Evaluation and comparison of computational models

Action Selection in Everyday Activities: The Opportunistic Planning Model

While action selection strategies in well-defined domains have received considerable attention, little is yet known about how people choose what to do next in ill-defined tasks. In this contribution, we shed light on this issue by considering everyday tasks, which in many cases have a multitude of possible solutions (e.g., it does not matter in which order the items are brought to the table when setting a table) and are thus categorized as ill-defined problems. Even if there are no hard constraints on the ordering of subtasks in everyday activities, our research shows that people exhibit specific preferences. We propose that these preferences arise from bounded rationality, that is, people only have limited knowledge and processing power available, which results in a preference to minimize the overall physical and cognitive effort. In the context of everyday activities, this can be achieved by (a) taking properties of the spatial environment into account to use them to one's advantage, and (b) employing a stepwise-optimal action selection strategy. We present the Opportunistic Planning Model as an explanatory cognitive model, which instantiates these assumptions, and show that the model is able to generalize to new everyday tasks, outperforming machine learning models such as neural networks during generalization.

Heptanoate Improves Compensatory Mechanism of Glucose Homeostasis in Mitochondrial Long-Chain Fatty Acid Oxidation Defect

Defects in mitochondrial fatty acid β-oxidation (FAO) impair metabolic flexibility, which is an essential process for energy homeostasis. Very-long-chain acyl-CoA dehydrogenase (VLCADD; OMIM 609575) deficiency is the most common long-chain mitochondrial FAO disorder presenting with hypoglycemia as a common clinical manifestation. To prevent hypoglycemia, triheptanoin-a triglyceride composed of three heptanoates (C7) esterified with a glycerol backbone-can be used as a dietary treatment, since it is metabolized into precursors for gluconeogenesis. However, studies investigating the effect of triheptanoin on glucose homeostasis are limited. To understand the role of gluconeogenesis in the pathophysiology of long-chain mitochondrial FAO defects, we injected VLCAD-deficient (VLCAD-/-) mice with 13C3-glycerol in the presence and absence of heptanoate (C7). The incorporation of 13C3-glycerol into blood glucose was higher in VLCAD-/- mice than in WT mice, whereas the difference disappeared in the presence of C7. The result correlates with 13C enrichment of liver metabolites in VLCAD-/- mice. In contrast, the C7 bolus significantly decreased the 13C enrichment. These data suggest that the increased contribution of gluconeogenesis to the overall glucose production in VLCAD-/- mice increases the need for gluconeogenesis substrate, thereby avoiding hypoglycemia. Heptanoate is a suitable substrate to induce glucose production in mitochondrial FAO defect.

Magnesium Ion-Driven Folding and Conformational Switching Kinetics of Tetracycline Binding Aptamer: Implications for in vivo Riboswitch Engineering

Engineering in vitro selected RNA aptamers into in vivo functional riboswitches represents a long-standing challenge in molecular biology. The highly specific aptamer domain of the riboswitch undergoes a conformational adjustment in response to ligand sensing, which in turn exerts the regulatory function. Besides essential factors like structural complexity and ligand binding kinetics, the active role of magnesium ions in stabilizing RNA tertiary structures and assisting in ligand binding can be a vital criterion. We present spectroscopic studies on the magnesium ion-driven folding of the Tetracycline binding aptamer. Using fluorescent labels, the aptamer pre-folding and subsequent ligand binding is monitored by magnesium titration experiments and time-resolved stopped-flow measurements. A minimum concentration of 0.5 mM magnesium is required to fold into a magnesium ion-stabilized binding-competent state with a preformed binding pocket. Tetracycline binding causes a pronounced conformational change that results in the establishment of the triple helix core motif, and that further propagates towards the closing stem. By a dynamic acquisition of magnesium ions, a kink motif is formed at the intersection of the triple helix and closing stem regions. This ultimately entails a stabilization of the closing stem which is discussed as a key element in the regulatory function of the Tetracycline aptamer.

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"?

The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

A New Class of Uracil-DNA Glycosylase Inhibitors Active against Human and Vaccinia Virus Enzyme

Uracil-DNA glycosylases are enzymes that excise uracil bases appearing in DNA as a result of cytosine deamination or accidental dUMP incorporation from the dUTP pool. The activity of Family 1 uracil-DNA glycosylase (UNG) activity limits the efficiency of antimetabolite drugs and is essential for virulence in some bacterial and viral infections. Thus, UNG is regarded as a promising target for antitumor, antiviral, antibacterial, and antiprotozoal drugs. Most UNG inhibitors presently developed are based on the uracil base linked to various substituents, yet new pharmacophores are wanted to target a wide range of UNGs. We have conducted virtual screening of a 1,027,767-ligand library and biochemically screened the best hits for the inhibitory activity against human and vaccinia virus UNG enzymes. Although even the best inhibitors had IC₅₀ ≥ 100 μM, they were highly enriched in a common fragment, tetrahydro-2,4,6-trioxopyrimidinylidene (PyO3). In silico, PyO3 preferably docked into the enzyme's active site, and in kinetic experiments, the inhibition was better consistent with the competitive mechanism. The toxicity of two best inhibitors for human cells was independent of the presence of methotrexate, which is consistent with the hypothesis that dUMP in genomic DNA is less toxic for the cell than strand breaks arising from the massive removal of uracil. We conclude that PyO3 may be a novel pharmacophore with the potential for development into UNG-targeting agents.

Introduction to In Silico Modeling to Study ROS Dynamics

This book chapter is drafted for biologists with experimental experiences in ROS biology but being newcomers in the field of modeling. We start with a general introduction about computational modeling in biology and an overview of software tools suitable for beginners. This chapter encompasses an introduction to computational models with special focus on simulation of ROS dynamics. A step-by-step tutorial follows providing guidance for all relevant model development processes. This course of action gives a comprehensible way to understand the benefits of computational models and to gain the necessary knowledge to build own small equation-based models. Small models can be created without any special programming expertise or in-depth technical and mathematical knowledge. Afterward in the final section, a short overview of pitfalls, challenges, and limitations is provided, combined with suggestions for further reading to improve and expand modeling skills of biologists.

Biochemical screening for SARS-CoV-2 main protease inhibitors

The Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) pandemic represents a global challenge. SARS-CoV-2's ability to replicate in host cells relies on the action of its non-structural proteins, like its main protease (M^pro). This cysteine protease acts by processing the viruses' precursor polyproteins. As proteases, together with polymerases, are main targets of antiviral drug design, we here have performed biochemical high throughput screening (HTS) with recombinantly expressed SARS-CoV-2 M^pro. A fluorescent assay was used to identify inhibitors in a compound library containing known drugs, bioactive molecules and natural products. These screens led to the identification of 13 inhibitors with IC₅₀ values ranging from 0.2 μM to 23 μM. The screens confirmed several known SARS-CoV M^pro inhibitors as inhibitors of SARS-CoV-2 M^pro, such as the organo-mercuric compounds thimerosal and phenylmercuric acetate. Benzophenone derivatives could also be identified among the most potent screening hits. Additionally, Evans blue, a sulfonic acid-containing dye, could be identified as an M^pro inhibitor. The obtained compounds could be of interest as lead compounds for the development of future SARS-CoV-2 drugs.

Structure guided fluorescence labeling reveals a two-step binding mechanism of neomycin to its RNA aptamer

The ability of the cytidine analog Ç_m^f to act as a position specific reporter of RNA-dynamics was spectroscopically evaluated. Ç_m^f-labeled single- and double-stranded RNAs differ in their fluorescence lifetimes, quantum yields and anisotropies. These observables were also influenced by the nucleobases flanking Ç_m^f. This conformation and position specificity allowed to investigate the binding dynamics and mechanism of neomycin to its aptamer N1 by independently incorporating Ç_m^f at four different positions within the aptamer. Remarkably fast binding kinetics of neomycin binding was observed with stopped-flow measurements, which could be satisfactorily explained with a two-step binding. Conformational selection was identified as the dominant mechanism.

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

Multi-factor analysis in language production: Sequential sampling models mimic and extend regression results

For multi-factor analyses of response times, descriptive models (e.g., linear regression) arguably constitute the dominant approach in psycholinguistics. In contrast empirical cognitive models (e.g., sequential sampling models, SSMs) may fit fewer factors simultaneously, but decompose the data into several dependent variables (a multivariate result), offering more information to analyze. While SSMs are notably popular in the behavioural sciences, they are not significantly developed in language production research. To contribute to the development of this modelling in language, we (i) examine SSMs as a measurement modelling approach for spoken word activation dynamics, and (ii) formally compare SSMs to the default method, regression. SSMs model response activation or selection mechanisms in time, and calculate how they are affected by conditions, persons, and items. While regression procedures also model condition effects, it is only in respect to the mean RT, and little work has been previously done to compare these approaches. Through analyses of two language production experiments, we show that SSMs reproduce regression predictors, and further extend these effects through a multivariate decomposition (cognitive parameters). We also examine a combined regression-SSM approach that is hierarchical Bayesian, which can jointly model more conditions than classic SSMs, and importantly, achieve by-item modelling with other conditions. In this analysis, we found that spoken words principally differed from one another by their activation rates and production times, but not their thresholds to be activated.

Statistical Learning of Discrete States in Time Series

Time series obtained from time-dependent experiments contain rich information on kinetics and dynamics of the system under investigation. This work describes an unsupervised learning framework, along with the derivation of the necessary analytical expressions, for the analysis of Gaussian-distributed time series that exhibit discrete states. After the time series has been partitioned into segments in a model-free manner using the previously developed change-point (CP) method, this protocol starts with an agglomerative hierarchical clustering algorithm to classify the detected segments into possible states. The initial state clustering is further refined using an expectation-maximization (EM) procedure, and the number of states is determined by a Bayesian information criterion (BIC). Also introduced here is an achievement scalarization function, usually seen in artificial intelligence literature, for quantitatively assessing the performance of state determination. The statistical learning framework, which is comprised of three stages, detection of signal change, clustering, and number-of-state determination, was thoroughly characterized using simulated trajectories with random intensity segments that have no underlying kinetics, and its performance was critically evaluated. The application to experimental data is also demonstrated. The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.

Analysis of a dual domain phosphoglycosyl transferase reveals a ping-pong mechanism with a covalent enzyme intermediate

Phosphoglycosyl transferases (PGTs) are integral membrane proteins with diverse architectures that catalyze the formation of polyprenol diphosphate-linked glycans via phosphosugar transfer from a nucleotide diphosphate-sugar to a polyprenol phosphate. There are two PGT superfamilies that differ significantly in overall structure and topology. The polytopic PGT superfamily, represented by MraY and WecA, has been the subject of many studies because of its roles in peptidoglycan and O-antigen biosynthesis. In contrast, less is known about a second, extensive superfamily of PGTs that reveals a core structure with dual domain architecture featuring a C-terminal soluble globular domain and a predicted N-terminal membrane-associated domain. Representative members of this superfamily are the Campylobacter PglCs, which initiate N-linked glycoprotein biosynthesis and are implicated in virulence and pathogenicity. Despite the prevalence of dual domain PGTs, their mechanism of action is unknown. Here, we present the mechanistic analysis of PglC, a prototypic dual domain PGT from Campylobacter concisus Using a luminescence-based assay, together with substrate labeling and kinetics-based approaches, complementary experiments were carried out that support a ping-pong mechanism involving a covalent phosphosugar intermediate for PglC. Significantly, mass spectrometry-based approaches identified Asp93, which is part of a highly conserved AspGlu dyad found in all dual domain PGTs, as the active-site nucleophile of the enzyme involved in the formation of the covalent adduct. The existence of a covalent phosphosugar intermediate provides strong support for a ping-pong mechanism of PglC, differing fundamentally from the ternary complex mechanisms of representative polytopic PGTs.

Patient-Specific Modeling of Multicompartmental Fluid and Mass Exchange during Dialysis

Background

Dialysis is associated with a non-negligible rate of morbidity, requiring treatment customization. Many mathematical models have been developed describing solute kinetics during hemodialysis (HD) for an average uremic patient. The clinical need can be more adequately addressed by developing a patient-specific, multicompartmental model.

Materials and Methods

The data from 148 sessions (20 patients), recorded at the Regional Hospital of Lugano, Switzerland, were used to develop and validate the mathematical model. Diffusive and convective interactions among patient, dialysate and substitution fluid were considered. Three parameters, related to mass transfer efficiency at the cell membrane, at the dialyzer and at the capillary wall, were used to tune the model. The ability of the model to describe the clinical evolution of a specific HD session was evaluated by comparing model outputs with clinically acquired data on solutes and catabolite concentrations.

Results

The model developed in this study allows electrolyte and catabolite concentration trends during each HD session to be described. The errors obtained before the estimation of the patient-specific parameters drastically decrease after their identification. With the optimized model, plasmatic concentration trends can be described with an average percent error lower than 2.1% for Na+, CI-, Ca2+ and HCO3-, lower than 5% for K+ and lower than 8% for urea.

Conclusions

The peculiarity of the proposed model is the possibility it offers to perform a real-time simulation enabling quantitative appraisal of hematochemical quantities whose direct measurement is prohibitive. These will be beneficial to dialysis therapy planning, reducing intradialysis complications and improving patients’ quality of life.

Role of the Substrate Specificity-Defining Residues of Human SIRT5 in Modulating the Structural Stability and Inhibitory Features of the Enzyme

Sirtuins are emerging as the key regulators of metabolism and aging, and their potential activators and inhibitors are being explored as therapeutics for improving health and treating associated diseases. Despite the global structural similarity among all seven isoforms of sirtuins (of which most of them catalyze the deacetylation reaction), SIRT5 is the only isoform that catalyzes the cleavage of negatively charged acylated substrates, and the latter feature appears to be encoded by the presence of Tyr102 and Arg105 residues at the active site pocket of the enzyme. To determine the contributions of the above residues in SIRT5 (vis a vis the corresponding residues of SIRT1) on substrate selectivity, inhibition by EX527 and nicotinamide, secondary structural features and thermal stability of the enzymes, we created single and double mutations (viz. Y102A, R105l, and Y102A/R105I) in SIRT5. The kinetic data revealed that while Y102A mutant enzyme catalyzed both deacetylation and desuccinylation reactions with comparable efficiencies, R105I and Y102A/R105I mutant enzymes favored the deacetylase reaction. Like SIRT1, the nicotinamide inhibition of SIRT5 double mutant (Y102A/R105I) exhibited the mixed non-competitive behavior. On the other hand, the desuccinylation reaction of both wild-type and Y102A mutant enzymes conformed to the competitive inhibition model. The inhibitory potency of EX527 progressively increased from Y102A, R105I, to Y102A/R105 mutant enzymes in SIRT5, but it did not reach to the level obtained with SIRT1. The CD spectroscopic data for the wild-type and mutant enzymes revealed changes in the secondary structural features of the enzymes, and such changes were more pronounced on examining their thermal denaturation patterns. A cumulative account of our experimental data reveal mutual cooperation between Y102 and R105 residues in promoting the desuccinylation versus deacetylation reaction in SIRT5, and the overall catalytic feature of the enzyme is manifested via the mutation induced modulation in the protein structure.

Temporal prediction errors modulate task-switching performance

We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus’ onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as “executive control” is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching.

Identification of two-step chemical mechanisms using small temperature oscillations and a single tagged species

In order to identify two-step chemical mechanisms, we propose a method based on a small temperature modulation and on the analysis of the concentration oscillations of a single tagged species involved in the first step. The thermokinetic parameters of the first reaction step are first determined. Then, we build test functions that are constant only if the chemical system actually possesses some assumed two-step mechanism. Next, if the test functions plotted using experimental data are actually even, the mechanism is attributed and the obtained constant values provide the rate constants and enthalpy of reaction of the second step. The advantage of the protocol is to use the first step as a probe reaction to reveal the dynamics of the second step, which can hence be relieved of any tagging. The protocol is anticipated to apply to many mechanisms of biological relevance. As far as ligand binding is considered, our approach can address receptor conformational changes or dimerization as well as competition with or modulation by a second partner. The method can also be used to screen libraries of untagged compounds, relying on a tracer whose concentration can be spectroscopically monitored.

Model-based selection of the robust JAK-STAT activation mechanism

JAK-STAT pathway family is a principal signaling mechanism in eukaryotic cells. Evolutionary conserved roles of this mechanism include control over fundamental processes such as cell growth or apoptosis. Deregulation of the JAK-STAT signaling is frequently associated with cancerogenesis. JAK-STAT pathways become hyper-activated in many human tumors. Therefore, components of these pathways are an attractive target for drugs, which design requires as adequate models as possible. Although, in principle, JAK-STAT signaling is relatively simple, the ambiguities in a receptor activation prevent a clear explanation of the underlying molecular mechanism. Here, we compare four variants of a computational model of the JAK1/2-STAT1 signaling pathway. These variants capture known, basic discrepancies in the mechanism of activation of a cytokine receptor, in the context of all key components of the pathway. We carry out a comparative analysis using mass action kinetics. The investigated differences are so marginal that all models satisfy a goodness of fit criteria to the extent that the state of the art Bayesian model selection (BMS) method fails to significantly promote one model. Therefore, we comparatively investigate changes in a robustness of the JAK1/2-STAT1 pathway variants using the global sensitivity analysis method (GSA), complemented with the identifiability analysis (IA). Both BMS and GSA are used to analyze the models for the varying parameter values. We found out that, both BMS and GSA, narrowed down to the receptor activation component, slightly promote the least complex model. Further, insightful, comprehensive GSA, motivated by the concept of robustness, allowed us to show that the precise order of reactions of a ligand binding and a receptor dimerization is not as important as the on-membrane pre-assembly of the dimers in the absence of ligand. The main value of this work is an evaluation of the usefulness of different model selection methods in a frequently encountered, but not much discussed case of a model of a considerable size, which has several variants differing at peripheries. In such situation, all considered variants can reach nearly perfect agreement with respect to their numerical simulations results and, most often, the sufficient experimental data to test against is not available. We argue that in such an adverse setting, the GSA and IA, although not directly corresponding to the model selection problem, can be more informative than the representative, generalizability-based approaches to this task. An additional insight into how the responsibility for the network dynamics spreads among model parameters, enables more conscious, expert-mediated choice of the preferred model.

An introduction to NMR-based approaches for measuring protein dynamics

Proteins are inherently flexible at ambient temperature. At equilibrium, they are characterized by a set of conformations that undergo continuous exchange within a hierarchy of spatial and temporal scales ranging from nanometers to micrometers and femtoseconds to hours. Dynamic properties of proteins are essential for describing the structural bases of their biological functions including catalysis, binding, regulation and cellular structure. Nuclear magnetic resonance (NMR) spectroscopy represents a powerful technique for measuring these essential features of proteins. Here we provide an introduction to NMR-based approaches for studying protein dynamics, highlighting eight distinct methods with recent examples, contextualized within a common experimental and analytical framework. The selected methods are (1) Real-time NMR, (2) Exchange spectroscopy, (3) Lineshape analysis, (4) CPMG relaxation dispersion, (5) Rotating frame relaxation dispersion, (6) Nuclear spin relaxation, (7) Residual dipolar coupling, (8) Paramagnetic relaxation enhancement. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.