Integration Of Computational Models Research Articles

Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

Research on the application of computer science in wind power generation and the future trends

In the context of the growing global demand for clean energy, wind power, as an important form of renewable energy, is gradually occupying a dominant position. At the same time, the continuous development of computer technology has also brought new breakthroughs in wind power generation. Traditional wind power generation suffers from low power generation efficiency and large fluctuations in power generation. However, through the integration of wind power generation with advanced computer technologies, the efficiency and cost of wind power generation have been greatly optimized. This paper seeks to provide an overview of how computer science is utilized in wind power generation, focusing on the integration of computational modeling, simulation, and machine learning. It highlights future trendsand emphasizes the significance of developing digital twins and integrating machine learning with physical models for efficient wind farm management. Combining findings from various existing studies, this paper delves into the profound implications for the future of energy, highlighting the transformative role of computer science in advancing sustainable power solutions and shaping the landscape of renewable energy sources.

Photopharmacology of Ion Channels through the Light of the Computational Microscope.

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.

Examining Student Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

Forming a conceptual understanding of mathematics at students of technical universities

The global challenges that humanity is facing today pose the task of higher education to prepare a specialist with fundamental training and the ability to learn throughout life. Fundamentalization of education is not possible without the formation of students' conceptual understanding of the material studied. This problem is quite relevant in the study of mathematics due to the specific nature of this science. The researchers' lack of a unified point of view in determining the essence of the conceptual understanding of mathematics does not allow practitioners to develop tools for assessing the level of conceptual understanding among university students. The purpose the article is to identify and formulate the essential characteristics and pedagogical conditions for the formation of a conceptual understanding of mathematics, as well as to explore the possibilities and effectiveness of the integration of computer modeling as an instrument of forming a conceptual understanding in the process of teaching probability theory to students of technical universities. The study showed that a computer workshop, developed based on identified pedagogical conditions and taking into account the didactic capabilities of ICT in the educational process, is an effective means of developing conceptual understanding when studying a course in probability theory. Students whose curriculum included a workshop with elements of computer modeling have a more excellent knowledge of methodologically significant knowledge and the ability to relate previously learned material to new problems.

Science Teachers' Attitudes towards Computational Modeling in the Context of an Inquiry-Based Learning Module.

This study focuses on science teachers’ first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers’ self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers’ attitudes and self-efficacy were studied using survey questions and workshop activity transcripts. As expected, prior experience in physics teaching was related to teachers’ self-efficacy in teaching physics in 9th grade. Also, teachers’ prior experience with programming was strongly related to their self-efficacy regarding the programming component of model construction. Surprisingly, the short interaction with computational modeling increased the group’s self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Qualitative data provides additional insights into teachers’ predispositions towards the integration of computational modeling into the physics teaching.Electronic supplementary materialThe online version of this article (10.1007/s10956-020-09855-3) contains supplementary material, which is available to authorized users.

Role of cytokines in combinatorial immunotherapeutics of non-small cell lung cancer through systems perspective.

Lung cancer is the leading cause of deaths related to cancer and accounts for more than a million deaths per year. Various new strategies have been developed and adapted for treatment; still the survival for 5 years is just 16% in patients with non‐small cell lung cancer (NSCLC). Most of these strategies to combat NSCLC whether it is a drug molecule or immunotherapy/vaccine candidate require a big cost and time. Integration of computational modeling with systems biology has opened new avenues for understanding complex cancer biology. Resolving the complex interactions of various pathways and their crosstalk leading to oncogenic changes could identify new therapeutic targets with lesser cost and time. Herein, this review provides an overview of various aspects of NSCLC along with available strategies for its cure concluding with our insight into how systems approach could serve as a therapeutic intervention dissecting the immunologic parameters and cross talk between various pathways involved.

Role of Cytokines in Combinatorial Immunotherapeutics of Non-Small Cell Lung Cancer Through Systems Perspective

Lung cancer is the leading cause of deaths related to cancer and accounts for nearly 160, 340 deaths per year. Various new strategies have been developed and adapted for treatment; still the survival for five years is just 16% in patients with non-small-cell lung cancer (NSCLC). Most of these strategies to combat NSCLC whether it’s a drug molecule or immunotherapy/vaccine candidate require a big cost and time. Integration of computational modeling with systems biology has opened new avenues for understanding complex cancer biology. Resolving the complex interactions of various pathways and their crosstalk are leading to oncogenic changes could identify new therapeutic targets with lesser cost and time. Herein, this review provides an overview of various aspects of NSCLC along with available strategies for its cure with our insight into how systems approach could serve as a therapeutic intervention dissecting the immunological parameters and cross talk between various pathways involved.

Integration of computational modeling and experimental techniques to design fuel surrogates

Conventional gasoline comprises of a large number of hydrocarbons that makes it difficult to utilize in a model for prediction of its properties. Modeling is needed for a better understanding of the fuel flow and combustion behavior that are essential to enhance fuel quality and improve engine performance. A simplified alternative is to develop surrogate fuels that have fewer compounds and emulate certain important desired physical properties of the target fuels. Six gasoline blends were formulated through a computer aided model based technique “Mixed Integer Non-Linear Programming” (MINLP). Different target properties of the surrogate blends for example, Reid vapor pressure (RVP), dynamic viscosity (η), density (ρ), Research octane number (RON) and liquid-liquid miscibility of the surrogate blends) were calculated. In this study, more rigorous property models in a computer aided tool called Virtual Process-Product Design Laboratory (VPPD-Lab) are applied onto the defined compositions of the surrogate gasoline. The aim is to primarily verify the defined composition of gasoline by means of VPPD-Lab. ρ, η and RVP are calculated with more accuracy and constraints such as distillation curve and flash point on the blend design are also considered. A post-design experiment-based verification step is proposed to further improve and fine-tune the “best” selected gasoline blends following the computation work. Here, advanced experimental techniques are used to measure the RVP, ρ, η, RON and distillation temperatures. The experimental results are compared with the model predictions as well as the extended calculations in VPPD-Lab.

Is 3D Printing Transforming the Project Management Function in the Aerospace Industry

Traditional manufacturing techniques fade into the background, while manufacturing systems require using additive technologies for rapid adaptation to current demand and reduction of production cycle duration. Many large mechanical and aircraft engineering companies have already adopted additive manufacturing technologies in their future production strategy. The general concept of 3D printers on the basis of e-manufacturing principles is aimed at the integration of computer models of physical objects and processes. This change requires a big transformation of the enterprise business model, affecting either core or support activities. For instance, additive manufacturing could also change project management practices. This paper aims to identify how additive manifacturing could transform the project management function for the aerospace industry. Results show that projects could improve their performance by integrating additive manufacturing; however, project managers could adapt their approach to a new paradigm for lifecycle management and for leaner supply chain management in aerospace industry.

Increasing the Efficiency of Liquid Phase Alkylation of Benzene with Propylene Using the Method of Mathematical Modeling

In this study the existing problems of liquid-phase alkylation of benzene with propylene problems are indicated. The paper describes the stages alkylation of benzene with propylene mathematical model development. The model allows to define the composition of the product stream under changing of plant process parameters: temperature, benzene/propylene molar ratio and feed space velocity. The error of the model does not exceed 7.5%. «Alkylation» computer modeling system is developed in Borland Delphi 7 and the optimization module of the process parameters is called «Optimization». In Aspen HYSYS the technological scheme of rectification products alkylation of benzene with propylene using aluminum chloride catalyst was developed. Integration of computer models allowed to determine the technological modes of production, providing isopropylbenzene outlet with higher quality.

Integration of computational modeling with membrane transport studies reveals new insights into amino acid exchange transport mechanisms.

Uptake of system L amino acid substrates into isolated placental plasma membrane vesicles in the absence of opposing side amino acid (zero-trans uptake) is incompatible with the concept of obligatory exchange, where influx of amino acid is coupled to efflux. We therefore hypothesized that system L amino acid exchange transporters are not fully obligatory and/or that amino acids are initially present inside the vesicles. To address this, we combined computational modeling with vesicle transport assays and transporter localization studies to investigate the mechanisms mediating [14C]l-serine (a system L substrate) transport into human placental microvillous plasma membrane (MVM) vesicles. The carrier model provided a quantitative framework to test the 2 hypotheses that l-serine transport occurs by either obligate exchange or nonobligate exchange coupled with facilitated transport (mixed transport model). The computational model could only account for experimental [14C]l-serine uptake data when the transporter was not exclusively in exchange mode, best described by the mixed transport model. MVM vesicle isolates contained endogenous amino acids allowing for potential contribution to zero-trans uptake. Both L-type amino acid transporter (LAT)1 and LAT2 subtypes of system L were distributed to MVM, with l-serine transport attributed to LAT2. These findings suggest that exchange transporters do not function exclusively as obligate exchangers.—Widdows, K. L., Panitchob, N., Crocker, I. P., Please, C. P., Hanson, M. A., Sibley, C. P., Johnstone, E. D., Sengers, B. G., Lewis, R. M., Glazier, J. D. Integration of computational modeling with membrane transport studies reveals new insights into amino acid exchange transport mechanisms.

Action, Language and Neuroinformatics: An Introduction

This Special Issue is based on presentations at the Workshop on “Action, Language and Neuroinformatics” held in July of 2011. It contributes to the view that neuroinformatics must include the informatics of computational modeling of neural systems as well as the development and linkage of database resources for both models and empirical data. The papers introduce key results in empirical research, computational modeling, and neuroinformatics for two areas of neuroscience–neurolinguistics and the study of neural mechanisms underlying manual action and its recognition– and assess ways in which the study of language processing can benefit from models of action production. Because the two areas span the spectrum from animal studies to human studies, and from basic sensorimotor processes to cognition, they provide a setting for assessing the diverse challenges of creating computational models for neuroscience, for providing databases for the very different forms of empirical data now exploited by neuroscience, and for linkage of data and models in systems and cognitive neuroscience generally, not just within our two focal areas. The papers in this Special Issue are divided into four parts: Part 1, Databasing the Brain, presents three approaches to the development of neuroinformatics databases, including a new methodology for linking data and models in systems and cognitive neuroscience, tools for federating online neuroinformatics databases, and tools to link gene expression data to cognitive brain systems. Part 2, Action, Imitation and Gesture, provides two cases studies linking research on monkeys, apes, humans and machines, exemplifying neuroinformatics in the wide sense that embraces computational modeling as well as database construction. Part 3, Language, develops this story in relation to the uniquely human capacity for language, offering models of human syntactic encoding and decoding, actor-based language comprehension and the linkage of visual scenes to language via template construction grammar, in each case considering how to test the models against data from human behavior and brain imaging. Finally, Part 4 builds upon a series of intense discussions held at the Workshop on the present and future of neuroinformatics, with especial emphasis on the integration of computational models with empirical data.

Just an additional hydrogen bond can dramatically reduce the catalytic activity of Bacillus subtilis lipase A I12T mutant: An integration of computational modeling and experimental analysis

Understanding the structural basis and energetic property of hydrogen bonding and its effects on enzymatic activity is fundamentally important for the rational design of specific enzymes with desired biological functions. In the current study, site-directed mutagenesis analysis preliminarily revealed that the amino acid substitution of Ile12 with Thr12 (I12T) dramatically reduced the hydrolytic activity of Bacillus subtilis lipase A. A further computational investigation proposed that the I12T mutation would establish a geometrically perfect hydrogen bond between the mutated Thr12 and catalytic Ser77 of lipase A, which considerably impaired the catalytic capability of lipase A through two distinct but complementary approaches: rigidizing the enzyme active site and lowering the nucleophilic ability of the catalytic residue Ser77. To verify this hypothesis, a homogenous mutation I12S serving as the control to the I12T mutation was created to examine the hydrogen bonding effect on enzymatic activity. It was found that the I12S mutant only suffered from a slight damage in its hydrolytic ability due to absence of the hydrogen bond originally present at the Thr12–Ser77 interface in the I12T mutant, which was further characterized systematically by quantum mechanics/molecular mechanics (QM/MM) modeling, atom-in-molecules (AIM) analysis and molecular dynamics (MD) simulation. It is suggested that the hydrogen bond arising from the I12T mutation in lipase A can considerably reduce the flexibility and mobility of the enzyme active site, thus impairing the catalytic activity of the lipase A I12T mutant remarkably; the activity loss can be, however, largely recovered by replacing Thr residue at the 12th position of I12T mutant with its analog Ser, which is chemically similar to Thr but cannot form effective hydrogen bonding with Ser77.

Computer models to study uterine activation at labour

Improving our understanding of the initiation of labour is a major aim of modern obstetric research, in order to better diagnose and treat pregnant women in which the process occurs abnormally. In particular, increased knowledge will help us identify the mechanisms responsible for preterm labour, the single biggest cause of neonatal morbidity and mortality. Attempts to improve our understanding of the initiation of labour have been restricted by the inaccessibility of gestational tissues to study during pregnancy and at labour, and by the lack of fully informative animal models. However, computer modelling provides an exciting new approach to overcome these restrictions and offers new insights into uterine activation during term and preterm labour. Such models could be used to test hypotheses about drugs to treat or prevent preterm labour. With further development, an effective computer model could be used by healthcare practitioners to develop personalized medicine for patients on a pregnancy-by-pregnancy basis. Very promising work is already underway to build computer models of the physiology of uterine activation and contraction. These models aim to predict changes and patterns in uterine electrical excitation during term labour. There have been far fewer attempts to build computer models of the molecular pathways driving uterine activation and there is certainly scope for further work in this area. The integration of computer models of the physiological and molecular mechanisms that initiate labour will be particularly useful.

On the integration of trust with negotiation, argumentation and semantics

AbstractAgreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing.

Overview of Computational Methods Employed in Early-Stage Drug Discovery

The understanding of biomolecular interactions ultimately depends on knowledge about the structural and dynamic details of the interacting system. Rational structure-based drug design implements computational methodology in this rationale. Together with increasing throughput of structural biology, molecular modeling has progressively contributed to rational drug design and elucidation of nontoxic and patient-tailored interventions, helping to make drug development more cost-efficient. But in this challenging time for the pharmaceutical industry, the successful discovery of novel therapeutics should rely on integration of computational modeling with experimentation when it comes to ligand-binding energetics, system flexibility and genetic diversity/heterogeneity of the target. Moreover, it appears that many drugs--even those for which specific receptors have been identified--intercalate in biological membranes, which could also become the actual target. Understanding the drug-target and drug-unwanted-target interactions at the atomic level is fundamental in the initial phases of the drug development process. Molecular dynamics simulations and complementary computational methods are already contributing in this endeavor for the soluble pharmacological targets and show an increasing importance in the understanding of membrane-ligand interactions.

Integration of computer modeling and initial studies of site-directed mutagenesis to improve cellulase activity on Cel9A from Thermobifida fusca.

Cellulases are a complex group of enzymes that are fundamental for the degradation of amorphous and crystalline cellulose in lignocellulosic material. Unfortunately, cellulases have a low catalytic efficiency on their substrates when compared to similar enzymes such as amylases, which has led to a strong interest in improving their activities. Thermobifida fusca secretes six cellulose degrading enzymes: two exo- and three endocellulases and an endo/exocellulase Cel9A (formerly called E4). Cel9A shows unique properties because of its endo- and exocellulase characteristics, strong activity on crystalline cellulose, and good synergistic properties. Therefore, it is an excellent target for mutagenesis techniques to improve crystalline cellulose degradation. In this article, we describe research conducted to improve Cel9A catalytic efficiency using a rational design and computer modeling. A computer model of Cel9A was created using the program CHARMM plus its PDB structure and a cellohexose molecule attached to the catalytic site as a starting model. Initially molecular graphics and energy minimization were used to extend the cellulose chain to 18 glucose residues spanning the catalytic domain and cellulose-binding domain (CBD). The interaction between this cellulose chain and conserved CBD residues was determined in the model, and mutations likely to improve the binding properties of the CBD were selected. Site-directed mutations were carried out using the pET vector pET26b, Escherichia coli DH5-alpha, and the QuickChange mutagenesis method. E. coli BL21-DE3 was used for protein production and expression. The purified proteins were assayed for enzymatic activity on filter paper, swollen cellulose, bacterial microcrystalline cellulose, and carboxymethylcellulose (CMC). Mutation of the conserved residue F476 to Y476 gave a 40% improved activity in assays with soluble and amorphous cellulose such as CMC and swollen cellulose.

Integration of Computational Models Inspired by Economics and Genetics

Multi-agent software systems can be modelled as complex dynamic systems in which agent adaptation and interaction occur continuously and concurrently. A genetics-inspired view has interaction occurring via exchange of agent characteristics encoded as genes, plus measures of success. An economics-inspired view has agent adaptation driven by changes in prices, and supply and demand. This paper describes work towards a synergistic combination of these views.

DELFTMDD: A tool for integration of independent models in a decision support system

The need for integrated analysis poses a request for integration of computer models, paying extra attention to interfaces, data management and user interaction. Sector wide standardization using data dictionaries and data exchange formats can be a great help in streamlining data exchange. However, this type of standardization can have some drawbacks for a generic framework for model integration. Another concept, called Model Data Dictionary (MDD), has been developed as an alternative for proper data management. The concept is a variant on the federated database concept, a concept where local databases maintain their autonomy, while an interconnection database provides a link for sharing data. The MDD is based on a highly generic data model for geographic referenced objects, which if needed facilitates mapping of the sector wide data dictionary. External interfaces provide, in combination with a data format mapping component, a link to SQL-based data sources and model specific databases. A generic Object Data Editor (ODE), linked to the MDD, has been proposed for provision of a common data editing facility for mathematical models. A test version of the combined MDD/ODE-concept has shown the applicability for integration of all kinds of geographic object oriented mathematical models (both simulation and optimization).