Stoichiometric Model Research Articles

Improve the accuracy of FT-NIR for determination of zearalenone content in wheat by using the characteristic wavelength optimization algorithm

The research contributes a unique method to achieve high-precision quantification of zearalenone (ZEN) in wheat, significantly improving accuracy in the analysis. Fourier transform near infrared spectroscopy (FT-NIR) was employed to capture the spectral information of wheat with different mildew degrees. Three feature selection models, competitive adaptive reweighted sampling (CARS), support vector machine-recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble-least absolute shrinkage and selection operator (MFE-LASSO) were introduced to processed pre-processed near-infrared spectral data and established partial least squares (PLS) regression according to the selected features. The outcomes indicated that the optimal generalization performance was achieved by the PLS model optimized through the MFE-LASSO model. The root mean square error of prediction (RMSEP) was 18.6442 μg·kg-1, coefficient of predictive determination (RP2) was 0.9545, and relative percent deviation (RPD) was 4.3198. According to the results, it is feasible to construct a stoichiometric model for the quantitative determination of ZEN in wheat by using FT-NIR combined with feature selection algorithm, and this method can also be extended to the detection of various molds in other cereals in the future.

Density discrepancy between transit-timing variations and radial velocity: Insights from the host star composition

Context. The determination of planetary densities from the masses derived with the radial velocity (RV) and transit-timing variation (TTV) methods reveals discrepancies. Specifically, planets detected through RV exhibit higher densities than those detected through TTV, even though their radii are similar. Understanding the origins of these discrepancies is crucial and timely, especially with upcoming ground- and space-based missions dedicated to exoplanet research.Aims. We explored the possibility that the discrepant mass/densities in the TTV and RV populations might be linked to the properties of the environments in which these planets are formed.Methods. For the largest currently available sample of FGK-type stars hosting low-mass TTV and RV planets, we determined the host star abundances. Then, by employing a simple stoichiometric model, we used these abundances to estimate the iron-to-silicate mass fraction (firon) and the water-mass fraction (wf) of the protoplanetary disks. We also calculated the kinematic properties of the host stars.Results. We observed an indication that the hosts of TTV planets have slightly higherfironand lowerwfvalues than their RV counterparts. This suggests that TTV planets (without considering their atmospheres) are denser than RV planets on average, which implies that larger atmospheres on TTV planets are required to account for their overall lower densities. However, we also note differences in the properties of the planets, such as their orbital periods, and variations in the quality of the spectroscopic data, which may have an impact on these results.Conclusions. Exploring the TTV-RV mass and/or density discrepancy through a chemical analysis of the host star holds promise for future research, particularly with larger sample sizes and higher-quality data. Meanwhile, the provided detailed host star abundances can be employed to study the composition of the planets within the current sample, thereby contributing to a better understanding of the aforementioned discrepancy.

Exploration and Application of DNA-Binding Proteins to Make a Versatile DNA-Protein Covalent-Linking Patch (D-Pclip): The Case of a Biosensing Element.

DNA-protein complexes are attractive components with broad applications in various research fields, such as DNA aptamer-enzyme complexes as biosensing elements. However, noncovalent DNA-protein complexes often decrease detection sensitivity because they are highly susceptible to environmental conditions. In this study, we developed a versatile DNA-protein covalent-linking patch (D-Pclip) for fabricating covalent and stoichiometric DNA-protein complexes. We comprehensively explored the database to determine the DNA-binding ability of the candidates and selected UdgX as the only uracil-DNA glycosylase known to form covalent bonds with DNA via uracil, with a binding efficiency >90%. We integrated a SpyTag/SpyCatcher protein-coupling system into UdgX to create a universal and convenient D-Pclip. The usability of D-Pclip was shown by preparing a stoichiometric model complex of a hemoglobin (Hb)-binding aptamer and glucose oxidase (GOx) by mixing at 4 °C. The prepared aptamer-GOx complexes detected Hb in a dose-dependent manner within the clinically required detection range in buffer and human serum without any washing procedures. D-Pclip covalently connects any uracil-inserted DNA sequence and any SpyCatcher-fused protein stoichiometrically; therefore, it has a high potential for various applications.

A novel experimental method to determine substrate uptake kinetics of gaseous substrates applied to the carbon monoxide-fermenting Clostridium autoethanogenum.

Syngas fermentation has gained momentum over the last decades. The cost-efficient design of industrial-scale bioprocesses is highly dependent on quantitative microbial growth data. Kinetic and stoichiometric models for syngas-converting microbes exist, but accurate experimental validation of the derived parameters is lacking. Here, we describe a novel experimental approach for measuring substrate uptake kinetics of gas-fermenting microbes using the model microorganism Clostridium autoethanogenum. One-hour disturbances of a steady-state chemostat bioreactor with increased CO partial pressures (up to 1.2 bar) allowed for measurement of biomass-specific CO uptake- and CO2 production rates ( , ) using off-gas analysis. At a pCO of 1.2 bar, a of -119 ± 1 mmol g-1 X h-1 was measured. This value is 1.8-3.5-fold higher than previously reported experimental and kinetic modeling results for syngas fermenters. Analysis of the catabolic flux distribution reveals a metabolic shift towards ethanol production at the expense of acetate at pCO 0.6 atm, likely to be mediated by acetate availability and cellular redox state. We characterized this metabolic shift as acetogenic overflow metabolism. These results provide key mechanistic understanding of the factors steering the product spectrum of CO fermentation in C. autoethanogenum and emphasize the importance of dedicated experimental validation of kinetic parameters.

The impact of water storage capacity on plant dynamics in arid environments: A stoichiometric modeling approach

Plants in arid environments have evolved many strategies to resist drought. Among them, the developed water storage tissue is an essential characteristic of xerophytes. To clarify the role of water storage capacity in plant performance, we originally formulate a stoichiometric model to describe the interaction between plants and water with explicit water storage. Via an ecological reproductive index, we explore the effects of precipitation and water storage capacity on plant dynamics. The model possesses saddle–node bifurcation and forward or backward bifurcation, and the latter may lead to the emergence of alternative stable states between a stable survival state and a stable extinction state. Numerical simulations illustrate the persistence and resilience of plants regulated by soil conditions, precipitation and water storage capacity. Our findings contribute to the botanical theory in the perspectives of environmental change and plant water storage traits.

Stoichiometric microplastics models in natural and laboratory environments

Microplastics pose a severe threat to marine ecosystems; however, relevant mathematical modeling and analysis are lacking. This paper formulates two stoichiometric producer-grazer models to investigate the interactive effects of microplastics, nutrients, and light on population dynamics under different settings. One model incorporates optimal microplastic uptake and foraging behavior based on nutrient availability for natural settings, while the other model does not include foraging in laboratory settings. We establish the well-posedness of the models and examine their long-term behaviors. Our results reveal that in natural environments, producers and grazers exhibit higher sensitivity to microplastics, and the system may demonstrate bistability or tristability. Moreover, the influences of microplastics, nutrients, and light intensity are highly intertwined. The presence of microplastics amplifies the constraints on grazer growth related to food quality and quantity imposed by extreme light intensities, while elevated phosphorus input enhances the system’s resistance to intense light conditions. Furthermore, higher environmental microplastic levels do not always imply elevated microplastic body burdens in organisms, as organisms are also influenced by nutrients and light. We also find that grazers are more vulnerable to microplastics, compared to producers. If producers can utilize microplastics for growth, the system displays significantly greater resilience to microplastics.

Dynamics of a stoichiometric phytoplankton-zooplankton model with season-driven light intensity.

Chemical heterogeneity significantly influences the dynamics of phytoplankton and zooplankton interactions through its effects on phytoplankton carrying capacity and zooplankton ingestion rates. Our central objective of this study was to develop and examine a nonautonomous model of phytoplankton-zooplankton growth, which incorporates season-driven variations in light intensity and chemical heterogeneity. The dynamics of the system is characterized by positive invariance, dissipativity, boundary dynamics, and internal dynamics. Subsequently, numerical simulations were conducted to validate the theoretical findings and to elucidate the effects of seasonal light intensity, nutrient availability, and zooplankton loss rates on phytoplankton dynamics. The outcomes of our model and analysis offer a potential explanation for seasonal phytoplankton blooms.

Population dynamics of a stoichiometric aquatic tri-trophic level model with fear effect

In this paper, a stoichiometric aquatic tri-trophic level model is proposed and analyzed, which incorporates the effect of light and phosphorus, as well as the fear effect in predator–prey interactions. The analysis of the model includes the dissipativity and the existence and stability of equilibria. The influence of environmental factors and fear effect on the dynamics of the system is particularly investigated. The key findings reveal that the coexistence of populations is positively influenced by an appropriate level of light intensity and/or the dissolved phosphorus input concentration; however, excessive levels of phosphorus input can disrupt the system, leading to chaotic behaviors. Furthermore, it is found that the fear effect can stabilize the system and promote the chances of population coexistence.

Enucleation of the C. elegans embryo revealed dynein-dependent spacing between microtubule asters.

The intracellular positioning of the centrosome, a major microtubule-organizing center, is important for cellular functions. One of the features of centrosome positioning is the spacing between centrosomes; however, the underlying mechanisms are not fully understood. To characterize the spacing activity in Caenorhabditis elegans embryos, a genetic setup was developed to produce enucleated embryos. The centrosome was duplicated multiple times in the enucleated embryo, which enabled us to characterize the chromosome-independent spacing activity between sister and non-sister centrosome pairs. We found that the timely spacing depended on cytoplasmic dynein, and we propose a stoichiometric model of cortical and cytoplasmic pulling forces for the spacing between centrosomes. We also observed dynein-independent but non-muscle myosin II-dependent movement of centrosomes in the later cell cycle phase. The spacing mechanisms revealed in this study are expected to function between centrosomes in general, regardless of the presence of a chromosome/nucleus between them, including centrosome separation and spindle elongation.

Reconciling contrasting effects of nitrogen on host immunity and pathogen transmission using stoichiometric models.

Hosts rely on the availability of nutrients for growth, and for defense against pathogens. At the same time, changes in host nutrition can alter the dynamics of pathogens that rely on their host for reproduction. For primary producer hosts, enhanced nutrient loads may increase host biomass or pathogen reproduction, promoting faster density-dependent pathogen transmission. However, the effect of elevated nutrients may be reduced if hosts allocate a growth-limiting nutrient to pathogen defense. In canonical disease models, transmission is not a function of nutrient availability. Yet, including nutrient availability is necessary to mechanistically understand the response of infection to changes in the environment. Here, we explore the implications of nutrient-mediated pathogen infectivity and host immunity on infection outcomes. We developed a stoichiometric disease model that explicitly integrates the contrasting dependencies of pathogen infectivity and host immunity on nitrogen (N) and parameterized it for an algal-host system. Our findings reveal dynamic shifts in host biomass build-up, pathogen prevalence, and the force of infection along N supply gradients with N-mediated host infectivity and immunity, compared with a model in which the transmission rate was fixed. We show contrasting responses in pathogen performance with increasing N supply between N-mediated infectivity and N-mediated immunity, revealing an optimum for pathogen transmission at intermediate N supply. This was caused by N limitation of the pathogen at a low N supply and by pathogen suppression via enhanced host immunity at a high N supply. By integrating both nutrient-mediated pathogen infectivity and host immunity into a stoichiometric model, we provide a theoretical framework that is a first step in reconciling the contrasting role nutrients can have on host-pathogen dynamics.

Whole-cell metabolic control analysis

Since its conception some fifty years ago, metabolic control analysis (MCA) aims to understand how cells control their metabolism by adjusting the activity of their enzymes. Here we extend its scope to a whole-cell context. We consider metabolism in the evolutionary context of growth-rate maximisation by optimisation of protein concentrations. This framework allows for the prediction of flux control coefficients from proteomics data or stoichiometric modelling. Since genes compete for finite biosynthetic resources, we treat all protein concentrations as interdependent. We show that elementary flux modes (EFMs) emerge naturally as the optimal metabolic networks in the whole-cell context and we derive their control properties. In the evolutionary optimum, the number of expressed EFMs is determined by the number of protein-concentration constraints that limit growth rate. We use published glucose-limited chemostat data of S. cerevisiae to illustrate that it uses only two EFMs prior to the onset of fermentation and that it uses four EFMs during fermentation. We discuss published enzyme-titration data to show that S. cerevisiae and E. coli indeed can express proteins at growth-rate maximising concentrations. Accordingly, we extend MCA to elementary flux modes operating at an optimal state. We find that the expression of growth-unassociated proteins changes results from classical metabolic control analysis. Finally, we show how flux control coefficients can be estimated from proteomics and ribosome-profiling data. We analyse published proteomics data of E. coli to provide a whole-cell perspective of the control of metabolic enzymes on growth rate. We hope that this paper stimulates a renewed interest in metabolic control analysis, so that it can serve again the purpose it once had: to identify general principles that emerge from the biochemistry of the cell and are conserved across biological species.

ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network.

Biochemical networks integrate enzyme-mediated substrate conversions with non-enzymatic complex formation and disassembly to accomplish complex biochemical and physiological functions. The choice of parameters and constraints used in most of these studies is numerically motivated and network-specific. Although sound in theory, the outcomes that result depart significantly from the intracellular milieu and are less likely to retain relevance in a clinical setting. There is a need for a computational tool which is biochemically relevant, mathematically rigorous, and unbiased, and can ascribe functionality to and generate potentially testable hypotheses for a user-defined biochemical network. Here, we present "ReDirection," an R-package which computes the probable dissociation constant for every reaction of a biochemical network directly from a null space-generated subspace of the stoichiometry number matrix of the modeled network. "ReDirection" delineates this subspace by excluding all trivial and redundant or duplicate occurrences of non-trivial vectors, combinatorially summing the vectors that remain and verifying that the upper or lower bounds of the sequence of terms formed by each row of this subspace belong to the open real-valued intervals or or whether the number of terms that are differently signed are almost equal. "ReDirection" iterates these steps until these bounds are consistent and unambiguous for all reactions of the modeled biochemical network. Thereafter, "ReDirection" filters the terms from each row of this subspace, bins them to outcome-specific subsets, sums and maps this to an outcome-specific reaction vector, and computes the p1-norm, which is the probable dissociation constant for a reaction. "ReDirection" works on first principles, does not discriminate between enzymatic and non-enzymatic reactions, offers a biochemically relevant and mathematically rigorous environment to explore user-defined biochemical networks under baseline and perturbed conditions, and can be used to address empirically intractable biochemical problems. The utility and relevance of "ReDirection" are highlighted by numerical studies on stoichiometric number models of biochemical networks of galactose metabolism and heme and cholesterol biosynthesis. "ReDirection" is freely available and accessible from the comprehensive R archive network (CRAN) with the URL (https://cran.r-project.org/package=ReDirection).

Exploring the Sun’s birth radius and the distribution of planet building blocks in the Milky Way galaxy: a multizone Galactic chemical evolution approach

ABSTRACT We explore the influence of the Milky Way galaxy’s chemical evolution on the formation, structure, and habitability of the Solar system. Using a multizone Galactic chemical evolution (GCE) model, we successfully reproduce key observational constraints, including the age–metallicity ([Fe/H]) relation, metallicity distribution functions, abundance gradients, and [X/Fe] ratio trends for critical elements involved in planetary mineralogy, including C, O, Mg, and Si. Our GCE model suggests that the Sun formed in the inner Galactic disc, Rbirth,⊙ ≈ 5 kpc. We also combined a stoichiometric model with the GCE model to examine the temporal evolution and spatial distribution of planet building blocks within the Milky Way galaxy, revealing trends in the condensed mass fraction (fcond), iron-to-silicon mass fraction (firon), and water mass fraction (fwater) over time and towards the inner Galactic disc regions. Specifically, our model predicts a higher fcond in the protoplanetary disc within the inner regions of the Milky Way galaxy, as well as an increased firon and a decreased fwater in the inner regions. Based on these findings, we discuss the potential impact of the Sun’s birth location on the overall structure and habitability of the Solar system.

Aspen Plus model of a downdraft gasifier for lignocellulosic biomass adjusted by Principal Component Analysis

The development and improvement of biomass gasification processes have been accelerated by the use of computational tools for process simulation, exploring their feasibility under different conditions and identifying key parameters that are validated through real experiments, contributing to maturing a gasification technology from laboratory to commercial scale. This work presents an Aspen Plus model of a small-scale downdraft gasifier using biomasses with different elemental and proximate analyses, combining stoichiometric and non-stoichiometric equilibrium approaches. The pyrolysis, oxidation, and reduction processes in the proposed model were separated into distinct steps, with the first one based on Gibbs energy minimization and the latter based on stoichiometric equilibrium models. A representative tar composition, with a production ratio to gas production based on literature, was considered as an input parameter to the model. The obtained data were analyzed using principal component analysis (PCA) to investigate the relations between equivalence ratio (ER), fixed carbon and volatile material ratio FCMV, the CO conversion through methanation (Mconv), carbon conversion via hydrogasification (Hconv), and the mean residual sum of square MRSS. The most significant results obtained from this study include the successful adjustment of the model to represent the gasification of different biomasses, particularly in correcting the H2 and CH4 concentrations. Notably, the adjustment resulted in a substantial reduction of the MRSS deviation from an average of 94.7% to an average of 7.2% concerning reported results in the literature. These findings demonstrate the accuracy of the model in simulating the gasification process and highlight the importance of considering CH4 reactions adjustment for more reliable predictions. The achieved adjustments are valuable insights for advancing gasification technology towards engineering applications.

An energy-efficient and cleaner production of hydrogen by steam reforming of glycerol using Aspen Plus

Hydrogen (H2) is produced in an efficient and sustainable manner using steam reforming of glycerol in this work. Two models are used to provide anticipation about how efficiently H2 can be produced: Model 1, a stoichiometric model, and Model 2, a thermodynamic model. Model 1 helps determine the water/glycerol molar input ratio and reaction pathway. The thermodynamic feasibility of Model 1 is examined using Model 2, which then determines the optimum conditions for maximal H2 generation. Model 2 produced 1275 kmol/h of H2 with an H2 mole fraction of 0.49 at 658 °C and 1 atm of pressure. The reactor's net heat load is greatly reduced through heat integration in this study. The energy required is reduced by 29.5% as compared to when no heat integration is used. The process of separating and eliminating CO2 is accomplished by employing the principles of mass and energy balance in the Aspen Plus.

Quantifying H2 emissions under different nutritional mitigation strategies and its impact on improving the prediction of enteric methane emissions of ruminants

Stoichiometric models that predict methane (CH4) production of ruminants assume that the amount of hydrogen gas (H2) produced during fermentation in the rumen equals the amount of H2 consumed by electron sinks in the rumen and thus that H2 should not be emitted into the environment. However, some studies have demonstrated that H2 emissions occur under practical conditions. In addition, the H2 emissions increase when nutritional mitigation strategies are used. Hence, this study hypothesized that considering H2 emission would improve the prediction of enteric CH4 emission, especially when using mitigation strategies that induce high variation in the H2 emission. The objective of this study was to develop and evaluate the performance of CH4 emission prediction models using H2 emission as an explanatory variable. A database of CH4 and H2 emission (mean treatment data) from ruminants (dairy cattle, growing cattle and sheeps) was built to develop mixed-effects models at three levels of complexity: level 1 - H2 production/yield, DMI, or both; level 2 - level 1 explanatory variables plus chemical composition of the diet (i.e. CP, EE, NDF, OM, or PCO); and level 3 - level 2 explanatory variables plus animal metabolic weight (BW0.75, kg). When all animal categories were grouped, including H2 production improved the performance of CH4 production prediction only in level 1 models for the electron-receptor mitigation strategy, reducing the root mean square of prediction error (RMSPE) by 18 %. For dairy cattle and sheep, dry matter intake was not a significant explanatory variable in level 1 or 2 models. For growing beef cattle, for all mitigation strategies together and the inhibitor-mitigation strategy alone, including H2 production reduced RMSPE by 13 % and 27 %, respectively. Overall, H2 production was included in 60 % of level 1 models and 100 % of level 2 and 3 models, which best predicted CH4 production. Thus, including H2 emission does improve prediction of enteric CH4 emission, especially when mitigation strategies that induce variation in the H2 are used.

Optimal energy and redox metabolism in the cyanobacterium Synechocystis sp. PCC 6803

Understanding energy and redox homeostasis and carbon partitioning is crucial for systems metabolic engineering of cell factories. Carbon metabolism alone cannot achieve maximal accumulation of metabolites in production hosts, since an efficient production of target molecules requires energy and redox balance, in addition to carbon flow. The interplay between cofactor regeneration and heterologous production in photosynthetic microorganisms is not fully explored. To investigate the optimality of energy and redox metabolism, while overproducing alkenes—isobutene, isoprene, ethylene and 1-undecene, in the cyanobacterium Synechocystis sp. PCC 6803, we applied stoichiometric metabolic modelling. Our network-wide analysis indicates that the rate of NAD(P)H regeneration, rather than of ATP, controls ATP/NADPH ratio, and thereby bioproduction. The simulation also implies that energy and redox balance is interconnected with carbon and nitrogen metabolism. Furthermore, we show that an auxiliary pathway, composed of serine, one-carbon and glycine metabolism, supports cellular redox homeostasis and ATP cycling. The study revealed non-intuitive metabolic pathways required to enhance alkene production, which are mainly driven by a few key reactions carrying a high flux. We envision that the presented comparative in-silico metabolic analysis will guide the rational design of Synechocystis as a photobiological production platform of target chemicals.

Stoichiometric model of a fully closed bioregenerative life support system for autonomous long-duration space missions

Bioregenerative life support systems (BLSS) are vital for long-duration and remote space missions to increase mission sustainability. These systems break down human waste materials into nutrients and CO2 for plants and other edible organisms, which in turn provide food, fresh water, and oxygen for astronauts. The central idea is to create a materially closed loop, which can significantly reduce mission mass and volume by cutting down or even eliminating disposable waste. In most BLSS studies only a fraction of the resources, such as food, are provided by the system itself, with the rest taken on board at departure or provided through resupply missions. However, for autonomous long-duration space missions without any possibility of resupply, a BLSS that generates all resources with minimal or no material loss, is essential. The goal of this study is to develop a stoichiometric model of a conceptually fully closed BLSS that provides all the metabolic needs of the crew and organisms. The MELiSSA concept of the European Space Agency is used as reference system, consisting of five interconnected compartments, each inhabited by different types of organisms. A detailed review of publicly available MELiSSA literature from 1989 to 2022 revealed that no existing stoichiometric model met the study’s requirements. Therefore, a new stoichiometric model was developed to describe the cycling of the elements C, H, O, and N through all five MELiSSA compartments and one auxiliary compartment. A compact set of chemical equations with fixed coefficients was established for this purpose. A spreadsheet model simulates the flow of all relevant compounds for a crew of six. By balancing the dimensions of the different compartments, a high degree of closure is attained at steady state, with 12 out of 14 compounds exhibiting zero loss, and oxygen and CO2 displaying only minor losses between iterations. This is the first stoichiometric model of a MELiSSA-inspired BLSS that describes a continuous provision of 100% of the food and oxygen needs of the crew. The stoichiometry serves as the foundation of an agent-based model of the MELiSSA loop, as part of the Evolving Asteroid Starships (E|A|S) research project.

Influence of oxygen uptake through the liver surface on the metabolism of ex vivo perfused liver during hypoxia

The low quality of transplants having undergone hypoxic injury can lead to postoperative complications. The aim of the present research is to estimate, by means of mathematical modeling, how the process of oxygen uptake through the liver surface influences the metabolism of ex vivo perfused liver under hypoxia. The value of oxygen uptake through the surface was established to depend on the degree of oxygenation of the perfusion medium. A decrease in the oxygenation of the perfusion medium resulted in a decreased oxygen uptake through the liver surface. Stoichiometric modeling of the liver metabolism shows that upon the decreased oxygenation of the perfusion medium more energy is required for the process of oxygen uptake through the surface even at a lower level as compared to the normal oxygen supply. The application of the Pareto optimality allows estimating the optimum distribution of the energy resources in liver under ex vivo conditions. Both upon the normal and decreased oxygenation of the perfusion medium, the phenomenon of “free competition” for the resource was observed, with the energy being optimally distributed among all the metabolic fluxes. Moreover, this energy is also spent on the accompanying processes, e.g. for the transport of interstitial fluid.

Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.