COPASI and its applications in biotechnology

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.

Development of a Highly Selective NAD+ -Dependent Glyceraldehyde Dehydrogenase and its Application in Minimal Cell-Free Enzyme Cascades

Anthropogenic climate change has been caused by over-exploitation of fossil fuels and CO2 emissions. To counteract this, the chemical industry has shifted its focus to sustainable chemical production and the valorization of renewable resources. However, the biggest challenges in biomanufacturing are technical efficiency and profitability. In our minimal cell-free enzyme cascade generating pyruvate as the central intermediate, the NAD+ -dependent, selective oxidation of D-glyceraldehyde was identified as a key reaction step to improve the overall cascade flux. Successive genome mining identified one candidate enzyme with 24-fold enhanced activity and another whose stability is unaffected in 10 % (v/v) ethanol, the final product of our model cascade. Semi-rational engineering improved the substrate selectivity of the enzyme up to 21-fold, thus minimizing side reactions in the one-pot enzyme cascade. The final biotransformation of D-glucose showed a continuous linear production of ethanol (via pyruvate) to a final titer of 4.9 % (v/v) with a molar product yield of 98.7 %. Due to the central role of pyruvate in diverse biotransformations, the optimized production module has great potential for broad biomanufacturing applications.

A Kinetic Approach to Oxygen Radical Absorbance Capacity (ORAC): Restoring Order to the Antioxidant Activity of Hydroxycinnamic Acids and Fruit Juices

This study introduces a kinetic model that significantly improves the interpretation of the oxygen radical absorbance capacity (ORAC) assay. Our model accurately simulates and fits the bleaching kinetics of fluorescein in the presence of various antioxidants, achieving high correlation values (R2 > 0.99) with the experimental data. The fit to the experimental data is achieved by optimizing two rate constants, k5 and k6. The k5 value reflects the reactivity of antioxidants toward scavenging peroxyl radicals, whereas k6 measures the ability of antioxidants to regenerate oxidized fluorescein. These parameters (1) allow the detailed classification of cinnamic acids based on their structure-activity relationships, (2) provide insights into the interaction of alkoxyl radicals with fluorescein, and (3) account for the regeneration of fluorescein radicals by antioxidants. The application of the model to different antioxidants and fruit extracts reveals significant deviations from the results of traditional ORAC tests based on the area under the curve (AUC) approach. For example, lemon juice, rich in 'fast' antioxidants such as ascorbic acid, shows a high k5 value, in contrast to its low AUC values. This finding underscores the limitations of the AUC approach and highlights the advantages of our kinetic model in understanding antioxidative dynamics in food systems. This study presents a comprehensive, quantitative, mechanism-oriented approach to assessing antioxidant reactivity, demonstrating a significant improvement in ORAC assay applications.

A query-response causal analysis of reaction events in biochemical reaction networks

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines.

A Fermentation State Marker Rule Design Task in Metabolic Engineering

There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate μ is above 0.060 h-1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2-10, where all metabolic fluxes units are mmol ∗ gDW-1 ∗ h-1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.

DyCeModel: a tool for 1D simulation for distribution of plant hormones controlling tissue patterning

To study the mechanisms of growth and development, it is necessary to analyze the dynamics of the tissue patterning regulators in time and space and to take into account their effect on the cellular dynamics within a tissue. Plant hormones are the main regulators of the cell dynamics in plant tissues; they form gradients and maxima and control molecular processes in a concentration-dependent manner. Here, we present DyCeModel, a software tool implemented in MATLAB for one-dimensional simulation of tissue with a dynamic cellular ensemble, where changes in hormone (or other active substance) concentration in the cells are described by ordinary differential equations (ODEs). We applied DyCeModel to simulate cell dynamics in plant meristems with different cellular structures and demonstrated that DyCeModel helps to identify the relationships between hormone concentration and cellular behaviors. The tool visualizes the simulation progress and presents a video obtained during the calculation. Importantly, the tool is capable of automatically adjusting the parameters by fitting the distribution of the substance concentrations predicted in the model to experimental data taken from the microscopic images. Noteworthy, DyCeModel makes it possible to build models for distinct types of plant meristems with the same ODEs, recruiting specific input characteristics for each meristem. We demonstrate the tool's efficiency by simulation of the effect of auxin and cytokinin distributions on tissue patterning in two types of Arabidopsis thaliana stem cell niches: the root and shoot apical meristems. The resulting models represent a promising framework for further study of the role of hormone-controlled gene regulatory networks in cell dynamics.

Reproducibility and FAIR principles: the case of a segment polarity network model

The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case. I analyze a computational model of the segment polarity network in Drosophila embryos published in 2000. Despite the high number of citations to this publication, 23 years later the model is barely accessible, and consequently not interoperable. Following the text of the original publication allowed successfully encoding the model for the open source software COPASI. Subsequently saving the model in the SBML format allowed it to be reused in other open source software packages. Submission of this SBML encoding of the model to the BioModels database enables its findability and accessibility. This demonstrates how the FAIR principles can be successfully enabled by using open source software, widely adopted standards, and public repositories, facilitating reproducibility and reuse of computational cell biology models that will outlive the specific software used.

Animal-derived products in science and current alternatives

More than fifty years after the 3Rs definition and despite the continuous implementation of regulatory measures, animals continue to be widely used in basic research. Their use comprises not only in vivo experiments with animal models, but also the production of a variety of supplements and products of animal origin for cell and tissue culture, cell-based assays, and therapeutics. The animal-derived products most used in basic research are fetal bovine serum (FBS), extracellular matrix proteins such as Matrigel™, and antibodies. However, their production raises several ethical issues regarding animal welfare. Additionally, their biological origin is associated with a high risk of contamination, resulting, frequently, in poor scientific data for clinical translation. These issues support the search for new animal-free products able to replace FBS, Matrigel™, and antibodies in basic research. In addition, in silico methodologies play an important role in the reduction of animal use in research by refining the data previously to in vitro and in vivo experiments. In this review, we depicted the current available animal-free alternatives in in vitro research.

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Muscarinic acetylcholine receptor-dependent and NMDA receptor-dependent LTP and LTD share the common AMPAR trafficking pathway

The forebrain cholinergic system promotes higher brain function in part by signaling through the M₁ muscarinic acetylcholine receptor (mAChR). Long-term potentiation (LTP) and long-term depression (LTD) of excitatory synaptic transmission in the hippocampus are also induced by mAChR. An AMPA receptor (AMPAR) trafficking model for hippocampal neurons has been proposed to simulate N-methyl-D-aspartate receptor (NMDAR)-dependent synaptic plasticity in the early phase. In this study, we demonstrated the validity of the hypothesis that the mAChR-dependent LTP/LTD shares a common AMPAR trafficking pathway associated with NMDAR-dependent LTP/LTD. However, unlike NMDAR, Ca²⁺ influx into the spine cytosol occurs owing to the Ca²⁺ stored inside the ER and is induced via the activation of inositol 1,4,5-trisphosphate (IP3) receptors during M1 mAChR activation. Moreover, the AMPAR trafficking model implies that alterations in LTP and LTD observed in Alzheimer's disease could be attributed to age-dependent reductions in AMPAR expression levels.

Decoding systems immunological model of sphingolipids with IL-6/IL-17/IL-23 axes in L. major infection

IL-6, IL-17, IL-23 and IL-1β are the crucial cytokines controlling inflammatory and immune response during L. major infection. During cutaneous leishmaniasis, an important T helper cell type CD4⁺ Th17 subset plays a deterministic role in lesion formation through channelling infected macrophages and production of IL-1β, IL-6, IL-23 and IFN-γ. Ceramide derived sphingosine precursors may assist in pro-inflammatory cytokine response. However, the role of these metabolites in inflammation with pleiotropic pro-inflammatory cytokines in L. major infection is unknown. The present study indicates IL-6/IL-17/IL-23 and SPHK1-S1P-S1PRs signaling axes with the overexpression of SATB1 aiding in disease progression. Targeting SATB1 might modulate the secretion of pro-inflammatory cytokines and abnormal immune functioning, thereby killing the intracellular parasite. Systems immunological methods assisted in a step towards identifying the key to the mystery of crucial components and serving as an approach for therapeutic intervention in L. major infection.

Cell-free enzyme cascades - application and transition from development to industrial implementation

In the vision to realize a circular economy aiming for net carbon neutrality or even negativity, cell-free bioconversion of sustainable and renewable resources emerged as a promising strategy. The potential of in vitro systems is enormous, delivering technological, ecological, and ethical added values. Innovative concepts arose in cell-free enzymatic conversions to reduce process waste production and preserve fossil resources, as well as to redirect and assimilate released industrial pollutions back into the production cycle again. However, the great challenge in the near future will be the jump from a concept to an industrial application. The transition process in industrial implementation also requires economic aspects such as productivity, scalability, and cost-effectiveness. Here, we briefly review the latest proof-of-concept cascades using carbon dioxide and other C1 or lignocellulose-derived chemicals as blueprints to efficiently recycle greenhouse gases, as well as cutting-edge technologies to maturate these concepts to industrial pilot plants.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

A Practical Guide to Reproducible Modeling for Biochemical Networks

While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.

Theoretical Explanation for the Rarity of Antibody-Dependent Enhancement of Infection (ADE) in COVID-19

Global vaccination against the SARS-CoV-2 virus has proved to be highly effective. However, the possibility of antibody-dependent enhancement of infection (ADE) upon vaccination remains underinvestigated. Here, we aimed to theoretically determine conditions for the occurrence of ADE in COVID-19. We developed a series of mathematical models of antibody response: model Ab—a model of antibody formation; model Cv—a model of infection spread in the body; and a complete model, which combines the two others. The models describe experimental data on SARS-CoV and SARS-CoV-2 infections in humans and cell cultures, including viral load dynamics, seroconversion times and antibody concentration kinetics. The modelling revealed that a significant proportion of macrophages can become infected only if they bind antibodies with high probability. Thus, a high probability of macrophage infection and a sufficient amount of pre-existing antibodies are necessary for the development of ADE in SARS-CoV-2 infection. However, from the point of view of the dynamics of pneumocyte infection, the two cases where the body has a high concentration of preexisting antibodies and a high probability of macrophage infection and where there is a low concentration of antibodies in the body and no macrophage infection are indistinguishable. This conclusion could explain the lack of confirmed ADE cases for COVID-19.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Immune response to SARS-CoV-2 in severe disease and long COVID-19

COVID-19 is mild to moderate in otherwise healthy individuals but may nonetheless cause life-threatening disease and/or a wide range of persistent symptoms. The general determinant of disease severity is age mainly because the immune response declines in aging patients. Here, we developed a mathematical model of the immune response to SARS-CoV-2 and revealed that typical age-related risk factors such as only a several 10% decrease in innate immune cell activity and inhibition of type-I interferon signaling by autoantibodies drastically increased the viral load. It was reported that the numbers of certain dendritic cell subsets remained less than half those in healthy donors even seven months after infection. Hence, the inflammatory response was ongoing. Our model predicted the persistent DC reduction and showed that certain patients with severe and even mild symptoms could not effectively eliminate the virus and could potentially develop long COVID.

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Systemic SARS-CoV-2 infection owing to ACE2 expression on a wide range of cell types

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Persistent viral infection can be ongoing within the host even if it is not severe

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Long-term dendritic cell deficiency is owing to viruses that cannot be removed by the host

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Ongoing persistent viral infection within the host potentially causes long COVID or PASC

Stoichiometric network analysis in reaction networks yielding spontaneous mirror symmetry breaking in a prebiotic atmosphere

The generation of amino acid homochirality under prebiotic atmosphere conditions is a relevant issue in the study of the origin of life. This research is based on the production of amino acids via Strecker synthesis and how it is adjusted to the Kondepudi-Nelson autocatalytic model. The spontaneous mirror symmetry breaking (SMSB) of the new Kondepudi-Nelson-Strecker model, subject to two modifications (with Limited Enantioselective and Cross Inhibition), and also their combination were studied using the stoichiometric network analysis (SNA). In the calculations, the values obtained from the literature for alanine were considered. A total production of alanine of 7.56 × 10⁹ mol year^-1 was determined under prebiotic atmosphere conditions and starting from that value, the reaction rates for the models studied were estimated. Only the model with cross inhibition or achiral dimer formation is driven by stochastic fluctuations during SMSB. The stochastic fluctuation was estimated for a value of 2.619 × 10^-15 mol L^-1. This perturbation was sufficient to trigger SMSB. Finally, the results of SMSB were used to calculate the entropy production for the cross inhibition model.

Deciphering miR-520c-3p as a probable target for immunometabolism in non-small cell lung cancer using systems biology approach

Background: Non-small cell lung cancer (NSCLC) is considered to have more than 80% of all lung cancer cases, making it the leading cause of cancer-related deaths globally. MicroRNA (miRNA) deregulation has been seen often in NSCLC and has been linked to the disease’s genesis, progression, and metastasis via affecting their target genes.

Materials and Methods: Our study focused on the functionality of down-regulated miRNAs in NSCLC. For this study, we used 91 miRNAs reported to be down-regulated in NSCLC. The targets of these miRNAs were chosen from miRNA databases with functionality in NSCLC, including miRBase, miRDB, miRTV, and others. Inter-regulatory miRNA-NSCLC networks were generated. Simulated annealing was used to improve the network’s resilience and understandability. GSEA was used to examine 24607 genes reported experimentally in order to gain physiologically relevant information about the target miR-520c-3p.

Results: The study revealed functional prominence on miR-520c-3p, down-regulated during NSCLC. The involvement of miR-520c-3p in the PI3K/AKT/mTOR signaling pathway was recognized.

Conclusions: The therapeutic usage by designing a synthetic circuit of miR-520c-3p was explored, which may help in suppressing tumors in NSCLC. Our study holds promise for the successful deployment of currently proposed miRNA-based therapies for malignant disorders, which are still in the early pre-clinical stages of development.

Robustness of the Krebs Cycle under Physiological Conditions and in Cancer: New Clues for Evaluating Metabolism-Modifying Drug Therapies

The Krebs cycle in cells that contain mitochondria is necessary for both energy production and anabolic processes. In given cell/condition, the Krebs cycle is dynamic but remains at a steady state. In this article, we first aimed at comparing the properties of a closed cycle versus the same metabolism in a linear array. The main finding is that, unlike a linear metabolism, the closed cycle can reach a steady state (SS) regardless of the nature and magnitude of the disturbance. When the cycle is modeled with input and output reactions, the “open” cycle is robust and reaches a steady state but with exceptions that lead to sustained accumulation of intermediate metabolites, i.e., conditions at which no SS can be achieved. The modeling of the cycle in cancer, trying to obtain marked reductions in flux, shows that these reductions are limited and therefore the Warburg effect is moderate at most. In general, our results of modeling the cycle in different conditions and looking for the achievement, or not, of SS, suggest that the cycle may have a regulation, not yet discovered, to go from an open cycle to a closed one. Said regulation could allow for reaching the steady state, thus avoiding the unwanted effects derived from the aberrant accumulation of metabolites in the mitochondria. The information in this paper might be useful to evaluate metabolism-modifying medicines.

BioSimulators: a central registry of simulation engines and services for recommending specific tools

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.

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits

Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.

An intrinsically disordered nascent protein interacts with specific regions of the ribosomal surface near the exit tunnel

The influence of the ribosome on nascent chains is poorly understood, especially in the case of proteins devoid of signal or arrest sequences. Here, we provide explicit evidence for the interaction of specific ribosomal proteins with ribosome-bound nascent chains (RNCs). We target RNCs pertaining to the intrinsically disordered protein PIR and a number of mutants bearing a variable net charge. All the constructs analyzed in this work lack N-terminal signal sequences. By a combination chemical crosslinking and Western-blotting, we find that all RNCs interact with ribosomal protein L23 and that longer nascent chains also weakly interact with L29. The interacting proteins are spatially clustered on a specific region of the large ribosomal subunit, close to the exit tunnel. Based on chain-length-dependence and mutational studies, we find that the interactions with L23 persist despite drastic variations in RNC sequence. Importantly, we also find that the interactions are highly Mg+2-concentration-dependent. This work is significant because it unravels a novel role of the ribosome, which is shown to engage with the nascent protein chain even in the absence of signal or arrest sequences.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Kinetics of the ancestral carbon metabolism pathways in deep-branching bacteria and archaea

The origin of life is believed to be chemoautotrophic, deriving all biomass components from carbon dioxide, and all energy from inorganic redox couples in the environment. The reductive tricarboxylic acid cycle (rTCA) and the Wood-Ljungdahl pathway (WL) have been recognized as the most ancient carbon fixation pathways. The rTCA of the chemolithotrophic Thermosulfidibacter takaii, which was recently demonstrated to take place via an unexpected reverse reaction of citrate synthase, was reproduced using a kinetic network model, and a competition between reductive and oxidative fluxes on rTCA due to an acetyl coenzyme A (ACOA) influx upon acetate uptake was revealed. Avoiding ACOA direct influx into rTCA from WL is, therefore, raised as a kinetically necessary condition to maintain a complete rTCA. This hypothesis was confirmed for deep-branching bacteria and archaea, and explains the kinetic factors governing elementary processes in carbon metabolism evolution from the last universal common ancestor.

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.

WinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways

We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models.

Pandemics: Historically Slow "Learning Curve" Leading to Biomedical Informatics and Vaccine Breakthroughs

BACKGROUND

The worldwide tragedy of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic vividly demonstrates just how inadequate mitigation and control of the spread of infectious diseases can be when faced with a new microorganism with unknown pathogenic effects. Responses by governments in charge of public health, and all other involved organizations, have proved largely wanting. Data infrastructure and the information and communication systems needed to deal with the pandemic have likewise not been up to the task. Nevertheless, after a year of the worldwide outbreak, hope arises from this being the first major pandemic event in history where genomic and related biosciences - relying on biomedical informatics - have been essential in decoding the viral sequence data and producing the mRNA and other biotechnologies that unexpectedly rapidly have led to investigation, design, development, and testing of useful vaccines. Medical informatics may also help support public health actions and clinical interventions - but scalability and impact will depend on overcoming ingrained human shortcomings to deal with complex socio-economic, political, and technological disruptions together with the many ethical challenges presented by pandemics.

OBJECTIVES

The principal goal is to review the history of biomedical information and healthcare practices related to past pandemics in order to illustrate just how exceptional and dependent on biomedical informatics are the recent scientific insights into human immune responses to viral infection, which are enabling rapid antiviral vaccine development and clinical management of severe cases - despite the many societal challenges ahead.

METHODS

This paper briefly reviews some of the key historical antecedents leading up to modern insights into epidemic and pandemic processes with their biomedical and healthcare information intended to guide practitioners, agencies, and the lay public in today's ongoing pandemic events.

CONCLUSIONS

Poor scientific understanding and excessively slow learning about infectious disease processes and mitigating behaviors have stymied effective treatment until the present time. Advances in insights about immune systems, genomes, proteomes, and all the other -omes, became a reality thanks to the key sequencing technologies and biomedical informatics that enabled the Human Genome Project, and only now, 20 years later, are having an impact in ameliorating devastating zoonotic infectious pandemics, including the present SARS-CoV-2 event through unprecedently rapid vaccine development. In the future these advances will hopefully also enable more targeted prevention and treatment of disease. However, past and present shortcomings of most of the COVID-19 pandemic responses illustrate just how difficult it is to persuade enough people - and especially political leaders - to adopt societally beneficial risk-avoidance behaviors and policies, even as these become better understood.

RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats

Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.

Virtual Transcriptomics: Noninvasive Phenotyping of Atherosclerosis by Decoding Plaque Biology From Computed Tomography Angiography Imaging

[Figure: see text].

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Building kinetic models for metabolic engineering

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

A cyanobacterial photorespiratory bypass model to enhance photosynthesis by rerouting photorespiratory pathway in C3 plants

Plants employ photosynthesis to produce sugars for supporting their growth. During photosynthesis, an enzyme Ribulose 1,5 bisphosphate carboxylase/oxygenase (Rubisco) combines its substrate Ribulose 1,5 bisphosphate (RuBP) with CO₂ to produce phosphoglycerate (PGA). Alongside, Rubisco also takes up O₂ and produce 2-phosphoglycolate (2-PG), a toxic compound broken down into PGA through photorespiration. Photorespiration is not only a resource-demanding process but also results in CO₂ loss which affects photosynthetic efficiency in C₃ plants. Here, we propose to circumvent photorespiration by adopting the cyanobacterial glycolate decarboxylation pathway into C₃ plants. For that, we have integrated the cyanobacterial glycolate decarboxylation pathway into a kinetic model of C₃ photosynthetic pathway to evaluate its impact on photosynthesis and photorespiration. Our results show that the cyanobacterial glycolate decarboxylation bypass model exhibits a 10% increase in net photosynthetic rate (A) in comparison with C₃ model. Moreover, an increased supply of intercellular CO₂ (C_i) from the bypass resulted in a 54.8% increase in PGA while reducing photorespiratory intermediates including glycolate (− 49%) and serine (− 32%). The bypass model, at default conditions, also elucidated a decline in phosphate-based metabolites including RuBP (− 61.3%). The C₃ model at elevated level of inorganic phosphate (Pi), exhibited a significant change in RuBP (+ 355%) and PGA (− 98%) which is attributable to the low availability of C_i. Whereas, at elevated Pi, the bypass model exhibited an increase of 73.1% and 33.9% in PGA and RuBP, respectively. Therefore, we deduce a synergistic effect of elevation in CO₂ and Pi pool on photosynthesis. We also evaluated the integrative action of CO₂, Pi, and Rubisco carboxylation activity (V_cmax) on A and observed that their simultaneous increase raised A by 26%, in the bypass model. Taken together, the study potentiates engineering of cyanobacterial decarboxylation pathway in C₃ plants to bypass photorespiration thereby increasing the overall efficiency of photosynthesis.

A practical guide to mechanistic systems modeling in biology using a logic-based approach

Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process-the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

In Silico Hemostasis Modeling and Prediction

Computational physiology, i.e., reproduction of physiological (and, by extension, pathophysiological) processes in silico, could be considered one of the major goals in computational biology. One might use computers to simulate molecular interactions, enzyme kinetics, gene expression, or whole networks of biochemical reactions, but it is (patho)physiological meaning that is usually the meaningful goal of the research even when a single enzyme is its subject. Although exponential rise in the use of computational and mathematical models in the field of hemostasis and thrombosis began in the 1980s (first for blood coagulation, then for platelet adhesion, and finally for platelet signal transduction), the majority of their successful applications are still focused on simulating the elements of the hemostatic system rather than the total (patho)physiological response in situ. Here we discuss the state of the art, the state of the progress toward the efficient "virtual thrombus formation," and what one can already get from the existing models.

Mechanism underlying hippocampal long-term potentiation and depression based on competition between endocytosis and exocytosis of AMPA receptors

N-methyl-D-aspartate (NMDA) receptor-dependent long-term potentiation (LTP) and long-term depression (LTD) of signal transmission form neural circuits and thus are thought to underlie learning and memory. These mechanisms are mediated by AMPA receptor (AMPAR) trafficking in postsynaptic neurons. However, the regulatory mechanism of bidirectional plasticity at excitatory synapses remains unclear. We present a network model of AMPAR trafficking for adult hippocampal pyramidal neurons, which reproduces both LTP and LTD. We show that the induction of both LTP and LTD is regulated by the competition between exocytosis and endocytosis of AMPARs, which are mediated by the calcium-sensors synaptotagmin 1/7 (Syt1/7) and protein interacting with C-kinase 1 (PICK1), respectively. Our result indicates that recycling endosomes containing AMPAR are always ready for Syt1/7-dependent exocytosis of AMPAR at peri-synaptic/synaptic membranes. This is because molecular motor myosin V_b constitutively transports the recycling endosome toward the membrane in a Ca²⁺-independent manner.

Ten simple rules on how to write a standard operating procedure

Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility.

Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

Impact of external amino acids on fluorescent protein chromophore biosynthesis revealed by molecular dynamics and mutagenesis studies

The precise positioning of catalytic amino acids against the substrate in an enzyme active site is a crucial factor in biocatalysis. Biosynthesis of the chromophores of fluorescent proteins (FPs) is an autocatalytic process that must conform to these requirements. Here, we show that, in addition to the internal amino acid residues in the proximity of the chromophore, chromophore biosynthesis is influenced by the remote amino acids exposed on the outer surface of the β-barrel structure of the FP. It has been shown earlier that chromophore biosynthesis of the red FP from Zoanthus sp. (zoan2RFP) proceeds via an immature green state. At the same time, the green state is the final stage of chromophore biosynthesis of green FP (zoanGFP), which is highly homologous to zoan2RFP. It was also shown that a single N66D substitution in the chromophore-forming sequence of zoanGFP might trigger the synthesis of the red chromophore. However, in this case, the synthesis of the red chromophore is incomplete and occurs only at elevated temperatures. Here, we tried to uncover additional structural determinants that govern the biosynthesis of the red chromophore. A comparison of zoanGFP and zoan2RFP revealed intrabarrel amino acid differences at five positions. Exhaustive substitutions of these five positions in zoanGFP-N66D gave rise to zoanGFPmut with the same intrabarrel amino acid composition as zoan2RFP. zoanGFPmut showed only partial green-to-red chromophore transformation at elevated temperatures. To elucidate the extra factors that can affect red chromophore biosynthesis, we performed comparative molecular dynamics simulations of zoan2RFP and zoanGFPmut. The simulations revealed several external amino acids that might influence the arrangement and flexibility of the chromophore-surrounding amino acid residues in these proteins. Mutagenesis experiments confirmed the crucial role of these residues in red chromophore biosynthesis. The obtained zoanGFPmut2 exhibited complete green-to-red transformation, suggesting that the mutated amino acids exposed on the surface of the β-barrel contribute to red chromophore biosynthesis.

Harnessing the Potential of Stem Cells for Disease Modeling: Progress and Promises

Ex vivo cell/tissue-based models are an essential step in the workflow of pathophysiology studies, assay development, disease modeling, drug discovery, and development of personalized therapeutic strategies. For these purposes, both scientific and pharmaceutical research have adopted ex vivo stem cell models because of their better predictive power. As matter of a fact, the advancing in isolation and in vitro expansion protocols for culturing autologous human stem cells, and the standardization of methods for generating patient-derived induced pluripotent stem cells has made feasible to generate and investigate human cellular disease models with even greater speed and efficiency. Furthermore, the potential of stem cells on generating more complex systems, such as scaffold-cell models, organoids, or organ-on-a-chip, allowed to overcome the limitations of the two-dimensional culture systems as well as to better mimic tissues structures and functions. Finally, the advent of genome-editing/gene therapy technologies had a great impact on the generation of more proficient stem cell-disease models and on establishing an effective therapeutic treatment. In this review, we discuss important breakthroughs of stem cell-based models highlighting current directions, advantages, and limitations and point out the need to combine experimental biology with computational tools able to describe complex biological systems and deliver results or predictions in the context of personalized medicine.

Mechanism of Reconstitution/Activation of the Soluble PQQ-Dependent Glucose Dehydrogenase from Acinetobacter calcoaceticus: A Comprehensive Study

The ability to switch on the activity of an enzyme through its spontaneous reconstitution has proven to be a valuable tool in fundamental studies of enzyme structure/reactivity relationships or in the design of artificial signal transduction systems in bioelectronics, synthetic biology, or bioanalytical applications. In particular, those based on the spontaneous reconstitution/activation of the apo-PQQ-dependent soluble glucose dehydrogenase (sGDH) from Acinetobacter calcoaceticus were widely developed. However, the reconstitution mechanism of sGDH with its two cofactors, i.e., pyrroloquinoline quinone (PQQ) and Ca²⁺, remains unknown. The objective here is to elucidate this mechanism by stopped-flow kinetics under single-turnover conditions. The reconstitution of sGDH exhibited biphasic kinetics, characteristic of a square reaction scheme associated with two activation pathways. From a complete kinetic analysis, we were able to fully predict the reconstitution dynamics and also to demonstrate that when PQQ first binds to apo-sGDH, it strongly impedes the access of Ca²⁺ to its enclosed position at the bottom of the enzyme binding site, thereby greatly slowing down the reconstitution rate of sGDH. This slow calcium insertion may purposely be accelerated by providing more flexibility to the Ca²⁺ binding loop through the specific mutation of the calcium-coordinating P248 proline residue, reducing thus the kinetic barrier to calcium ion insertion. The dynamic nature of the reconstitution process is also supported by the observation of a clear loop shift and a reorganization of the hydrogen-bonding network and van der Waals interactions observed in both active sites of the apo and holo forms, a structural change modulation that was revealed from the refined X-ray structure of apo-sGDH (PDB: 5MIN).

A software package for immunologists to learn simulation modeling

Background

As immunology continues to become more quantitative, increasingly sophisticated computational tools are commonly used. One useful toolset are simulation models. Becoming familiar with such models and their uses generally requires writing computer code early in the learning process. This poses a barrier for individuals who do not have prior coding experience.

Results

To help reduce this barrier, I wrote software that teaches the use of mechanistic simulation models to study infection and immune response dynamics, without the need to read or write computer code. The software, called Dynamical Systems Approach to Immune Response Modeling (DSAIRM), is implemented as a freely available package for the R programming language. The target audience are immunologists and other scientists with no or little coding experience. DSAIRM provides a hands-on introduction to simulation models, teaches the basics of those models and what they can be used for. Here, I describe the DSAIRM R package, explain the different ways the package can be used, and provide a few introductory examples.

Conclusions

Working through DSAIRM will equip individuals with the knowledge needed to critically assess studies using simulation models in the published literature and will help them understand when such a modeling approach might be suitable for their own research. DSAIRM also provides users a potential starting point towards development and use of simulation models in their own research.

Parameter Estimation and Uncertainty Quantification for Systems Biology Models

Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quantification before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.

Interactive Multiresolution Visualization of Cellular Network Processes

Visualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.