Chemical potentials (molar Gibbs energies) are usually extrapolated to the remote physical-chemical reference state and then stored. Subsequent use under in vivo conditions requires a similarly substantial, reverse extrapolation, again with significant potential errors. In order to shrink both extrapolations drastically and thereby enhance both biological meaning and accuracy, we propose a transformation to a more biological reference state: pH = 7, pMg = 3, 99.5% water, with 1 mm each of the additional 'precursors' inorganic phosphate, sulfate, ammonium, and bicarbonate, and with twin temperatures 37 and 25 °C, ionic strength 0.15 m and mm as concentration unit. These precursors substitute for reference compounds alien to biology such as H2 at 1 bar, and solid graphite, sulfur, and phosphorus. The standard chemical potentials are herewith increased by the magnitudes of the chemical potentials of protons, Mg2+, water, and the four precursors, each multiplied by the number of corresponding atoms in the molecule. This defines standard 'metabolic potentials'. We make these potentials findable and accessible as 1360 collated standard chemical potentials for 320 compounds of biochemical interest at the twin metabolic reference states. We do this for 3 reference pH's: We present the metabolic reference state as a convenient anchor, not a universal intracellular milieu. All datasets must continue to report the actual experimental state (T, pH, pMg, I, osmolarity, concentrations), yet aim at (also) reporting parameter values for this anchor state; we supply algorithms to transform between states. This preserves interoperability across diverse organelles, media and between enzymology and chemical engineering, while facilitating reuse.

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.

Terpenoid biosynthesis involves linear prenyl diphosphate intermediates of various chain lengths. These are constructed from 2 C5 precursors, the starter unit dimethylallyl diphosphate (DMADP) and the extender unit, isopentenyl diphosphate (IDP). Isopentenyl diphosphate isomerase (IDI) alters the DMADP:IDP ratio and may furnish a specific blend of C5 precursors appropriate for the length of intermediates being formed in each cellular compartment. We studied IDI in two woody plant species, Norway spruce (Picea abies) and gray poplar (Populus × canescens), whose major terpenoid specialized metabolites are of different sizes. While the catalytic parameters of IDI from each species measured in vitro were in line with the different C5 precursor demands, the DMADP:IDP ratios of both species in vivo did not differ. Moreover, although IDI silencing in both spruce and poplar increased IDP content and significantly decreased the DMADP:IDP ratio, it caused a few significant alterations in the content of downstream terpenoid pathway intermediates or products. Taken together, these results suggest that IDI exercises a limited control over the relative amounts of different size terpenoid products. Nevertheless, the elevated IDP content of both transgenic spruce and poplar lines was associated with dramatically increased emission of isoprenol and isoprenyl acetate. Feeding experiments with cultured poplar plants indicated that these metabolites were derived directly from IDP, and their formation could serve as a metabolic mechanism to reduce high intracellular accumulation of IDP. Such a mechanism can be considered analogous to the formation of isoprene as a way to reduce high concentrations of DMADP.

Biocatalysis needs improved reproducibility and quality of research reporting. Our interdisciplinary team has developed a flexible and extensible metadata catalogue based on STRENDA guidelines, essential for describing complex experimental setups in biocatalysis. The catalogue is available online via GitHub for community use.

Thioredoxin, glutaredoxin and peroxiredoxin systems play central roles in redox regulation, signaling and metabolism in cells. In these systems, reducing equivalents from NAD(P)H are transferred by coupled thiol-disulfide exchange reactions to redoxins which then reduce a wide array of targets. However, the characterization of redoxin activity has been unclear, with redoxins regarded as enzymes in some studies and redox metabolites in others. Consequently, redoxin activities have been quantified by enzyme kinetic parameters in vitro, and redox potentials or redox ratios within cells. By analyzing all the reactions within these systems, computational models showed that many kinetic properties attributed to redoxins were due to system-level effects. Models of cellular redoxin networks have also been used to estimate intracellular hydrogen peroxide levels, analyze redox signaling and couple omic and kinetic data to understand the regulation of these networks in disease. Computational modeling has emerged as a powerful complementary tool to traditional redoxin enzyme kinetic and cellular assays that integrates data from a number of sources into a single quantitative framework to accelerate the analysis of redoxin systems.

The plastidic 2-C-methylerythritol 4-phosphate (MEP) pathway supplies the precursors of a large variety of essential plant isoprenoids, but its regulation is still not well understood. Using metabolic control analysis (MCA), we examined the first enzyme of this pathway, 1-deoxyxylulose 5-phosphate synthase (DXS), in multiple grey poplar (Populus × canescens) lines modified in their DXS activity. Single leaves were dynamically labeled with 13CO2 in an illuminated, climate-controlled gas exchange cuvette coupled to a proton transfer reaction mass spectrometer, and the carbon flux through the MEP pathway was calculated. Carbon was rapidly assimilated into MEP pathway intermediates and labeled both the isoprene released and the IDP+DMADP pool by up to 90%. DXS activity was increased by 25% in lines overexpressing the DXS gene and reduced by 50% in RNA interference lines, while the carbon flux in the MEP pathway was 25–35% greater in overexpressing lines and unchanged in RNA interference lines. Isoprene emission was also not altered in these different genetic backgrounds. By correlating absolute flux to DXS activity under different conditions of light and temperature, the flux control coefficient was found to be low. Among isoprenoid end products, isoprene itself was unchanged in DXS transgenic lines, but the levels of the chlorophylls and most carotenoids measured were 20–30% less in RNA interference lines than in overexpression lines. Our data thus demonstrate that DXS in the isoprene-emitting grey poplar plays only a minor part in controlling flux through the MEP pathway.

Computational biology is a diverse research field that has gained increasing importance over the last two decades. Broadly, it aims to apply computational approaches to advance our understanding of biological systems. This can take place on multiple levels, for example, by creating computational models of specific biological systems, by developing algorithms that assist in the analysis of experimental data, or by investigating fundamental biological design principles through modelling. The articles in this special issue highlight and review four such distinct applications of computational biology.

The peroxiredoxins are an important antioxidant protein family and their ability to neutralise oxidants is regularly investigated using horse radish peroxidase in a competition assay system. In this method, the rate constant of a peroxiredoxin is calculated from the fractional inhibition of horse radish peroxidase activity caused by competition with the peroxiredoxin for an oxidant substrate. We developed a model capable of simulating this assay and, using this model, demonstrate that the fractional inhibition calculation significantly and systematically mis-estimates the rate constant under fairly common conditions. We go on to develop a method for fitting simulated assay time-courses to experimental data directly, which significantly outperforms the fractional inhibition method yielding more accurate results. Based on our findings, we recommend using the direct fitting approach to determine peroxidase rate constants from horseradish peroxidase experiments.

A critical feature of the cellular antioxidant response is the induction of gene expression by redox-sensitive transcription factors. In many cells, activating these transcription factors is a dynamic process involving multiple redox steps, but it is unclear how these dynamics should be measured. Here, we show how the dynamic profile of the Schizosaccharomyces pombe Pap1 transcription factor is quantifiable by three parameters: signal amplitude, signal time and signal duration. In response to increasing hydrogen peroxide concentrations, the Pap1 amplitude decreased while the signal time and duration showed saturable increases. In co-response plots, these parameters showed a complex, non-linear relationship to the mRNA levels of four Pap1-regulated genes. We also demonstrate that hydrogen peroxide and tert-butyl hydroperoxide trigger quantifiably distinct Pap1 activation profiles and transcriptional responses. Based on these findings, we propose that different oxidants and oxidant concentrations modulate the Pap1 dynamic profile, leading to specific transcriptional responses. We further show how the effect of combination and pre-exposure stresses on Pap1 activation dynamics can be quantified using this approach. This method is therefore a valuable addition to the redox signalling toolbox that may illuminate the role of dynamics in determining appropriate responses to oxidative stress.

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

Infectious diseases are a significant health burden for developing countries, particularly with the rise of multidrug resistance. There is an urgent need to elucidate the factors underlying the persistence of pathogens such as Mycobacterium tuberculosis, Plasmodium falciparum and Trypanosoma brucei. In contrast to host cells, these pathogens traverse multiple and varied redox environments during their infectious cycles, including exposure to high levels of host-derived reactive oxygen species. Pathogen antioxidant defenses such as the peroxiredoxin and thioredoxin systems play critical roles in the redox stress tolerance of these cells. However, many of the kinetic rate constants obtained for the pathogen peroxiredoxins are broadly similar to their mammalian homologs and therefore, their contributions to the redox tolerances within these cells are enigmatic. Using graph theoretical analysis, we show that compared to a canonical Escherichia coli redoxin network, pathogen redoxin networks contain unique network connections (motifs) between their thioredoxins and peroxiredoxins. Analysis of these motifs reveals that they increase the hydroperoxide reduction capacity of these networks and, in response to an oxidative insult, can distribute fluxes into specific thioredoxin-dependent pathways. Our results emphasize that the high oxidative stress tolerance of these pathogens depends on both the kinetic parameters for hydroperoxide reduction and the connectivity within their thioredoxin/peroxiredoxin systems.

Mathematical modelling is an important approach in metabolic engineering projects to support the design and adaptation of organisms for the needs of biotechnology. Assuming sufficient quality of the model, mechanistic mathematical descriptions of the processes enable the prediction of the systemic impact of metabolic pathway engineering on the organism’s metabolism. In this paper, modelling is used to design the necessary changes in Arabidopsis thaliana metabolism for the production of cis-abienol and isoprene. We developed a kinetic model of the A. thaliana 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, including the production of plastoquinone, chlorophyll side chains and carotenoids. For the purpose of optimisation, it was extended with additional reactions leading to the production of cis-abienol (using geranylgeranyl diphosphate as precursor) and isoprene (using dimethylallyl pyrophosphate as precursor).A total optimisation potential (TOP) based search of reasonable enzyme concentration adjustments yielded sets of parameter combinations, from which those were selected that gave the largest increase in the product flux compared to parameter sets with a smaller number of parameters. Three designs (sets of required concentration changes of particular enzymes) are suggested for each product, based on optimisation of the 2048 possible combinations of adjustable parameters (enzyme concentrations). The resulting optimization of the model forecasts a cis-abienol production of 0.021 nmol.s−1.ml−1 and an isoprene production of 0.048 nmol.s−1.ml−1 in plastids of A. thaliana.

Dimethylallyl diphosphate (DMADP) and isopentenyl diphosphate (IDP) serves as the universal C5 precursors of isoprenoid biosynthesis in plants. These compounds are formed by the last step of the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, catalyzed by (E)-4-hydroxy-3-methylbut-2-en-1-yl diphosphate reductase (HDR). In this study, we investigated the major HDR isoforms of two woody plant species, Norway spruce (Picea abies) and gray poplar (Populus × canescens), to determine how they regulate isoprenoid formation. Since each of these species has a distinct profile of isoprenoid compounds, they may require different proportions of DMADP and IDP with proportionally more IDP being needed to make larger isoprenoids. Norway spruce contained two major HDR isoforms differing in their occurrence and biochemical characteristics. PaHDR1 produced relatively more IDP than PaHDR2 and it encoding gene was expressed constitutively in leaves, likely serving to form substrate for production of carotenoids, chlorophylls, and other primary isoprenoids derived from a C20 precursor. On the other hand, Norway spruce PaHDR2 produced relatively more DMADP than PaHDR1 and its encoding gene was expressed in leaves, stems, and roots, both constitutively and after induction with the defense hormone methyl jasmonate. This second HDR enzyme likely forms a substrate for the specialized monoterpene (C10), sesquiterpene (C15), and diterpene (C20) metabolites of spruce oleoresin. Gray poplar contained only one dominant isoform (named PcHDR2) that produced relatively more DMADP and the gene of which was expressed in all organs. In leaves, where the requirement for IDP is high to make the major carotenoid and chlorophyll isoprenoids derived from C20 precursors, excess DMADP may accumulate, which could explain the high rate of isoprene (C5) emission. Our results provide new insights into the biosynthesis of isoprenoids in woody plants under conditions of differentially regulated biosynthesis of the precursors IDP and DMADP.

Peroxiredoxins play central roles in the detoxification of reactive oxygen species and have been modelled across multiple organisms using a variety of kinetic methods. However, the peroxiredoxin dimer-to-decamer transition has been underappreciated in these studies despite the 100-fold difference in activity between these forms. This is due to the lack of available kinetics and a theoretical framework for modelling this process. Using published isothermal titration calorimetry data, we obtained association and dissociation rate constants of 0.050 µM−4·s−1 and 0.055 s−1, respectively, for the dimer–decamer transition of human PRDX1. We developed an approach that greatly reduces the number of reactions and species needed to model the peroxiredoxin decamer oxidation cycle. Using these data, we simulated horse radish peroxidase competition and NADPH-oxidation linked assays and found that the dimer–decamer transition had an inhibition-like effect on peroxidase activity. Further, we incorporated this dimer–decamer topology and kinetics into a published and validated in vivo model of PRDX2 in the erythrocyte and found that it almost perfectly reconciled experimental and simulated responses of PRDX2 oxidation state to hydrogen peroxide insult. By accounting for the dimer–decamer transition of peroxiredoxins, we were able to resolve several discrepancies between experimental data and available kinetic models.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Plantaricin 423 is produced by Lactobacillus plantarum 423 using the pla biosynthetic operon located on the 8,188-bp plasmid pPLA4. As with many class IIa bacteriocin operons, the pla operon carries biosynthetic genes (plaA, precursor peptide; plaB, immunity; plaC, accessory; and plaD, ABC transporter) but does not carry local regulatory genes. Little is known about the regulatory mechanisms involved in the expression of the apparently regulationless class IIa bacteriocins, such as plantaricin 423. In this study, phylogenetic analysis of class IIa immunity proteins indicated that at least three distinct clades exist, which were then used to subgroup the class IIa operons. It became evident that the absence of classical quorum-sensing genes on mobile bacteriocin-encoding elements is a predisposition of the subgroup that includes plantaricin 423, pediocin AcH/PA-1, divercin V41, enterocin A, leucocin-A and -B, mesentericin Y105, and sakacin G. Further analysis of the subgroup suggested that the regulation of these class IIa operons is linked to transition metal homeostasis in the host. By using a fluorescent promoter-reporter system in Lactobacillus plantarum 423, transcriptional regulation of plantaricin 423 was shown to be upregulated in response to manganese privation. IMPORTANCE Lactic acid bacteria hold huge industrial application and economic value, especially bacteriocinogenic strains, which further aids in the exclusion of specific foodborne pathogens. Since bacteriocinogenic strains are sought after, it is equally important to understand the mechanism of bacteriocin regulation. This is currently an understudied aspect of class IIa operons. Our research suggests the existence of a previously undescribed mode of class IIa bacteriocin regulation, whereby bacteriocin expression is linked to management of the producer's transition metal homeostasis. This delocalized metalloregulatory model may fundamentally affect the selection of culture conditions for bacteriocin expression and change our understanding of class IIa bacteriocin gene transfer dynamics in a given microbiome.

The accompanying paper (Uys et al., in silico Plants, 2021: diab013) presented a core model of sucrose accumulation within the advection–diffusion–reaction framework, which is able to capture the spatio-temporal evolution of the system from a set of initial conditions. This paper presents a sensitivity analysis of this model. Because this is a non-steady-state model based on partial differential equations, we performed the sensitivity analysis using two approaches from engineering. The Morris method is based on a one-at-a-time design, perturbing parameters individually and calculating the influence on model output in terms of elementary effects. Fourier amplitude sensitivity test (FAST) is a global sensitivity analysis method, where all parameters are perturbed simultaneously, oscillating at different frequencies, enabling the calculation of the contribution of each parameter through Fourier analysis. Overall, both methods gave similar results. Perturbations in reactions tended to have a large influence on their own rate, as well as on directly connected metabolites. Sensitivities varied both with the time of the simulation and the position along the sugarcane stalk. Our results suggest that vacuolar sucrose concentrations are most sensitive to vacuolar invertase in the centre of the stalk, but that phloem unloading and vacuolar sucrose uptake also contribute, especially towards the stalk edges. Sucrose in the phloem was most sensitive to phloem loading at the nodes, but most sensitive to phloem unloading in the middle of the internodes. Sink concentrations of sucrose in the symplast were most sensitive to phloem unloading in the middle of the internodes, but at the nodes cytosolic invertase had the greatest effect.

The glycine conjugation pathway in humans is involved in the metabolism of natural substrates and the detoxification of xenobiotics. The interactions between the various substrates in this pathway and their competition for the pathway enzymes are currently unknown. The pathway consists of a mitochondrial xenobiotic/medium-chain fatty acid: coenzyme A (CoA) ligase (ACSM2B) and glycine N-acyltransferase (GLYAT). The catalytic mechanism and substrate specificity of both of these enzymes have not been thoroughly characterised. In this study, the level of evolutionary conservation of GLYAT missense variants and haplotypes were analysed. From these data, haplotype variants were selected (156Asn > Ser, [17Ser > Thr,156Asn > Ser] and [156Asn > Ser,199Arg > Cys]) in order to characterise the kinetic mechanism of the enzyme over a wide range of substrate concentrations. The 156Asn > Ser haplotype has the highest frequency and the highest relative enzyme activity in all populations studied, and hence was used as the reference in this study. Cooperative substrate binding was observed, and the kinetic data were fitted to a two-substrate Hill equation. The coding region of the GLYAT gene was found to be highly conserved and the rare 156Asn > Ser,199Arg > Cys variant negatively affected the relative enzyme activity. Even though the 156Asn > Ser,199Arg > Cys variant had a higher affinity for benzoyl-CoA (s0.5,benz = 61.2 µM), kcat was reduced to 9.8% of the most abundant haplotype 156Asn > Ser (s0.5,benz = 96.6 µM), while the activity of 17Ser > Thr,156Asn > Ser (s0.5,benz = 118 µM) was 73% of 156Asn > Ser. The in vitro kinetic analyses of the effect of the 156Asn > Ser,199Arg > Cys variant on human GLYAT enzyme activity indicated that individuals with this haplotype might have a decreased ability to metabolise benzoate when compared to individuals with the 156Asn > Ser variant. Furthermore, the accumulation of acyl-CoA intermediates can inhibit ACSM2B leading to a reduction in mitochondrial energy production.