COPASI--a COmplex PAthway SImulator

DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

Directionality Reversal and Shift of Rotational Axis in a Hemithioindigo Macrocyclic Molecular Motor

Molecular motors are central driving units for nanomachinery, and control of their directional motions is of fundamental importance for their functions. Light-driven variants use easy to provide, easy to dose, and waste-free fuel with high energy content, making them particularly interesting for applications. Typically, light-driven molecular motors work via rotations around dedicated chemical bonds where the directionality of the rotation is dictated by the steric effects of asymmetry in close vicinity to the rotation axis. In this work, we show how unidirectional rotation around a virtual axis can be realized by reprogramming a molecular motor. To this end, a classical light-driven motor is restricted by macrocyclization, and its intrinsic directional rotation is transformed into a directional rotation of the macrocyclic chain in the opposite direction. Further, solvent polarity changes allow to toggle the function of this molecular machine between a directional motor and a nondirectional photoswitch. In this way, a new concept for the design of molecular motors is delivered together with elaborate control over their motions and functions by simple solvent changes. The possibility of sensing the environmental polarity and correspondingly adjusting the directionality of motions opens up a next level of control and responsiveness to light-driven nanoscopic motors.

Integrative in vivo analysis of the ethanolamine utilization bacterial microcompartment in Escherichia coli

Bacterial microcompartments (BMCs) are self-assembling protein megacomplexes that encapsulate metabolic pathways. Although approximately 20% of sequenced bacterial genomes contain operons encoding putative BMCs, few have been thoroughly characterized, nor any in the most studied Escherichia coli strains. We used an interdisciplinary approach to gain deep molecular and functional insights into the ethanolamine utilization (Eut) BMC system encoded by the eut operon in E. coli K-12. The eut genotype was linked with the ethanolamine utilization phenotype using deletion and overexpression mutants. The subcellular dynamics and morphology of the E. coli Eut BMCs were characterized in cellula by fluorescence microscopy and electron (cryo)microscopy. The minimal proteome reorganization required for ethanolamine utilization and the in vivo stoichiometric composition of the Eut BMC were determined by quantitative proteomics. Finally, the first flux map connecting the Eut BMC with central metabolism in cellula was obtained by genome-scale modeling and 13C-fluxomics. Our results reveal that contrary to previous suggestions, ethanolamine serves both as a nitrogen and a carbon source in E. coli K-12, while also contributing to significant metabolic overflow. Overall, this study provides a quantitative molecular and functional understanding of the BMCs involved in ethanolamine assimilation by E. coli.IMPORTANCEThe properties of bacterial microcompartments make them an ideal tool for building orthogonal network structures with minimal interactions with native metabolic and regulatory networks. However, this requires an understanding of how BMCs work natively. In this study, we combined genetic manipulation, multi-omics, modeling, and microscopy to address this issue for Eut BMCs. We show that the Eut BMC in Escherichia coli turns ethanolamine into usable carbon and nitrogen substrates to sustain growth. These results improve our understanding of compartmentalization in a widely used bacterial chassis.

Transacylation and hydrolysis of the acyl glucuronides of ibuprofen and its α-methyl-substituted analogues investigated by 1H NMR spectroscopy and computational chemistry: Implications for drug design

Drugs and drug metabolites containing a carboxylic-acid moiety can undergo in vivo conjugation to form 1-β-O-acyl-glucuronides (1-β-O-AGs). In addition to hydrolysis, these conjugates can undergo spontaneous acyl migration, and anomerisation reactions, resulting in a range of positional isomers. Facile transacylation has been suggested as a mechanism contributing to the toxicity of acyl glucuronides, with the kinetics of these processes thought to be a factor. Previous 1H NMR spectroscopic and HPLC-MS studies have been conducted to measure the degradation rates of the 1-β-O-AGs of three nonsteroidal anti-inflammatory drugs (ibufenac, R-ibuprofen, S-ibuprofen) and a dimethyl-analogue (termed here as "bibuprofen"). These studies have also determined the relative contributions of hydrolysis and acyl migration in both buffered aqueous solution, and human plasma. Here, a detailed kinetic analysis is reported, providing the individual rate constants for the acyl migration and hydrolysis reactions observed in buffer for each of the 4 AGs, together with the overall degradation rate constants of the parent 1-β-O-AGs. Computational modelling of the reactants and transition states of the transacylation reaction using density functional theory indicated differences in the activation energies that reflected the influence of both substitution and stereochemistry on the rate of transacylation/hydrolysis.

Kinetic analysis of prothrombinase assembly and substrate delivery mechanisms

Prothrombinase complex, composed of coagulation factors Xa (FXa) and Va (FVa) is a major enzyme of the blood coagulation network that produces thrombin via activation of its inactive precursor prothrombin (FII) on the surface of phospholipid membranes. However, pathways and mechanisms of prothrombinase formation and substrate delivery are still discussed. Here we designed a novel mathematical model that considered different potential pathways of FXa or FII binding (from the membrane or from solution) and analyzed the kinetics of thrombin formation in the presence of a wide range of reactants concentrations. We observed the inhibitory effect of large FVa concentrations and this effect was phospholipid concentration-dependent. We predicted that efficient FII activation occurred via formation of the ternary complex, in which FVa, FXa and FII were in the membrane-bound state. Prothrombin delivery was mostly membrane-dependent, but delivery from solution was predominant under conditions of phospholipid deficiency or FXa/FVa excess. Likewise, FXa delivery from solution was predominant in the case of FVa excess, but high FII did not switch the FXa delivery to the solution-dependent one. Additionally, the FXa delivery pathway did not depend on the phospholipid concentration, being the membrane-dependent one even in case of the phospholipid deficiency. These results suggest a flexible mechanism of prothrombinase functioning which utilizes different complex formation and even inhibitory mechanisms depending on conditions.

An adaptable in silico ensemble model of the arachidonic acid cascade

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

Comparative mathematical modeling reveals the differential effects of high-fat diet and ketogenic diet on the PI3K-Akt signaling pathway in heart

BACKGROUND

Obesity is a global health concern associated with increased risk of diseases like cardiovascular conditions including ischemic heart disease, a leading cause of mortality. The ketogenic diet (KD) has potential therapeutic applications in managing obesity and related disorders. However, the intricate effects of KD on diverse physiological conditions remain incompletely understood. The PI3K-Akt signaling pathway is critical for heart health, and its dysregulation implicates numerous cardiac diseases.

METHODS

We developed comprehensive mathematical models of the PI3K-Akt signaling pathway under high-fat diet (HFD) and KD conditions to elucidate their differential impacts and quantify apoptosis. Simulations and sensitivity analysis were performed.

RESULTS

Simulations demonstrate that KD can reduce the activation of key molecules like Erk and Trp53 to mitigate apoptosis compared to HFD. Findings align with experimental data, highlighting the potential cardiac benefits of KD. Sensitivity analysis identifies regulators like Trp53 and Bcl2l1 that critically influence apoptosis under HFD.

CONCLUSIONS

Mathematical modeling provides quantitative insights into the contrasting effects of HFD and KD on cardiac PI3K-Akt signaling and apoptosis. Findings have implications for precision nutrition and developing novel therapeutic strategies to address obesity-related cardiovascular diseases.

Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems

MOTIVATION

Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/C++ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem.

RESULTS

In response, we developed Poincaré and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincaré serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincaré and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-in-time compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincaré and SimBio as valuable tools for the modeling community.

AVAILABILITY AND IMPLEMENTATION

Poincaré and SimBio are released under the MIT license. Their source code is available on GitHub (https://github.com/maurosilber/poincare and https://github.com/hgrecco/simbio) and can be installed from PyPI or conda-forge.

Revealing the Mechanistic Features of an Electrosynthetic Catalytic Reaction and the Role of Redox Mediators through DFT Calculations and Microkinetic Modeling

Organic electrosynthesis is an emerging field that provides original selectivity while adding features of atom economy, sustainability, and selectivity. Electrosynthesis is often enhanced by redox mediators or electroauxiliaries. The mechanistic understanding of organic electrosynthesis is however often limited by the low lifetime of intermediates and its difficult detection. In this work, we report a computational analysis of the mechanism of an appealing reaction previously reported by Mei and co-workers which is catalyzed by copper and employs iodide as redox mediator. Our scheme combines DFT calculations with microkinetic modeling and covers both the reaction in solution and the electrodic steps. A detailed mechanistic scheme is obtained which reproduces well experimental data and opens perspectives for the general treatment of these processes.

Bifurcations in coupled amyloid-β aggregation-inflammation systems

A complex interplay between various processes underlies the neuropathology of Alzheimer's disease (AD) and its progressive course. Several lines of evidence point to the coupling between Aβ aggregation and neuroinflammation and its role in maintaining brain homeostasis during the long prodromal phase of AD. Little is however known about how this protective mechanism fails and as a result, an irreversible and progressive transition to clinical AD occurs. Here, we introduce a minimal model of a coupled system of Aβ aggregation and inflammation, numerically simulate its dynamical behavior, and analyze its bifurcation properties. The introduced model represents the following events: generation of Aβ monomers, aggregation of Aβ monomers into oligomers and fibrils, induction of inflammation by Aβ aggregates, and clearance of various Aβ species. Crucially, the rates of Aβ generation and clearance are modulated by inflammation level following a Hill-type response function. Despite its relative simplicity, the model exhibits enormously rich dynamics ranging from overdamped kinetics to sustained oscillations. We then specify the region of inflammation- and coupling-related parameters space where a transition to oscillatory dynamics occurs and demonstrate how changes in Aβ aggregation parameters could shift this oscillatory region in parameter space. Our results reveal the propensity of coupled Aβ aggregation-inflammation systems to oscillatory dynamics and propose prolonged sustained oscillations and their consequent immune system exhaustion as a potential mechanism underlying the transition to a more progressive phase of amyloid pathology in AD. The implications of our results in regard to early diagnosis of AD and anti-AD drug development are discussed.

The Subtle Mechanism of Nickel-Photocatalyzed C(sp3)-H Cross-Coupling

This computational study revises and reformulates the mechanism for the cross-coupling reaction between chlorobenzene and tetrahydrofuran catalyzed by a Ni complex with the assistance of an Ir photocatalyst. This is a representative process of transition-metal photocatalysis, and variations of it have been reported by different experimental authors. It has been also the subject of previous computational studies, which we revise and extend. Density functional theory (DFT) calculations and microkinetic modeling indicate that the most efficient mechanism takes place through an energy-transfer step and involves a NiIII complex.

Event horizon kinetic Monte Carlo

In this study, we present a novel approach for modeling the dynamics of stochastic processes. The fundamental concept involves constructing a stochastic Markov chain comprising states separated by more than one stochastic event. Initially, the method explores the network of neighboring states connected by stochastic events. This exploration results in a "horizon" of events leading to a set of "boundary" states at the periphery of each local network. Subsequently, the next member in the Markov chain is selected from the "boundary" states based on the probability of reaching each of the "boundary" states for the first time. Meanwhile, the simulation clock is updated according to the time required to reach the boundary for the first time. This can be achieved using an analytical approach, where the probability of reaching each boundary state for the first time is estimated using absorbing conditions for all boundary states in the analytical solution of a master equation describing the local network of states around each current state. The proposed method is demonstrated in modeling the dynamics in networks of stochastic reactions but can be easily applied in any stochastic system whose dynamics can be expressed via the solution of a master equation. It is expected to enhance the efficiency of event-driven Monte Carlo simulations, originally introduced by Gillespie and widely regarded as the gold standard in the field, especially in cases where the presence of events is characterized by different timescales.

Transcriptomic balance and optimal growth are determined by cell size

Cell size and growth are intimately related across the evolutionary scale, but whether cell size is important to attain maximal growth or fitness is still an open question. We show that growth rate is a non-monotonic function of cell volume, with maximal values around the critical size of wild-type yeast cells. The transcriptome of yeast and mouse cells undergoes a relative inversion in response to cell size, which we associate theoretically and experimentally with the necessary genome-wide diversity in RNA polymerase II affinity for promoters. Although highly expressed genes impose strong negative effects on fitness when the DNA/mass ratio is reduced, transcriptomic alterations mimicking the relative inversion by cell size strongly restrain cell growth. In all, our data indicate that cells set the critical size to obtain a properly balanced transcriptome and, as a result, maximize growth and fitness during proliferation.

PEMPS: a phylogenetic software tool to model the evolution of metabolic pathways

BACKGROUND

Metabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype-phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function.

RESULTS

A software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters.

CONCLUSIONS

The PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS .

Synthesis of Styrylbenzazole Photoswitches and Evaluation of their Photochemical Properties

Styrylbenzazoles form a promising yet under-represented class of photoswitches that can perform a light-driven E-Z isomerization of the central alkene double bond without undergoing irreversible photocyclization, typical of the parent stilbene. In this work, we report the synthesis and photochemical study of 23 styrylbenzazole photoswitches. Their thermal stabilities, quantum yields, maximum absorption wavelengths and photostationary state (PSS) distributions can be tuned by changing the benzazole heterocycle and the substitution pattern on the aryl ring. In particular, we found that push-pull systems show large redshifts of the maximum absorption wavelengths and the highest quantum yields, whereas ortho-substituted styrylbenzazole photoswitches exhibit the most favorable PSS ratios. Taking advantage of both design principles, we produced 2,6-dimethyl-4-(dimethylamino)-styrylbenzothiazole, a thermally stable and efficient P-type photoswitch which displays negative photochromism upon irradiation with visible light up to 470 nm to obtain a near-quantitative isomerization with a very high quantum yield of 59 %. Furthermore, 4-hydroxystyrylbenzoxazole was demonstrated to be a pH-sensitive switch which exhibits a 100 nm redshift upon deprotonation. Ortho-methylation of its benzothiazole analogue improved the obtained PSS ratio in its deprotonated state from E : Z=53 : 47 to E : Z=18 : 82. We anticipate that this relatively unexplored class of photoswitches will form a valuable expansion of the current family of photoswitches.

6-Phosphogluconolactonase is critical for the efficient functioning of the pentose phosphate pathway

The metabolic networks of microorganisms are remarkably robust to genetic and environmental perturbations. This robustness stems from redundancies such as gene duplications, isoenzymes, alternative metabolic pathways, and also from non-enzymatic reactions. In the oxidative branch of the pentose phosphate pathway (oxPPP), 6-phosphogluconolactone hydrolysis into 6-phosphogluconate is catalysed by 6-phosphogluconolactonase (Pgl) but in the absence of the latter, the oxPPP flux is thought to be maintained by spontaneous hydrolysis. However, in Δpgl Escherichia coli, an extracellular pathway can also contribute to pentose phosphate synthesis. This raises question as to whether the intracellular non-enzymatic reaction can compensate for the absence of 6-phosphogluconolactonase and, ultimately, on the role of 6-phosphogluconolactonase in central metabolism. Our results validate that the bypass pathway is active in the absence of Pgl, specifically involving the extracellular spontaneous hydrolysis of gluconolactones to gluconate. Under these conditions, metabolic flux analysis reveals that this bypass pathway accounts for the entire flux into the oxPPP. This alternative metabolic route-partially extracellular-sustains the flux through the oxPPP necessary for cell growth, albeit at a reduced rate in the absence of Pgl. Importantly, these findings imply that intracellular non-enzymatic hydrolysis of 6-phosphogluconolactone does not compensate for the absence of Pgl. This underscores the crucial role of Pgl in ensuring the efficient functioning of the oxPPP.

Studying Cation Exchange in {Cr7Co} Pseudorotaxanes: Preparatory Studies for Making Hybrid Molecular Machines

In the design of dynamic supramolecular systems used in molecular machines, it is important to understand the binding preferences between the macrocycle and stations along the thread. Here, we apply 1H NMR spectroscopy to investigate the relative stabilities of a series of linear alkylammonium templated pseudorotaxanes with the general formula [H2NRR'][Cr7CoF8(O2CCH2 tBu)16] by exchanging the cation in solution. Our results show that the pseudorotaxanes are able to exchange threads via a dissociative mechanism. The position of equilibrium is dependent upon the ammonium cation and solvent used. Short chain primary ammonium cations are shown to be far less favourable macrocycle stations than secondary ammonium cations. Collision-induced dissociation mass spectrometry (CID-MS) has been used to look at disassembly of the pseudorotaxanes in a solvent-free environment and stability trends compared to those in acetone-d6. The energy needed to induce 50 % of the precursor ion loss (E50) is used and shows a similar trend to the equilibria measured by NMR. The relative stabilities of these hybrid inorganic-organic pseudo-rotaxanes are different to those of host-guest compounds involving crown ethers and this may be valuable for the design of molecular machines.

Fusion-fission-mitophagy cycling and metabolic reprogramming coordinate nerve growth factor (NGF)-dependent neuronal differentiation

Neuronal differentiation is regulated by nerve growth factor (NGF) and other neurotrophins. We explored the impact of NGF on mitochondrial dynamics and metabolism through time-lapse imaging, metabolomics profiling, and computer modeling studies. We show that NGF may direct differentiation by stimulating fission, thereby causing selective mitochondrial network fragmentation and mitophagy, ultimately leading to increased mitochondrial quality and respiration. Then, we reconstructed the dynamic fusion-fission-mitophagy cycling of mitochondria in a computer model, integrating these processes into a single network mechanism. Both the computational model and the simulations are able to reproduce the proposed mechanism in terms of mitochondrial dynamics, levels of reactive oxygen species (ROS), mitophagy, and mitochondrial quality, thus providing a computational tool for the interpretation of the experimental data and for future studies aiming to detail further the action of NGF on mitochondrial processes. We also show that changes in these mitochondrial processes are intertwined with a metabolic function of NGF in differentiation: NGF directs a profound metabolic rearrangement involving glycolysis, TCA cycle, and the pentose phosphate pathway, altering the redox balance. This metabolic rewiring may ensure: (a) supply of both energy and building blocks for the anabolic processes needed for morphological reorganization, as well as (b) redox homeostasis.

In silico simulation of glycosylation and related pathways

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area.

Use of interactive mathematical simulations in Fundamentals of Biochemistry, a LibreText online educational resource, to promote understanding of dynamic reactions

Biology is perhaps the most complex of the sciences, given the incredible variety of chemical species that are interconnected in spatial and temporal pathways that are daunting to understand. Their interconnections lead to emergent properties such as memory, consciousness, and recognition of self and non-self. To understand how these interconnected reactions lead to cellular life characterized by activation, inhibition, regulation, homeostasis, and adaptation, computational analyses and simulations are essential, a fact recognized by the biological communities. At the same time, students struggle to understand and apply binding and kinetic analyses for the simplest reactions such as the irreversible first-order conversion of a single reactant to a product. This likely results from cognitive difficulties in combining structural, chemical, mathematical, and textual descriptions of binding and catalytic reactions. To help students better understand dynamic reactions and their analyses, we have introduced two kinds of interactive graphs and simulations into the online educational resource, Fundamentals of Biochemistry, a LibreText biochemistry book. One is available for simple binding and kinetic reactions. The other displays progress curves (concentrations vs. time) for simple reactions and complex metabolic and signal transduction pathways. Users can move sliders to change dissociation and kinetic constants as well as initial concentrations and see instantaneous changes in the graphs. They can also export data into a spreadsheet for further processing, such as producing derivative Lineweaver-Burk and traditional Michaelis-Menten graphs of initial velocity (v0) versus substrate concentration.

The dynamic hypoosmotic response of Vibrio cholerae relies on the mechanosensitive channel MscS

Vibrio cholerae adapts to osmotic down-shifts by releasing metabolites through two mechanosensitive (MS) channels, low-threshold MscS and high-threshold MscL. To investigate each channel's contribution to the osmotic response, we generated ΔmscS, ΔmscL, and double ΔmscL ΔmscS mutants in V. cholerae O395. We characterized their tension-dependent activation in patch-clamp, and the millisecond-scale osmolyte release kinetics using a stopped-flow light scattering technique. We additionally generated numerical models describing osmolyte and water fluxes. We illustrate the sequence of events and define the parameters that characterize discrete phases of the osmotic response. Survival is correlated to the extent of cell swelling, the rate of osmolyte release, and the completeness of post-shock membrane resealing. Not only do the two channels interact functionally, but there is also an up-regulation of MscS in the ΔmscL strain, suggesting transcriptional crosstalk. The data reveal the role of MscS in the termination of the osmotic permeability response in V. cholerae.

Long-range charge transfer mechanism of the III2IV2 mycobacterial supercomplex

Aerobic life is powered by membrane-bound redox enzymes that shuttle electrons to oxygen and transfer protons across a biological membrane. Structural studies suggest that these energy-transducing enzymes operate as higher-order supercomplexes, but their functional role remains poorly understood and highly debated. Here we resolve the functional dynamics of the 0.7 MDa III2IV2 obligate supercomplex from Mycobacterium smegmatis, a close relative of M. tuberculosis, the causative agent of tuberculosis. By combining computational, biochemical, and high-resolution (2.3 Å) cryo-electron microscopy experiments, we show how the mycobacterial supercomplex catalyses long-range charge transport from its menaquinol oxidation site to the binuclear active site for oxygen reduction. Our data reveal proton and electron pathways responsible for the charge transfer reactions, mechanistic principles of the quinone catalysis, and how unique molecular adaptations, water molecules, and lipid interactions enable the proton-coupled electron transfer (PCET) reactions. Our combined findings provide a mechanistic blueprint of mycobacterial supercomplexes and a basis for developing drugs against pathogenic bacteria.

Cardiac myosin binding protein-C phosphorylation as a function of multiple protein kinase and phosphatase activities

Phosphorylation of cardiac myosin binding protein-C (cMyBP-C) is a determinant of cardiac myofilament function. Although cMyBP-C phosphorylation by various protein kinases has been extensively studied, the influence of protein phosphatases on cMyBP-C's multiple phosphorylation sites has remained largely obscure. Here we provide a detailed biochemical characterization of cMyBP-C dephosphorylation by protein phosphatases 1 and 2 A (PP1 and PP2A), and develop an integrated kinetic model for cMyBP-C phosphorylation using data for both PP1, PP2A and various protein kinases known to phosphorylate cMyBP-C. We find strong site-specificity and a hierarchical mechanism for both phosphatases, proceeding in the opposite direction of sequential phosphorylation by potein kinase A. The model is consistent with published data from human patients and predicts complex non-linear cMyBP-C phosphorylation patterns that are validated experimentally. Our results suggest non-redundant roles for PP1 and PP2A under both physiological and heart failure conditions, and emphasize the importance of phosphatases for cMyBP-C regulation.

OsTH1 is a key player in thiamin biosynthesis in rice

Thiamin is a vital nutrient that acts as a cofactor for several enzymes primarily localized in the mitochondria. These thiamin-dependent enzymes are involved in energy metabolism, nucleic acid biosynthesis, and antioxidant machinery. The enzyme HMP-P kinase/thiamin monophosphate synthase (TH1) holds a key position in thiamin biosynthesis, being responsible for the phosphorylation of HMP-P into HMP-PP and for the condensation of HMP-PP and HET-P to form TMP. Through mathematical kinetic model, we have identified TH1 as a critical player for thiamin biofortification in rice. We further focused on the functional characterization of OsTH1. Sequence and gene expression analysis, along with phylogenetic studies, provided insights into OsTH1 bifunctional features and evolution. The indispensable role of OsTH1 in thiamin biosynthesis was validated by heterologous expression of OsTH1 and successful complementation of yeast knock-out mutants impaired in thiamin production. We also proved that the sole OsTH1 overexpression in rice callus significantly improves B1 concentration, resulting in 50% increase in thiamin accumulation. Our study underscores the critical role of OsTH1 in thiamin biosynthesis, shedding light on its bifunctional nature and evolutionary significance. The significant enhancement of thiamin accumulation in rice callus upon OsTH1 overexpression constitutes evidence of its potential application in biofortification strategies.

Complexation and disproportionation of group 4 metal (alkoxy) halides with phosphine oxides

Group 4 Lewis acids are well-known catalysts and precursors for (non-aqueous) sol-gel chemistry. Titanium, zirconium and hafnium halides, and alkoxy halides are precursors for the controlled synthesis of nanocrystals, often in the presence of Lewis base. Here, we investigate the interaction of Lewis bases with the tetrahalides (MX4, X = Cl, Br) and metal alkoxy halides (MXx(OR)4-x, x = 1-3, R = OiPr, OtBu). The tetrahalides yield the expected Lewis acid-base adducts MX4L2 (L = tetrahydrofuran or phosphine oxide). The mixed alkoxy halides react with Lewis bases in a more complex way. 31P NMR spectroscopy reveals that excess of phosphine oxide yields predominantly the complexation product, while a (sub)stoichiometric amount of phosphine oxide causes disproportionation of the MXx(OR)4-x species into MXx+1(OR)3-x and MXx-1(OR)5-x. The combination of complexation and disproportionation yields an atypical Job plot. In the case of zirconium isopropoxy chlorides, we fitted the concentration of all observed species and extracted thermodynamic descriptors from the Job plot. The complexation equilibrium constant decreases in the series: ZrCl3(OiPr) > ZrCl2(OiPr)2 ≫ ZrCl(OiPr)3, while the disproportionation equilibrium constant follows the opposite trend. Using calculations at the DFT level of theory, we show that disproportionation is driven by the more energetically favorable Lewis acid-base complex formed with the more acidic species. We also gain more insight into the isomerism of the complexes. The disproportionation reaction turns out to be a general phenomenon, for titanium, zirconium and hafnium, for chlorides and bromides, and for isopropoxides and tert-butoxides.

Gradual ER calcium depletion induces a progressive and reversible UPR signaling

The unfolded protein response (UPR) is a widespread signal transduction pathway triggered by endoplasmic reticulum (ER) stress. Because calcium (Ca2+) is a key factor in the maintenance of ER homeostasis, massive Ca2+ depletion of the ER is a potent inducer of ER stress. Although moderate changes in ER Ca2+ drive the ubiquitous Ca2+ signaling pathways, a possible incremental relationship between UPR activation and Ca2+ changes has yet to be described. Here, we determine the sensitivity and time-dependency of activation of the three ER stress sensors, inositol-requiring protein 1 alpha (IRE1α), protein kinase R-like ER kinase (PERK), and activating transcription factor 6 alpha (ATF6α) in response to controlled changes in the concentration of ER Ca2+ in human cultured cells. Combining Ca2+ imaging, fluorescence recovery after photobleaching experiments, biochemical analyses, and mathematical modeling, we uncover a nonlinear rate of activation of the IRE1α branch of UPR, as compared to the PERK and ATF6α branches that become activated gradually with time and are sensitive to more important ER Ca2+ depletions. However, the three arms are all activated within a 1 h timescale. The model predicted the deactivation of PERK and IRE1α upon refilling the ER with Ca2+. Accordingly, we showed that ER Ca2+ replenishment leads to the complete reversion of IRE1α and PERK phosphorylation in less than 15 min, thus revealing the highly plastic character of the activation of the upstream UPR sensors. In conclusion, our results reveal a dynamic and dose-sensitive Ca2+-dependent activation/deactivation cycle of UPR induction, which could tightly control cell fate upon acute and/or chronic stress.

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of 'bottom up' parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard 'macroscopic' kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and 'microscopic' rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

Systems-wide analysis of A. fumigatus using kinetic modeling of metabolic pathways to identify putative drug targets

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein-protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus's metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.

A Minimalistic Covalent Bond-Forming Chemical Reaction Cycle that Consumes Adenosine Diphosphate

The development of synthetic active matter requires the ability to design materials capable of harnessing energy from a source to carry out work. Nature achieves this using chemical reaction cycles in which energy released from an exergonic chemical reaction is used to drive biochemical processes. Although many chemically fuelled synthetic reaction cycles that control transient responses, such as self-assembly, have been reported, the generally high complexity of the reported systems hampers a full understanding of how the available chemical energy is actually exploited by these systems. This lack of understanding is a limiting factor in the design of chemically fuelled active matter. Here, we report a minimalistic synthetic responsive reaction cycle in which adenosine diphosphate (ADP) triggers the formation of a catalyst for its own hydrolysis. This establishes an interdependence between the concentrations of the network components resulting in the transient formation of the catalyst. The network is sufficiently simple that all kinetic and thermodynamic parameters governing its behaviour can be characterised, allowing kinetic models to be built that simulate the progress of reactions within the network. While the current network does not enable the ADP-hydrolysis reaction to populate a non-equilibrium composition, these models provide insight into the way the network dissipates energy. Furthermore, essential design principles are revealed for constructing driven systems, in which the network composition is driven away from equilibrium through the consumption of chemical energy.

A Stoichiometric Haloform Coupling for Ester Synthesis with Secondary Alcohols

The haloform reaction from methyl ketones to carboxylic acids is one of the oldest known synthetic organic reactions, which has been used in myriad applications over the decades. The corresponding reaction to produce esters is, however, less developed, as the reaction is generally limited to simple, primary alcohols that are used in solvent-level quantities, thereby restricting the complexity of esters that can be directly formed. Herein, we detail the development of a general ester-forming haloform coupling reaction using one equivalent of alcohol. Mechanistic and kinetic modelling studies demonstrated that the key intermediates are formed under equilibrium, which facilitated the development of conditions that are amenable to secondary alcohols.

Drugs Form Ternary Complexes with Human Liver Fatty Acid Binding Protein 1 (FABP1) and FABP1 Binding Alters Drug Metabolism

Liver fatty acid binding protein 1 (FABP1) binds diverse endogenous lipids and is highly expressed in the human liver. Binding to FABP1 alters the metabolism and homeostasis of endogenous lipids in the liver. Drugs have also been shown to bind to rat FABP1, but limited data are available for human FABP1 (hFABP1). FABP1 has a large binding pocket, and up to two fatty acids can bind to FABP1 simultaneously. We hypothesized that drug binding to hFABP1 results in formation of ternary complexes and that FABP1 binding alters drug metabolism. To test these hypotheses, native protein mass spectrometry (MS) and fluorescent 11-(dansylamino)undecanoic acid (DAUDA) displacement assays were used to characterize drug binding to hFABP1, and diclofenac oxidation by cytochrome P450 2C9 (CYP2C9) was studied in the presence and absence of hFABP1. DAUDA binding to hFABP1 involved high (Kd,1 = 0.2 μM) and low (Kd,2 > 10 μM) affinity binding sites. Nine drugs bound to hFABP1 with equilibrium dissociation constant (Kd) values ranging from 1 to 20 μM. None of the tested drugs completely displaced DAUDA from hFABP1, and fluorescence spectra showed evidence of ternary complex formation. Formation of DAUDA-hFABP1-diclofenac ternary complex was verified with native MS. Docking predicted diclofenac binding in the portal region of FABP1 with DAUDA in the binding cavity. The catalytic rate constant of diclofenac hydroxylation by CYP2C9 was decreased by ∼50% (P < 0.01) in the presence of FABP1. Together, these results suggest that drugs form ternary complexes with hFABP1 and that hFABP1 binding in the liver will alter drug metabolism and clearance. SIGNIFICANCE STATEMENT: Many commonly prescribed drugs bind fatty acid binding protein 1 (FABP1), forming ternary complexes with FABP1 and the fluorescent fatty acid 11-(dansylamino)undecanoic acid. These findings suggest that drugs will bind to apo-FABP1 and fatty acid-bound FABP1 in the human liver. The high expression of FABP1 in the liver, together with drug binding to FABP1, may alter drug disposition processes in vivo.

Platinum(II) Phenylpyridyl Schiff Base Complexes as Latent, Photoactivated, Alkene Hydrosilylation Catalysts

Photoactivated catalysts for the hydrosilylation of alkenes with silanes offer temporal control in manufacturing processes that require silicone curing. We report the development of a range of air-stable Pt(II) (salicylaldimine)(phenylpyridyl), [Pt(sal)(ppy)], complexes as photoinitiated hydrosilylation catalysts. Some of these catalysts show appreciable latency in thermal catalysis and can also be rapidly (10 s) activated by a LED UV-light source (365 nm), to give systems that selectively couple trimethylvinylsilane and hexamethylsiloxymethylsilane to give the linear hydrosilylation product. Although an undetectable (by NMR spectroscopy) amount of precatalyst is converted to the active form under UV-irradiation in the timescale required to initiate hydrosilylation, clean and reliable kinetics can be measured for these systems that allow for a detailed mechanism to be developed for Pt(sal)(ppy)-based photoactivated hydrosilylation. The suggested mechanism is shown to have close parallels with, but also subtle differences from, those previously proposed for thermally-activated Karstedt-type Pt(0) systems.

RNA-Templated Peptide Bond Formation Promotes L-Homochirality

The world in which we live is homochiral. The ribose units that form the backbone of DNA and RNA are all D-configured and the encoded amino acids that comprise the proteins of all living species feature an all-L-configuration at the α-carbon atoms. The homochirality of α-amino acids is essential for folding of the peptides into well-defined and functional 3D structures and the homochirality of D-ribose is crucial for helix formation and base-pairing. The question of why nature uses only encoded L-α-amino acids is not understood. Herein, we show that an RNA-peptide world, in which peptides grow on RNAs constructed from D-ribose, leads to the self-selection of homo-L-peptides, which provides a possible explanation for the homo-D-ribose and homo-L-amino acid combination seen in nature.

Selenium catalysis enables negative feedback organic oscillators

The construction of materials regulated by chemical reaction networks requires regulatory motifs that can be stacked together into systems with desired properties. Multiple autocatalytic reactions producing thiols are known. However, negative feedback loop motifs are unavailable for thiol chemistry. Here, we develop a negative feedback loop based on the selenocarbonates. In this system, thiols induce the release of aromatic selenols that catalyze the oxidation of thiols by organic peroxides. This negative feedback loop has two important features. First, catalytic oxidation of thiols follows Michaelis-Menten-like kinetics, thus increasing nonlinearity for the negative feedback. Second, the strength of the negative feedback can be tuned by varying substituents in selenocarbonates. When combined with the autocatalytic production of thiols in a flow reactor, this negative feedback loop induces sustained oscillations. The availability of this negative feedback motif enables the future construction of oscillatory, homeostatic, adaptive, and other regulatory circuits in life-inspired systems and materials.

Quantifying CO2 Insertion Equilibria for Low-Pressure Propene Oxide and Carbon Dioxide Ring Opening Copolymerization Catalysts

While outstanding catalysts are known for the ring-opening copolymerization (ROCOP) of CO2 and propene oxide (PO), few are reported at low CO2 pressure. Here, a new series of Co(III)M(I) heterodinuclear catalysts are compared. The Co(III)K(I) complex shows the best activity (TOF = 1728 h-1) and selectivity (>90% polymer, >99% CO2) and is highly effective at low pressures (<10 bar). CO2 insertion is a prerate determining chemical equilibrium step. At low pressures, the concentration of the active catalyst depends on CO2 pressure; above 12 bar, its concentration is saturated, and rates are independent of pressure, allowing the equilibrium constant to be quantified for the first time (Keq = 1.27 M-1). A unified rate law, applicable under all operating conditions, is presented. As proof of potential, published data for leading literature catalysts are reinterpreted and the CO2 equilibrium constants estimated, showing that this unified rate law applies to other systems.

Universal Glycosyltransferase Continuous Assay for Uniform Kinetics and Inhibition Database Development and Mechanistic Studies Illustrated on ST3GAL1, C1GALT1, and FUT1

Chemical systems glycobiology requires experimental and computational tools to make possible big data analytics benefiting genomics and proteomics. The impediment to tool development is that the nature of glycan construction and mutation is not template driven but rests on cooperative glycosyltransferase (GT) catalytic synthesis. What is needed is the collation of kinetics and inhibition data in a standardized form to make possible analytics of glycan and glycoconjugate synthesis, mechanism extraction, and pattern recognition. Currently, kinetics assays in use for GTs are not universal in processing nucleoside phosphate UDP, GDP, and CMP donor-based glycosylation reactions due to limitations in accuracy and large substrate volume requirements. Here we present a universal glycosyltransferase continuous (UGC) assay able to measure the declining concentration of the NADH reporter molecule through fluorescence spectrophotometry and, therefore, determine reaction rate parameters. The development and parametrization of the assay is based on coupling the nucleotide released from GT reactions with pyruvate kinase, via nucleoside diphosphate kinase (NDK) in the case of NDP-based donor reactions. In the case of CMP-based reactions, the coupling is carried out via another kinase, cytidylate kinase in combination with NDK, which phosphorylates CMP to CDP, then CDP to CTP. Following this, we conduct kinetics and inhibition assay studies on the UDP, GDP, and CMP-based glycosylation reactions, specifically C1GAlT1, FUT1, and ST3GAL1, to represent each class of donor, respectively. The accuracy of calculating initial rates using the continuous assay compared to end point (noncontinuous) assays is demonstrated for the three classes of GTs. The previously identified natural product soyasaponin1 inhibitor was used as a model to demonstrate the application of the UGC assay as a standardized inhibition assay for GTs. We show that the dose response of ST3GAL1 to a serial dilution of Soyasaponin1 has time-dependent inhibition. This brings into question previous inhibition findings, arrived at using an end point assay, that have selected a seemingly random time point to measure inhibition. Consequently, using standardized Km values taken from the UGC assay study, ST3GAL1 was shown to be the most responsive enzyme to soyasaponin1 inhibition, followed by FUT1, then C1GALT1 with IC50 values of 37, 52, and 886 μM respectively.

ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA

In vitro biotransformation (ivBT) facilitated by in vitro synthetic enzymatic biosystems (ivSEBs) has emerged as a highly promising biosynthetic platform. Several ivSEBs have been constructed to produce poly-3-hydroxybutyrate (PHB) via acetyl-coenzyme A (acetyl-CoA). However, some systems are hindered by their reliance on costly ATP, limiting their practicality. This study presents the design of an ATP-free ivSEB for one-pot PHB biosynthesis via acetyl-CoA utilizing starch-derived maltodextrin as the sole substrate. Stoichiometric analysis indicates this ivSEB can self-maintain NADP+/NADPH balance and achieve a theoretical molar yield of 133.3%. Leveraging simple one-pot reactions, our ivSEBs achieved a near-theoretical molar yield of 125.5%, the highest PHB titer (208.3 mM, approximately 17.9 g/L) and the fastest PHB production rate (9.4 mM/h, approximately 0.8 g/L/h) among all the reported ivSEBs to date, and demonstrated easy scalability. This study unveils the promising potential of ivBT for the industrial-scale production of PHB and other acetyl-CoA-derived chemicals from starch.

Stereocontrol via Propeller Chirality in FLP-Catalyzed Asymmetric Hydrogenation

Utilization of chiral frustrated Lewis pairs as catalysts in enantioselective hydrogenation of unsaturated molecules represents a promising approach in asymmetric synthesis. In our effort to improve our current understanding of the factors governing the stereoselectivity in these catalytic processes, herein we examined the mechanism of direct hydrogenation of aromatic enamines catalyzed by a binaphthyl-based chiral amino-borane. Our computational analysis reveals that only one particular conformer of the key borohydride reaction intermediate can be regarded as a reactive form of this species. This borohydride conformer has a well-defined chiral propeller shape, which induces facial selectivity in the hydride transfer to pro-chiral iminium intermediates. The propeller chirality of the reactive borohydride conformer is generated by the axially chiral binaphthyl scaffold of the amino-borane catalyst through stabilizing π-π stacking interactions. This new computational insight can be readily used to interpret the high degree of stereoinduction observed for these reactions. We expect that the concept of chirality relay could be further exploited in catalyst design endeavors.

Mechanism of Asymmetric Homologation of Alkenylboronic Acids with CF3-Diazomethane via Borotropic Rearrangement

Density functional theory calculations have been performed to investigate the mechanism for the BINOL-catalyzed asymmetric homologation of alkenylboronic acids with CF3-diazomethane. The reaction proceeds via a chiral BINOL ester of the alkenylboronic acid substrate. The calculations reveal a complex scenario for the formation of the chiral BINOL-alkenylboronate species, which is the key intermediate in the catalytic process. The aliphatic alcohol additive plays an important role in the reaction. This study provides a rationalization of the stereoinduction step of the reaction, and the enantioselectivity is mainly attributed to the steric repulsion between the CF3 group of the diazomethane reagent and the γ-substituent of the BINOL catalyst. The complex potential energy surface obtained by the calculations is analyzed by means of microkinetic simulations.

Physical Chemistry Models for Chemical Research in the XXth and XXIst Centuries

Thermodynamic hypotheses and models are the touchstone for chemical results, but the actual models based on time-invariance, which have performed efficiently in the development of chemistry, are nowadays invalid for the interpretation of the behavior of complex systems exhibiting nonlinear kinetics and with matter and energy exchange flows with the surroundings. Such fields of research will necessarily foment and drive the use of thermodynamic models based on the description of irreversibility at the macroscopic level, instead of the current models which are strongly anchored in microreversibility.

Mathematical kinetic modelling followed by in vitro and in vivo assays reveal the bifunctional rice GTPCHII/DHBPS enzymes and demonstrate the key roles of OsRibA proteins in the vitamin B2 pathway

BACKGROUND

Riboflavin is the precursor of several cofactors essential for normal physical and cognitive development, but only plants and some microorganisms can produce it. Humans thus rely on their dietary intake, which at a global level is mainly constituted by cereals (> 50%). Understanding the riboflavin biosynthesis players is key for advancing our knowledge on this essential pathway and can hold promise for biofortification strategies in major crop species. In some bacteria and in Arabidopsis, it is known that RibA1 is a bifunctional protein with distinct GTP cyclohydrolase II (GTPCHII) and 3,4-dihydroxy-2-butanone-4-phosphate synthase (DHBPS) domains. Arabidopsis harbors three RibA isoforms, but only one retained its bifunctionality. In rice, however, the identification and characterization of RibA has not yet been described.

RESULTS

Through mathematical kinetic modeling, we identified RibA as the rate-limiting step of riboflavin pathway and by bioinformatic analysis we confirmed that rice RibA proteins carry both domains, DHBPS and GTPCHII. Phylogenetic analysis revealed that OsRibA isoforms 1 and 2 are similar to Arabidopsis bifunctional RibA1. Heterologous expression of OsRibA1 completely restored the growth of the rib3∆ yeast mutant, lacking DHBPS expression, while causing a 60% growth improvement of the rib1∆ mutant, lacking GTPCHII activity. Regarding OsRibA2, its heterologous expression fully complemented GTPCHII activity, and improved rib3∆ growth by 30%. In vitro activity assays confirmed that both OsRibA1 and OsRibA2 proteins carry GTPCHII/DHBPS activities, but that OsRibA1 has higher DHBPS activity. The overexpression of OsRibA1 in rice callus resulted in a 28% increase in riboflavin content.

CONCLUSIONS

Our study elucidates the critical role of RibA in rice riboflavin biosynthesis pathway, establishing it as the rate-limiting step in the pathway. By identifying and characterizing OsRibA1 and OsRibA2, showcasing their GTPCHII and DHBPS activities, we have advanced the understanding of riboflavin biosynthesis in this staple crop. We further demonstrated that OsRibA1 overexpression in rice callus increases its riboflavin content, providing supporting information for bioengineering efforts.

Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing

BACKGROUND AND PURPOSE

Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed.

EXPERIMENTAL APPROACH

Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing.

KEY RESULTS

The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario.

CONCLUSION AND IMPLICATIONS

Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

MakeSBML: a tool for converting between Antimony and SBML

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.

SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem

Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for high-performance symbolic-numeric computations. It allows users to automatically enhance high-level descriptions of their models with symbolic preprocessing and automatic sparsification and parallelization of computations. This enables performant solution of differential equations, efficient parameter estimation and methodologies for automated model discovery with neural differential equations and sparse identification of nonlinear dynamics. To give the systems biology community easy access to SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports dynamic SBML models into the SciML ecosystem to accelerate model simulation and fitting of kinetic parameters. By providing computational systems biologists with easy access to the open-source Julia ecosystevnm, we hope to catalyze the development of further Julia tools in this domain and the growth of the Julia bioscience community. SBMLToolkit.jl is freely available under the MIT license. The source code is available at https://github.com/SciML/SBMLToolkit.jl.

BioModME for building and simulating dynamic computational models of complex biological systems

Summary

Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed "BioModME," a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization.

Availability and implementation

All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

First-order ultrasensitivity in phosphorylation cycles

Cellular signal transduction takes place through a network of phosphorylation cycles. These pathways take the form of a multi-layered cascade of cycles. This work focuses on the sensitivity of single, double and n length cycles. Cycles that operate in the zero-order regime can become sensitive to changes in signal, resulting in zero-order ultrasensitivity (ZOU). Using frequency analysis, we confirm previous efforts that cascades can act as noise filters by computing the bandwidth. We show that n length cycles display what we term first-order ultrasensitivity which occurs even when the cycles are not operating in the zero-order regime. The magnitude of the sensitivity, however, has an upper bound equal to the number of cycles. It is known that ZOU can be significantly reduced in the presence of retroactivity. We show that the first-order ultrasensitivity is immune to retroactivity and that the ZOU and first-order ultrasensitivity can be blended to create systems with constant sensitivity over a wider range of signal. We show that the ZOU in a double cycle is only modestly higher compared with a single cycle. We therefore speculate that the double cycle has evolved to enable amplification even in the face of retroactivity.

Stochastic model of vesicular stomatitis virus replication reveals mutational effects on virion production

We present the first complete stochastic model of vesicular stomatitis virus (VSV) intracellular replication. Previous models developed to capture VSV's intracellular replication have either been ODE-based or have not represented the complete replicative cycle, limiting our ability to understand the impact of the stochastic nature of early cellular infections on virion production between cells and how these dynamics change in response to mutations. Our model accurately predicts changes in mean virion production in gene-shuffled VSV variants and can capture the distribution of the number of viruses produced. This model has allowed us to enhance our understanding of intercellular variability in virion production, which appears to be influenced by the duration of the early phase of infection, and variation between variants, arising from balancing the time the genome spends in the active state, the speed of incorporating new genomes into virions, and the production of viral components. Being a stochastic model, we can also assess other effects of mutations beyond just the mean number of virions produced, including the probability of aborted infections and the standard deviation of the number of virions produced. Our model provides a biologically interpretable framework for studying the stochastic nature of VSV replication, shedding light on the mechanisms underlying variation in virion production. In the future, this model could enable the design of more complex viral phenotypes when attenuating VSV, moving beyond solely considering the mean number of virions produced.

Symmetry breaking and chiral amplification in prebiotic ligation reactions

The single chirality of biological molecules is a signature of life. Yet, rationalizing how single chirality emerged remains a challenging goal1. Research has commonly focused on initial symmetry breaking and subsequent enantioenrichment of monomer building blocks-sugars and amino acids-that compose the genetic polymers RNA and DNA as well as peptides. If these building blocks are only partially enantioenriched, however, stalling of chain growth may occur, whimsically termed in the case of nucleic acids as "the problem of original syn"2. Here, in studying a new prebiotically plausible route to proteinogenic peptides3-5, we discovered that the reaction favours heterochiral ligation (that is, the ligation of L monomers with D monomers). Although this finding seems problematic for the prebiotic emergence of homochiral L-peptides, we demonstrate, paradoxically, that this heterochiral preference provides a mechanism for enantioenrichment in homochiral chains. Symmetry breaking, chiral amplification and chirality transfer processes occur for all reactants and products in multicomponent competitive reactions even when only one of the molecules in the complex mixture exhibits an imbalance in enantiomer concentrations (non-racemic). Solubility considerations rationalize further chemical purification and enhanced chiral amplification. Experimental data and kinetic modelling support this prebiotically plausible mechanism for the emergence of homochiral biological polymers.