedback dynamics on acetate excretion are necessary to capture the behavior of t et al. , and hasS1 FileAn Excel file detailing the nutrient composition of the medium used in the fermentation.(XLSX)Click here for additional data file.S2 FileA PDF manual containing specifications and user instructions for the fermentation simulation program written in Matlab.(PDF)Click here for additional data file.S3 FileA zip file containing the Matlab source code.(ZIP)Click here for additional data file.S4 FileExcel file detailing the fermentation, including protocol and sampling history.(XLSX)Click here for additional data file.


Despite the apparent simplicity of the microbial community involved in biogas upgrading, the dynamics behind most of the interspecies interaction remain obscure. To understand the role of the microbial species in CO2 fixation, the biofilm generated during the biogas upgrading process has been selected as a case study. The present work investigates via genome-centric metagenomics, based on a hybrid Nanopore-Illumina approach the biofilm developed on the diffusion devices of four ex situ biogas upgrading reactors. Moreover, genome-guided metabolic reconstruction and flux balance analysis were used to propose a biological role for the dominant microbes.Carbon fixation through biological methanation has emerged as a promising technology to produce renewable energy in the context of the circular economy. The anaerobic digestion microbiome is the fundamental biological system operating biogas upgrading and is paramount in power-to-gas conversion. Carbon dioxide  were widespread (~ 67% of tested experiments), while others  had a scattered distribution. Genome-scale metabolic models of the microbial community were built with boundary conditions taken from the biochemical data and showed the presence of a flexible interaction network mainly based on hydrogen and carbon dioxide uptake and formate exchange.The combined microbiome was composed of 59 species, with five being dominant ; the metagenome-assembled genomes representing these species were refined to reach a high level of completeness. Genome-guided metabolic analysis appointed Limnochordia sp. GSMM975 and Methanothermobacter wolfeii GSMM957. Finally, it suggested species interactions to be based on formate and amino acids exchanges.Our work investigated the interplay between five dominant species within the biofilm and showed their importance in a large spectrum of anaerobic biogas reactor samples. Flux balance analysis provided a deeper insight into the potential syntrophic interaction between species, especially Video AbstractThe online version contains supplementary material available at 10.1186/s40168-022-01311-1. The concentration of the microbial community used to rescale the fluxes was defined based on the measure of volatile suspended solid obtained growing a simplified hydrogenotrophic community in BA medium  and QOUT,T the total theoretical gas output rate [L/LR.d]. As described by Bassani et al. PCH4 indicates the CH4 production rate, YCH4 the CH4 yield, nH2 and nCO2 the hydrogen and CO2 conversion efficiency, respectively [Figure S1. Representation of the reconstructed genome of Methanothermobacter wolfeii DTU-UNIPD957. Figure S2. Representation of the reconstructed genome of Lymnochordia sp. GSMM975. Figure S2. Representation of the reconstructed genome of Lymnochordia sp. GSMM975. Figure S4. Overall import and export fluxes (> 1 mmol/gDW/hr) of the community from the environment. Figure S5. Metabolic reconstruction of Acetomicrobium sp. GSSM972. Figure S6. Metabolic reconstruction of Methanothermobacter wolfeii GSSM957. Figure S7. Metabolic reconstruction of Firmicutes sp. GSSM974. Figure S8. Metabolic reconstruction of Firmicutes sp. GSSM966. Figure S9. Metabolic reconstruction of Limnochordia sp. GSSM975. Figure S10. Reactions and corresponding genetic evidence of CO2 reduction pathways included in the GSMM. Figure S11. FISH image of the microbial community.Additional file 2: Dataset S1. Genes responsible for EPS biosynthesis or involved in biofilm formation according to Model Seed categories used for targeted investigation through Hidden Markov Models. List of KEGG code, reaction and name of genes searched, combined with the number of copies found in each genome. “*” refers to results retrieved by EggNOG annotation alone, “^” refer to results obtained through HMM search and no annotation refers to results retrieved by RAST.Additional file 3: Dataset S2. Metagenomes clusters used to summarize the abundance profile of MAGs and experiment related unique identifiers of raw reads. The corresponding reference is associated with each experiment used for coverage profile calculation.Additional file 4: Dataset S3. KEGG modules completeness level of MAGs. Integrated results of EggNOG and Diamond annotations.Additional file 5: Dataset S4. Genome scale metabolic models reconstructed for the five most abundant species and their quality assessment. Models are available in SBML format, and the reports generated with MEMOTE in HTML format at 10.6084/m9.figshare.16692028.Additional file 6: Dataset S5. Basic Anaerobic Medium composition used for the community model simulation. “Compounds” are the unique ModelSEED identifier, “Name” the corresponding human readable metabolites and “MaxFlux” the uptake upper bound allowed form the feedstock.Additional file 7: Dataset S6. MAG statistics and phylogenetic classification. General information regarding the recovered 59 MAGs.Additional file 8: Dataset S7. Operons predicted by Operon Mapper in MAGs. Results are available at 10.6084/m9.figshare.16691878.Additional file 9: Dataset S8. Illumina filtered reads and raw nanopore reads quality assessment. Results obtained with FastQC (v.0.11.9) and NanoPlot (v.1.32.1) are available at 10.6084/m9.figshare.19355303.


Streptomyces. Streptomyces radiopugnans was screened in radiation-polluted soil, which has the potential to overproduce geosmin. However, due to the complex cellular metabolism and regulation mechanism, the phenotypes of S. radiopugnans were hard to investigate. A genome-scale metabolic model of S. radiopugnans named iZDZ767 was constructed. Model iZDZ767 involved 1,411 reactions, 1,399 metabolites, and 767 genes; its gene coverage was 14.1%. Model iZDZ767 could grow on 23 carbon sources and five nitrogen sources, which achieved 82.1% and 83.3% prediction accuracy, respectively. For the essential gene prediction, the accuracy was 97.6%. According to the simulation of model iZDZ767, D-glucose and urea were the best for geosmin fermentation. The culture condition optimization experiments proved that with D-glucose as the carbon source and urea as the nitrogen source (4 g/L), geosmin production could reach 581.6 ng/L. Using the OptForce algorithm, 29 genes were identified as the targets of metabolic engineering modification. With the help of model iZDZ767, the phenotypes of S. radiopugnans could be well resolved. The key targets for geosmin overproduction could also be identified efficiently.Geosmin is one of the most common earthy-musty odor compounds, which is mainly produced by  Streptomyces  is an irregular sesquiterpenoid of various actinomycetes and fungi, which has a distinct earthy or musty odor . Geosminptomyces  and seveptomyces , myxobacptomyces , and funptomyces  can prodin China . The genHaemophilus influenzae in 1999. Over 2,000 GSMMs have been published for over 1,000 organisms (Escherichia coli K12 (6 GSMMs) (Saccharomyces cerevisiae S288c (12 GSMMs) (Yarrowia lipolytica CLIB 122 (6 GSMMs)  is a mathematical model, which presents the gene-protein-reaction relationship. GSMM has been being developed for more than 20 years, since the first published GSMM of rganisms . With th6 GSMMs) , Sacchar2 GSMMs) , and Yar6 GSMMs)  have mor6 GSMMs) .S. radiopugnans, a GSMM named iZDZ767 was constructed. Model iZDZ767 contained 1,411 reactions, 1,399 metabolites, and 767 genes. Compared with experimental data, iZDZ767 could achieve 82.1% and 83.3% accuracy for the utilization of different carbon sources and nitrogen sources. In addition, the prediction accuracy of essential genes was 97.6%, compared with the DEG database  were identified as potential targets, which could improve the geosmin synthesis rate using the OptForce algorithm . The protein sequences of S. radiopugnans were downloaded from the UniProt database (https://www.uniprot.org/taxonomy/403935).The genome sequence of 2HPO4, 4 g/L KH2PO4, and 0.5 g/L MgSO4. Before cultivation, the medium pH was adjusted to 7.2 using NH4OH .The fermentation medium (1 L) contained 10 g/L glucose, 4 g/L yeast tract, 4 g/L KS. radiopugnans strain was first cultured on an agar plate. Then, a single colony was selected to be cultured in 50 mL fresh medium until the OD600 reached a value of 0.8. Finally, the strain was transferred into a 500 mL shake-flask containing 50 mL fermentation medium and cultivated at 30°C for 240 h with shaking at 200 rpm.For shake-flask cultivation, the 600 by 0.36 g/L  was first measured at 600 nm with a spectrophotometer. The cell dry weight was then calculated by multiplying OD0.36 g/L   = robustnessAnalysis ] program was run in MATLAB to simulate the effect of the urea uptake rate on the synthesis rate of geosmin.Detailed information is listed in S. radiopugnans, several steps were carried out. First, ModelSEED   . CytoscaiZDZ767 was used to simulate whether 28 carbon sources and 6 nitrogen sources could be utilized. For carbon sources, model iZDZ767 achieved 82.1% (23/28) correction. For the nitrogen sources, there was 83.3% (5/6) agreement with the experimental results, which could not grow with L-Cysteine as the sole nitrogen source (max) was 0.131 h−1, which was only 4.4% lower than the measured growth rate  should be downregulated to decrease the carbon flux of the TCA cycle (fabD gene [(acyl-carrier-protein) S-malonyltransferase] should also be downregulated to limit the flux of fatty acids synthesis. For energy supplements, the nuo gene (NADH-quinone oxidoreductase) was predicted to be knocked out so that more NADH could be supplied for geosmin synthesis.To identify potential targets for the improvement of geosmin production, the OptForce algorithm was used . Accordiegulated . For by-CA cycle . SimilarSalmonella typhimurium and sea urchin embryos  of S. radiopugnans, we analyzed the synthesis mechanism of geosmin and identified the key targets of geosmin synthesis based on the model, which provided a basis for further study of the synthesis of geosmin and the internal mechanism of S. radiopugnans.S. radiopugnans. Based on model iZDZ767, a series of strategies for improving geosmin were proposed. Although S. radiopugnans can make good use of microbial fermentation to produce geosmin, its metabolic network is complex, and the fermentation experiment period is long. It depends on repeated experiments to increase the production of geosmin, and the economic cost is high. Model iZDZ767 can predict the effects of carbon and nitrogen sources on the synthesis rate of geosmin well and provide directions for optimizing the culture conditions of S. radiopugnans. At the same time, the model is used to analyze the algorithm to predict the key targets for improving the geosmin synthesis rate. Through the analysis of these key targets, we found that the key reactions affecting the synthesis of geosmin are mainly divided into two types. One type of reaction is related to the synthesis of geosmin itself, while the other is related to the growth and reproduction of S. radiopugnans. No matter which type of reaction is upregulated, downregulated, or knocked out, the synthesis of geosmin can be effectively improved. Although the effectiveness of these regulation methods has not been proven, they provide the opportunity to study geosmin production by fermentation. In summary, model iZDZ767 is a powerful tool for analyzing and predicting the metabolic pathways and yields of various products in S. radiopugnans, which provides convenient conditions for us to study S. radiopugnans in the field of systems biology.Traditional model construction methods are mainly divided into automatic construction and manual construction. Among them, automatic construction automatically obtains the GSMM of the target strain or plant by uploading the genomic data to the existing tools. To date, researchers have developed many tools, such as ModelSEED , COBRA , and RAV


Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme‐constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well‐known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell‐based processes and, thus, the usefulness of such models.Genome‐scale, constraint‐based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism  S. cerevisiae strains. Besides, we introduced flux sampling as a tool to analyze intracellular flux predictions of ecGEMs, of major importance for model guided strain design.Enzyme constrained Genome Scale Models (ecGEM) add an additional layer of information to traditional GEMs based on the limited capacity of the cells to synthesize proteins. We combined ecGEMs with dynamic Flux Balance Analysis and obtained accurate predictions of extracellular fluxes during chemostat, batch and fed‐batch growth of different  Metabolic engineering introduces heterologous pathways and rewires cell metabolism to increase product yield, titre and productivity  limit the flux of the corresponding reactions. The ecGEM of Saccharomyces cerevisiae enables a more extensive and accurate simulation of microbial physiology including overflow metabolism, stress responses and consumption rates of different carbon sources.Genome‐scale metabolic models (GEM) are mathematical representations of cell metabolism able to establish genotype–phenotype relationships linking genes and enzymes with metabolic reactions. These models are based on annotated genomes and can be expanded to include resource allocation constraints such as maximum membrane surface area or cell volume  is the most common method to simulate genome‐scale metabolism. It uses linear programming to optimise an objective function and has extensively been used to predict cellular growth, product secretion patterns and to develop overproduction strains  and ecYeast8 (ecGEM) with experimental data. We challenged the models to predict changes in cell metabolism  in response to the operation of the reactor, constituting one of the few examples of combination of ecGEM and dFBA. For the first time, we used flux sampling of ecYeast8 to evaluate central carbon metabolic fluxes at a range of growth rates representative of chemostat, fed‐batch and batch growth of S. cerevisiae. We tested how flux sampling can be used to study central metabolic fluxes, of major importance for strain design applications. We provide a set of scripts to easily implement dFBA on traditional and ecGEM as well as a validation dataset containing fermentation‐related data of S. cerevisiae cells growing in chemostat, batch and fed‐batch reactors. We show how the combination of ecGEM, dFBA and flux sampling enables more realistic and faithful designs of industrially relevant cell‐based processes and, thus, increases the usefulness of such models.We used FBA and dFBA to predict growth dynamics of S. cerevisiae cells grown in continuous cultures change their metabolism depending on the dilution rate. At low growth rates, they present a completely aerobic metabolism whereas ethanol production is observed at growth rates higher than the critical dilution rate (Dcrit), process known as the Crabtree effect. Data from chemostat growth of S. cerevisiae strains CBS8066, DS28911 and H1022 was obtained from literature  with different glucose concentrations in the feed. For each dilution rate, we constrained the growth rate of Yeast8 and ecYeast8 and used mass balances to calculate the cell, glucose and by‐product concentrations in the reactors at steady state.Dcrit. The decrease in biomass concentration is also observed in the experimental data, which shows different critical dilution rates for different strains    while all strains are able to grow at dilution rates as high as 0.4 h−1. However, when cells are grown experimentally at dilution rates higher than 0.3 h−1, the dilution rate has to increase in small steps to avoid wash out, indicating cells need time to adapt to high growth rates , the Michaelis‐Menten equation for glucose uptake becomes the limiting factor. The protein availability constraint results in ethanol production by ecYeast8 and a realistic yield of biomass on glucose, overestimated by Yeast8. Although ethanol consumption was allowed during the entire simulation, it was only predicted after glucose depletion . However, during this phase ecYeast8 simulates higher biomass concentration and faster ethanol consumption than the experimental measurements.Simulations using Yeast8 predict no ethanol production, faster glucose consumption and higher cell concentrations than the experimental measurements Fig. . In thesS. cerevisiae CEN PK‐113‐7D and used oxygen uptake and CO2 production rates  as an indication of cell metabolism. This process was simulated using dFBA and model predictions were compared to experimental data. Yeast8 showed higher OUR and CPR as a result of a higher growth rate during the batch phase which resulted in depletion of glucose before the start of the feeding phase. Simulations with ecYeast8 resulted in accurate prediction of OUR, CPR and biomass concentration in the reactor  activity, laboratory evolved to tolerate high glucose concentrations and engineered to produce lactate  and 93% decrease in ecYeast8 (from 0.58 to 0.04 mmol gDW−1 h−1) . To ensure experimentally observed flux through phospho‐fructo kinase and fructose bisphosphate aldolase, the reversible transaldolase reaction was blocked before sampling , succinyl‐CoA synthetase and fumarase; Frick and Wittmann, et al., et al., In general, we observed good agreement between predicted fluxes and experimental measurements. As example, the flux through TCA reactions decrease with growth rate and, at maximum growth rate, sampling results show the operation of the TCA cycle as two different branches  production processes. This model and simulation framework therefore provides the means for more accurate and realistic designs of cell‐based processes increasing their usefulness for industrial applications.et al.  and minimising glucose consumption as objective for FBA optimization (maximise r_1714 for Yeast8 and minimise r_1714_REV for ecYeast8)  equal to the dilution rate  was set as objective to maximise  was constrained based on the glucose concentration in the reactor using a Michaelis‐Menten kinetic equation . The gl−1 h−1 was used in simulations with Yeast8 and the upper bound of these reactions was unconstrained in ecYeast8. Additional simulations with ecYeast8 were performed including specific constraints based on inhibition of substrate uptake by some of the carbon sources  or a positive upper bound (ecYeast8) to their exchange reactions . This upper bound was calculated using a Michaelis‐Menten equation for glucose. For the rest of substrates a lower bound of −10 mmol gDWLactobacillus plantarum  assuming no oxygen accumulation . To analyse intracellular fluxes the median ± the median absolute deviation (MAD) of the valid samples is considered as the predicted flux through a given reaction. Sampling data is presented in https://gitlab.com/saramorenopaz/ecmodels‐predict‐growth‐dynamics‐s.‐cerevisiae.git.The Artificial Centering Hit‐and‐Run (ACHR) Sampler from cobrapy was used to sample the solution space. Before sampling, bounds of the biomass reaction were constrained to the desired growth rate, glucose uptake was set as objective to minimise and the flux through this reaction was constrained to the minimal flux ± 10%. In all cases, the modified model was used and 10 000 samples were taken. Samples which contained fluxes that violated lower and/or upper bounds or the steady state assumption were discarded using achr.validate (Ebrahim Joep Schmitz (JS) is employed by DSM and Vitor A. P. Martins dos Santos (VAPMdS) has interests in LifeGlimmer GmbH.Appendix S1. Supplementary methods.Table S1. Summary of experimental data used in this study. *CEN.PK 113.7D pdc1(‐6.‐2)::loxP pdc5(‐6.‐2)::loxP pdc6(‐6.‐2)::lox P ura3‐52 YEpLpILDH.Table S2. Glucose, biomass and product mass balances in chemostat reactors.Table S3. Glucose, biomass and product mass balances in batch and fed‐batch reactors.Fig. S1. Simulations of OUR (A), CPR (B) and biomass concentration (C) of S. cerevisiae CEN.PK 113.7D grown with an exponential glucose feed. Peaks in experimental OUR and CPR data were caused by sampling of the reactor.Fig. S2. Two‐carbon sources S. cerevisiae DGI342 batch simulations using Yeast8 (A–C), ecYeast8 (D–F) and ecYeast8 with additional regulation (G‐H) compared to experimental data (squares) . Colored areas represent different phases in the process: glucose consumption (orange), sucrose hydrolysis and glucose consumption (green), fructose consumption (red) and mannose consumption (purple).Click here for additional data file.Appendix S2. Experimental data.Click here for additional data file.


Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation.E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways . The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published 13C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites.An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts.The online version contains supplementary material available at 10.1186/s12859-022-05108-9. Escherichia coli was reconstructed in the early 2000s, and has subsequently been updated with various biological functions and improved prediction capabilities . I. I26]. IE. coli reference strains to the model  were deactivated by constraining the flux to zero for this analysis . Additiol-Tyrosine, N-Acetyl-d-Glucosamine, N-Acetyl-d-Galactosamine, N-Acetyl-d-Mannosamine, and allantoin). All five sources did enable growth when succinate was used as a carbon source instead of pyruvate  and in line with predictions in other recently published models (68–84%) . We foun13C-fluxomics data. Fluxes determined by 13C experiments with growth on glucose and gluconate were compared to model-predicted fluxes [After comparing the GEM predictions with the Biolog data, the predictive quality of internal fluxes was validated with previously published d fluxes . The medE. coli K-12 MG1655, one gene was found to be only essential in MG1655, zupT, and three were found to be only essential in EcN, pgsA, can and leuB. The latter two have been experimentally validated under aerobic conditions [. (2021) the gene argF was additionally mentioned to be only essential in EcN, due to the lack of the gene argI in the genome. However we identified two genes in EcN encoding for an ornithine carbamoyltransferase [argF, with locus tags CIW80_16625 and CIW80_16605. During construction, only the first gene was included as argF. Therefore, the second locus tag was included in the model as argI and linked to the same reaction (OCBT).GEMs can be used to predict essential genes. iHM1533 predicted 156 genes and 230 reactions to be essential in anaerobic gut microbiome media  . TherefoAn algorithm called flux scanning with enforced objective function (FSEOF) was applied to identify targets for enhanced secondary metabolite production, while also allowing flux through biomass objective function . The preFurther, we calculated the slope of flux variation of secondary metabolites against predicted FSEOF target reaction. A positive slope denotes increased flux through secondary metabolite as the flux through target reaction is increased and a negative slope denotes a decrease in secondary metabolite flux as the flux through target reaction is increased Fig. : Data S7Lastly, we carried out a biomass sensitivity analysis to measure the impact of the transition from primary to secondary metabolism across the subsystems. For this purpose, we created series of conditions where biomass reaction bounds were constrained to series of values between 0 and 100% of the optimal flux, while keeping the secondary metabolite exchange reaction as the objective function. This allowed us to measure reaction fluxes across all reactions at various stages going from no secondary metabolite production (100% biomasss) to maximum secondary metabolite production (0% biomass). The theoretically allowed flux ranges for all reactions at each biomass constraint were calculated using FVA, whereas the optimal solution flux was calculated using pFBA , a Python package for the generation of species-specific biomass objective functions . Future E. coli has not been a very successful host given the large size of the responsible biosynthetic gene clusters (BGCs) of PKS and NRPS and the high complexity of these modular megaenzymes. EcN is one of the few non-pathogenic E. coli strains harbouring an NRPS and two other hybrid PKS-NRPSs, including the 55-kb large colibactin biosynthetic gene cluster. Hence, the iHM1533 may provide a platform to guide future microbial host design for important PKS and NRPS products in E. coli.In the models used for construction, secondary metabolite biosynthetic pathways were absent or very limited. As a result, the draft model contained only a limited description of these processes. In this work, a more extensive description of the secondary metabolite formation process was defined. Application of the FSEOF algorithm on iHM1533 predicted a total of 219 target reactions to boost production of various secondary metabolites. The fluxes through many of the predicted target reactions changed as the biomass flux was constrained. These target reactions essentially form a set of primary metabolic pathways that enable the switch to secondary metabolites production. As many modular PKS and NRPS products are an important source of antibiotics, there is growing interest in heterologously expressing them in easy to manipulate bacterial hosts. However, 13C fluxomics data). Phenotypic growth data could be predicted with an accuracy of 82.3% and the flux variability analysis validated the ability of predicting internal fluxes. The inability to correctly predict all behaviour is due to the simplification of the model compared to the organism. Multiscale models are gradually developing, in which elements such as enzyme kinetics, 3D structures, or transcriptional regulation are included. For example, Metabolism and macromolecular Expression (ME) models take into account the processes of enzyme synthesis, such as the transcription and translation machinery, folding, and complex formation [Model predictions on fluxes and growth phenotypes were compared to experimental data  was downloaded from the BiGG database in JSON format and reduced by removing all reactions associated with genes that did not have homology to EcN, except S0001, an artificial gene for i.a. spontaneous reactions . SubsequThe draft EcN model was compared to iDK1463 for addition of reactions missing in the draft model. As iDK1463 was based on an older genome sequence, locus tags of the genes differed between the two models. A bidirectional protein blast (BLASTp) was run to correlate the old to the new locus tags. Next, gene names, subsystems, and annotation were updated using the BioCyc and BiGG database , 65.The draft model was corrected for duplicate reactions, based on identical reaction formulas and different versions of the same reaction identifier. The BioCyc database and comparison to related strains was used to identify the correct directionality of reactions. Unbound flux cycles found by Memote that involved energy generation were corrected . Mass anWe searched for biosynthetic reactions of secondary metabolites at the BiGG database containing curated reactions from many different high-quality GEMs. Only enterobactin and salmochelins biosynthetic pathways were covered in the BiGG database. Most of the GEMs at the BiGG database lack a comprehensive description of complex secondary metabolism. Next, we searched for the biosynthetic reactions described in the MetaCyc database. Comprehensive description of the biosynthesis of enterobactin (ENTBACSYN-PWY), salmochelins PWY-8023), aerobactin (AEROBACTINSYN-PWY) and colibactin (PWY-8117) were found. The biosynthetic reactions for the yersiniabactin pathway were still represented by a single lumped reaction PWY-6407). Thus we manually reconstructed yersiniabactin biosynthesis pathways based on chemical steps described in literature [7. Thus w3, aerobaE. coli MG1655 was used [BOFdat , a Python package for the generation of species-specific biomass objective functions was used to generate a new biomass objective function (BOF) based on experimental data . BOFdat was used . The asswas used , 62. ForE. coli Nissle strain in literature  and both plasmids (pMut1 & pMut2) were removed. Cells were streaked from glycerol stock on LB agar, grown overnight at 37 °C and restreaked twice. Individual colonies were picked by inoculation loop and suspended in inoculating fluid (IF-0) at an absorbance of 0.37 at 600 nm . This wa4Cl, 2.0 mM NaH2PO4, 0.25 mM Na2SO4, 0.05 mM MgCl2, 1.0 mM KCl, and 1.0 μM ferric chloride [. [For 319 nutrients of the PM plates, exchange reactions were identified from the BiGG database . The compounds were split up in NH4, PO4, SO4, Mg2+, Ca2+, Na+, K+, Cl−, thiamine and carbon source to define them as media in silico. All reactions described in the 13C experimental data were linked to a reaction in the model.To assess the accuracy of predicted fluxes by the iHM1533 model, experimental data describing fluxes in EcN in minimal synthetic medium with 15 mM of glucose or gluconate was used for comparison . The syn. (2021) [Flux variability analysis (FVA) and a parsimonious FBA were run  .The essentiality of each gene in iHM1533 in gut microbiome media  algorithm applied by using exchange reactions for each secondary metabolite as objectives to predict reaction amplification targets for secondary metabolite production . The slo. .To compare the performance of iHM1533, iDK1463, and iML1515, all three models were run on the Memote web application and scores were compared (Additional file Additional file 1. Data S1.Additional file 2. Data S2.Additional file 3. Supplementary figures and tables.Additional file 4. iHM1533 model.Additional file 5. Data S3.Additional file 6. Data S4.Additional file 7. Data S5.Additional file 8. Data S6.Additional file 9. Data S7.Additional file 10. Data S8.Additional file 11. Data S9.


Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe’s metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast.The fission yeast, IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models. Schizosaccharomyces pombe is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain much less explored. While these two yeasts share some similarities, distinct differences in, e.g., cell cycle regulation  . A GEM iS. pombe have previously been constructed. However, several issues, including incompatibility with current Systems Biology Markup Language (SBML) standards  of the Yeast8.3.3 model with the BOF used in the SpoMBEL1693 model  in the model is 1.28, consistent with experimental measurements . In termW−1 h−1) . FurtherW−1 h−1) . We used=0.1 h−1   plotted on the Y-axis, compared to the experimentally determined values (X-axis). 13C labeling data taken from Klein et al., 2013  uses NADP+ as the co-factor (BiGG reaction identifier GLYCDy). Addition of a NAD+-dependent reaction (GLYCDx) resulted in an improved agreement with experimental data. Download FIG S1, EPS file, 0.2 MB.Flux distributions in carbon-limited chemostat cultures with glucose (left) and glycerol/acetate mixture (right) as carbon sources. l., 2013 . In the Copyright © 2022 Grigaitis et al.2022Grigaitis et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the pomGEM model by predicting growth on a panel of 21 single carbon sources  and lethality of single-gene knockouts . It should be noted that the growth medium used for testing in the study by Choi et al. (in silico) and false negatives (viable only in vivo) were 22.0% and 8.1% of the data set, respectively. We, however, were not able to test the single-gene KOs on previously published reconstructions due to inherent technical issues with these models.We benchmarked the i et al.  is not cSpoMBEL1693 model, where essentiality was assessed in terms of reactions rather than genes. We determined the essentiality  of 2,017 model reactions with gene-protein-reaction (GPRs) associations and mapped the GPRs with the individual genes in the data set of gene KOs  on the true prediction rate achieved by SpoMBEL1693 reconstruction (61.2%)  model of S. cerevisiae (pcYeast)   that canEM model .pcPombe model  of ATP  glucose , to detebe model . We firsS. cerevisiae, glucose enters the cell via facilitated diffusion, while di- or oligosaccharides  are imported into the cell through sugar: H+ symport, leading to additional energetic costs of using these sugars for growth . Thus, we ran a set of model simulations, where we varied the peptide elongation rate S. cerevisiae  . Next, wrevisiae   of the proteome to TCA cycle and oxidative phosphorylation proteins. This suggests that the same constraints limit growth in glucose excess, and we have previously shown that this constraint is the cytosolic proteome capacity  medium with the same parameter values  remains active. In this state, excretion of additional overflow products  is predicted, consistent with the behavior of .34 h−1) . To summS. pombe in a minimal medium . We spe.375 h−1 . Althougpc-models allows for the inspection of proteome allocation in a fine-grained manner; for every enzyme that supports growth by a catalyzing a metabolic flux, a corresponding minimal protein demand can be computed for the  case that all proteins work at their maximal rate: S. cerevisiae pc-modelsS. cerevisiae and compared it to that of S. pombe. Both models predicted and experimentally determined proteome fractions; most of these coarse-grained clusters occupy comparable-sized proteome fractions in both organisms. Also, the deviations between predicted minimal protein demand and experimental protein fraction have similar patterns in both organisms. When looking at predictions, a significant deviation from experimental data is seen in the proteome fraction involved in the metabolism of carbohydrates. The experimentally determined fraction of glycolytic enzymes is 2-fold higher than the predicted minimal demand.We used a manually curated proteome annotation set  to map pS. cerevisiae cultures at the maximal growth rate in minimal medium (batch cultures with excess glucose)  in previously published proteome data of glucose) . It appeS. cerevisiae, to gain insights into the metabolism and physiology of the distantly related fission yeast, S. pombe. As a result, we presented a computational toolbox to investigate fission yeast metabolism at genome scale. Two types of models, in our view, are required to cover this need: a genome-scale metabolic model  and a proteome-constrained (pc-) model .In this study, we used metabolic modeling and data from the well-studied budding yeast, pomGEM, based on a metabolic model of budding yeast S. cerevisiae pomGEM, bS. pombe . An earlol gDW−1  correspol gDW−1) , furtherl gDW−1) .pomGEM model by first predicting growth on single carbon sources  (E. coli (>90%)  , in termi (>90%) , it is apomGEM and using pcYeast as template . Thus, some proteins will be unaccounted for when mapping them to annotated pathways. Improved GPR annotations in future versions of pomGEM would reduce such “lost” mappings.Quantitative differences in proteome composition, especially at individual protein level, between the model and experimental measurements , can be influenced by several factors. First, we consider the minimal protein demand in the model. This assumption ignores any preparatory protein expression, and the predicted protein abundance is highly dependent on the n models , 34. SecpomGEM and pcPombe models is severely hampered by a lack of consistent, high-quality experimental data sets needed to calibrate and validate the models. The hope is that our current effort to provide a computational tool to study S. pombe’s metabolism will stimulate an iterative cycle of hypothesis generation, experimental testing, and model refinement. For S. cerevisiae, its genome-scale model is already in its 8th iteration, with efforts beginning almost two decades ago (S. cerevisiae (pcYeast) was created and could be successfully tested in a number of scenarios, as seen in studies by Elsemman et al.  will beS. pombe could find novel applications in biotechnology, including winemaking  was used to determine growth capacity on different individual carbon sources. Glycerol stocks were prepared from cells grown to saturation in yeast extract peptone dextrose (YPD) medium and stored at −80°C. All cultures were performed at 30°C using EMM2  using the same medium and transferred to 96-well microtiter plates. Per carbon source, 10 technical replicates were included (300 μL per well), along with 5 negative controls . Temperature was set to 30°C and double orbital shaking at 600 rpm was used. OD values were recorded in 5-min intervals at 600 nm for approximately 80 h.ing EMM2  as a base tested  were inoS. pombe was reconstructed with CBMPy MetaDraft  for all reactions in the model. If blocked flux through a reaction resulted in a predicted growth rate 90% or lower of the maximal (wild-type) growth rate, we considered such reaction essential; otherwise, the mutant is considered viable. Only reactions with existing gene-protein-reaction (GPR) associations were considered and compared with experimental data. For GPRs containing an “OR” clause, the experimentally determined essentiality must match for all listed genes (or combinations of) to be assigned either “viable” or “essential.” For GPRs containing an “AND” clause, reaction was assigned “essential” if at least one of the genes was experimentally determined to be essential; “viable” was assigned the same way as for “OR” clauses. Conflicting results or missing essentiality experiments were labeled “ambiguous” and not considered further.S. pombe is provided in the S. pombe from UniProt  was taken. When no The detailed description of reconstruction of the proteome-constrained model of  UniProt . The kin UniProt . For evepcPombe model was simulated using CBMPy 0.8.2 (48) under the Python 3.9 environment with IBM ILOG CPLEX 20.10 and SoPlex 4.0  were collected from PomBase . The pcPPlex 4.0  as the lS. pombe on different carbon sources is provided in pomGEM model, pcPombe model, and the materials used to generate the pcPombe model, together with information required to generate the figures of this manuscript, are available on Zenodo https://doi.org/10.5281/zenodo.6513462.Experimental data on growth of 10.1128/msystems.00423-22.3TEXT S1pcPombe model. Download Text S1, PDF file, 0.1 MB.The documentation of the Copyright © 2022 Grigaitis et al.2022Grigaitis et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Hemicellulose is the second most abundant carbohydrate in lignocellulosic biomass and has extensive applications. In conventional biomass refinery, hemicellulose is easily converted to unwanted by-products in pretreatment and therefore can't be fully utilized. The present study aims to summarize the most recent development of lignocellulosic polysaccharide degradation and fully convert it to value-added bioproducts through microbial and enzymatic catalysis. Firstly, bioprocess and microbial metabolic engineering for enhanced utilization of lignocellulosic carbohydrates were discussed. The bioprocess for degradation and conversion of natural lignocellulose to monosaccharides and organic acids using anaerobic thermophilic bacteria and thermostable glycoside hydrolases were summarized. Xylose transmembrane transporting systems in natural microorganisms and the latest strategies for promoting the transporting capacity by metabolic engineering were summarized. The carbon catabolite repression effect restricting xylose utilization in microorganisms, and metabolic engineering strategies developed for co-utilization of glucose and xylose were discussed. Secondly, the metabolic pathways of xylose catabolism in microorganisms were comparatively analyzed. Microbial metabolic engineering for converting xylose to value-added bioproducts based on redox pathways, non-redox pathways, pentose phosphate pathway, and improving inhibitors resistance were summarized. Thirdly, strategies for degrading lignocellulosic polysaccharides and fully converting hemicellulose to value-added bioproducts through microbial metabolic engineering were proposed. •Hemicellulose is the main carbohydrate of biomass and has valuable applications.•Hemicellulose is underutilized in conventional biomass refinery and pretreatment.•Microbial and enzymatic catalysis were applied for hemicellulose utilization.•Xylose is converted to value-added bioproducts by metabolic engineering. CCRCarbon Catabolite RepressionCAPCatabolite gene-activator proteinACAdenylate cyclaseHPrK/PHPr kinase/phosphorylaseCcpACatabolite control protein APEPPhosphoenolpyruvatePTSCarbohydrate phosphotransferase systemABCATP-binding cassettePKPhosphoketolase pathwayTCATricarboxylic acid cyclexylOxyl operatorCoACoenzyme AEMPEmbden-Meyerhof-Parnas1The extensive use of fossil fuels has brought challenges such as environmental pollution and carbon dioxide emission to the world today . LignoceA wide variety of microorganisms in nature can grow with hemicellulose through different metabolic pathways . Xylose The present review aims to discuss the latest research progress in bioprocess and metabolic engineering to convert hemicellulose to value-added bioproducts. Firstly, microbial metabolic engineering and bioprocess for utilization of lignocellulosic carbohydrates were discussed. Secondly, the metabolism, regulatory, transmembrane transport, CCR mechanism for xylose utilization in microorganisms, and microbial metabolic engineering for converting xylose to value-added bioproducts were summarized. Thirdly, bioprocess for biodegrading biomass polysaccharides and converting hemicellulose to value-added bioproducts through metabolic engineering were proposed.22.1Caldicellulosiruptor can grow at 85゜C with un-pretreated lignocellulose as substrate. Both cellulose and hemicellulose of natural biomass can be assimilated, and interestingly lignin can also be partially solubilized by Caldicellulosiruptor  domain, catalytic domain, carbohydrate-binding modules (CBMs), and linker peptides  for cellulose and hemicellulose degrading have been identified in the genome of  strains . Some expeptides . Compareellulose . In addiradation .Table 1GCaldicellulosiruptor was cultured with un-pretreated lignocellulose as substrate, both extracellular and intracellular GHs were identified and characterized. A bioprocess for converting natural lignocellulose to monosaccharides by sequential hydrolysis with extracellular GHs of Caldicellulosiruptor and commercial cellulase from fungi was developed. With the bioprocess, the unpretreated lignocellulosic biomass was successfully degraded to monosaccharides by sequential enzymatic hydrolysis. The lignocellulose degradation performance of the sequential bioprocess is comparable with that of the traditional process combining chemical or physicochemical pretreatment and enzymatic hydrolysis. With the sequential bioprocess, hemicellulose of biomass can be fully utilized without being converted to un-wanted chemicals Caldicell necator . The ligr bescii . In addistridium .Fig. 1Bi2.22.2.1E. coli, Haloferax volcanii, and some rumen bacteria  for both xylose and glucose besides the proton-driven cotransport systems. S. cerevisiae can use xylose under certain conditions, which is mainly transported through FDS  was significantly improved in the mutant S. cerevisia -Type ABC transporter, which is the first CUT2 family ABC transporter analyzed in the domain of Archaea  also called catabolite gene-activator protein(CAP), the signal metabolite cAMP, adenylate cyclase (AC), and EIIAGlc of glucose transporter are involved in the process (E. coli. The major regulatory protein of CCR is catabolite control protein A (CcpA), and it has been reported that about 10% of gene transcription in B. subtilis is regulated by CcpA E. coli,  process . The CCR by CcpA . CcpA bi by CcpA . The cre by CcpA . In CCR  by CcpA . Besides kinases .2.3.2The existence of CCR effect in microorganisms severely restricts the absorption and metabolism of xylose, so efforts have been made to reduce or eliminate CCR effect. Three strategies have been applied to address the CCR effect and promote xylose utilization, namely disrupting signaling pathways of CCR effect, engineering xylose transport system, and constructing a microbial co-culture system.E.coli was weakened through disrupting ptsG gene of phosphotransferase system (PTS). After being cultured for 48 h, the wild strain consumed only 12.5% xylose after consuming 37.5% glucose, while the mutant strain can consume 37.5% glucose and 37.5% xylose to produce PHASCL , a recombinant strain Ct-pTBA was constructed through the over-expression of genes xylT, xylA, and xlyB from C. acetobutylicum ATCC 824. Compared with the parent strain(CT), the engineered strain Ct-pTBA showed a higher utilization rate of xylose (1.28 g/L·h vs 0.16 g/L·h) in the co-existence of glucose, and also produced more butyric acid (0.53 g/L·h vs 0.26 g/L·h)  was constructed. At 72 h, the parent strain consumed 33% glucose and 4% xylose, the mutant strain(Δrok7B7) consumed 25% glucose and 38% xylose, and the production of avermectin and oligomycin A were increased (To co-utilize glucose and xylose in 6 g/L·h) . The mecncreased .Clostridium beijerinckii and S. cerevisiae was developed. In the co-culture system, xylose and glucose were co-utilized, and the xylose consumption was increased by 32.99% compared with the single culture system of C. beijerinckii F-6  pathway, isomerase pathway, pentose phosphoketolase (PK) pathway, weimberg pathway, and dahms pathway  Cheudje. Most fume fungi , and plame fungi . The isome fungi . Pentoseme fungi  is a majstridium . Throughutylicum . Weimbernd so on . Dahms pnd so on .Fig. 2Me3.23.2.1S. cerevisiae to convert lignocellulosic hydrolysates to ethanol  compared with 0.83 g/(L·h) of the original strain  gene. To promote NADPH regeneration, the NADP+ dependent D-glyceraldehyde-3-phosphate dehydrogenase (NADP-GAPDH) (EC1.2.1.13) from Kluyveromyces lactis was also expressed. Compared with the original strain, ethanol production of the engineered S. cerevisiae from xylose increased by 50% -acetoin and ethylene glycol from xylose based on Weimberg pathway. The seven-step synthesis with in situ coenzyme regeneration was constructed using xylose dehydrogenase, xylonolactonase, xylonate dehydratase, 2-keto-3-deoxy-D-xylonate aldolase, lactaldehyde reductase, α-acetolactate synthase, and α-acetolactate decarboxylase. In the ATP-free and cell-free synthetic system, 99.2% of xylose was consumed and (R)-acetoin with stereoisomeric purity of 99.5% was obtained under the optimum condition  and poly(d-lactate-co-glycolate-co-d-2-hydroxybutyrate) production from xylose by introducing Dahms pathway. The metabolic flux towards Dahms pathway was then modulated, and 6.93 g/L polymer was produced from xylose in fed-batch fermentation of engineered E. coli  were then overexpressed. The results showed that the overexpression of XI or XK increased xylose utilization and lipid accumulation in the engineered Mucor circinelloides , transketolase (TKL), ribose 5-phosphate ketol-isomerase (RKI), and ribulose 5-phosphate epimerase (RPE) of PP pathway promoted xylose utilization  of lignocellulose for ethanol production. To increase the fermentation temperature of S. cerevisiae for ethanol production, the genes of PP pathway from S. cerevisiae and thermostable Kluyveromyces marxianus were co-expressed. It was found that xylose metabolism was improved by the expression of thermostable PP pathway genes, and ethanol productivity reached 0.51 g/L/h at 38 °C with engineered S. cerevisiae  was obtained using xylose and glucose as substrates  under heterologous conditions compared to the control strain under photoautotrophic conditions , organic acids, lignin derivatives, and phenols produced can inhibit the following microbial fermentation. Some natural microorganisms such as analyzed . Furfuraentified . It has ncreased .S. cerevisiae to inhibitors, an engineered S. cerevisiae was therefore constructed by expressing genes of spermidine synthesis and disrupting polyamine transport protein. The engineered S. cerevisiae can synthesize spermidine using lignocellulosic hydrolysates with furan derivatives and acetic acid  by a syntrophic consortium of engineered  to FDCA . The trae strain .Pseudomonas putida was engineered to utilize the components, genes of dmp monooxygenase and meta-cleavage pathway were constitutively expressed, the native chaperones clpB, groES, and groEL and alcohol dehydrogenase were overexpressed, the pathways for utilization of furfural, acetone, and aromatic compounds were incorporated. The constructed P. putida strain can utilize 89% (w/w) of carbon in the mixture of lignocellulose CFP  and major facilitator superfamily (MFS) in S. cerevisiae. It was found that the expression of PDR18 gene of ABC transporter can increase the ethanol tolerance of S. cerevisiae and ethanol production  of lignocellulose for biofuel production, a carbon-rich and toxic wastewater containing phenol, methanol, aromatic compounds, and furfural were produced. lose CFP . The grooduction  (Table 24Caldicellulosiruptor showed promising applications in lignocellulose degradation and the efficiency of which can be improved through metabolic engineering and bioreactor design. The partial solubilization of lignin is crucial for lignocellulose degradation, the corresponding enzymes and metabolic pathways need to be further studied (As the main component of biomass, developing efficient and clean strategies to fully utilize hemicellulose is of great significance to biomass refineries. The solubilization of cellulose, hemicellulose, and lignin of biomass by thermophilic anaerobic bacteria  studied . TransmeIn the fermentation of lignocellulosic hydrolysates for bioproduct synthesis, the inhibitors produced in the pretreatment process need to be degraded or removed. A large number of inhibitors are produced in conventional chemical or physicochemical pretreatment, the compositions of which vary depending on different pretreat conditions and compositions of lignocellulose. Although the degradation or utilization of some inhibitors has been studied, some of which such as lignin-derived chemicals still can not be fully degraded and utilized, it would be helpful to design novel pathways to metabolize the lignin-derived aromatics. A bioprocess integrating sequential biomass solubilization with thermophilic bacteria and thermostable enzymes and microbial metabolic engineering for converting xylose to value-added bioproducts were proposed . DevelopThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.