The work is devoted to the study of the qualitative properties of solutions of the mathematical model of the dynamics of aseptic inflammation and the issues of their practical application. Data are presented that indicate the potential use of the model to describe a wide range of biological processes and diseases in which aseptic inflammation is a pathogenic factor. The multistability of the dynamic system in the vicinity of biologically significant solutions and the corresponding range of parameter values is found. It is shown that, depending on the initial conditions, the model describes not only the conditional norm state (in the absence of a wound) and the classical acute inflammatory reaction to damage, but also its transition to a chronic form. The trigger mechanism of switching states of the system is investigated. The possibilities of the model as an effective tool for studying and early predicting the nature of the immune response, as well as for analyzing hypothetical therapeutic strategies that prevent the progression of acute inflammation into chronic inflammation are shown.
{"title":"The Trigger Model of the Dynamics of Acute and Chronic Aseptic Inflammation","authors":"T.S. Mikhakhanova, O. F. Voropaeva","doi":"10.17537/2022.17.266","DOIUrl":"https://doi.org/10.17537/2022.17.266","url":null,"abstract":"\u0000 The work is devoted to the study of the qualitative properties of solutions of the mathematical model of the dynamics of aseptic inflammation and the issues of their practical application. Data are presented that indicate the potential use of the model to describe a wide range of biological processes and diseases in which aseptic inflammation is a pathogenic factor. The multistability of the dynamic system in the vicinity of biologically significant solutions and the corresponding range of parameter values is found. It is shown that, depending on the initial conditions, the model describes not only the conditional norm state (in the absence of a wound) and the classical acute inflammatory reaction to damage, but also its transition to a chronic form. The trigger mechanism of switching states of the system is investigated. The possibilities of the model as an effective tool for studying and early predicting the nature of the immune response, as well as for analyzing hypothetical therapeutic strategies that prevent the progression of acute inflammation into chronic inflammation are shown.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88892385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Akhmetzianova, T. Davletkulov, R. Garafutdinov, I. Gubaydullin
This paper presents a program which allows user to do primer design for identifying DNA target site or a whole genome with a goal of performing loopmediated isothermal amplification. The review of the most popular existing primer design programs for LAMP is carried out. Recommended conditions are presented in the paper. They are required to be taken in consideration during the process of primer design for loop-mediated isothermal amplification. These are the conditions: primer’s length, GC-content, amplicon average size, annealing temperature and distance between primers. A search for primer positions in genome is needed since loop-mediated isothermal amplification requires primer kits that consist of 6 primers in order for primer design to be done. The Aho–Corasick algorithm was proposed for a search implementation. This algorithm is capable of simultaneous search for a number of sample (primer) entries in a longer sequence (a fragment or a whole genome). This software allows the search for primers in genomes of various length and it groups primers by kits, which in turn could be applied in laboratory experiments. These kits are formed according both to the recommended conditions of primer selection for performing loop-mediated isothermal amplification and to the initial conditions, which are determined by the user before the process. After that, the user may choose the best option for their case from a list of primer kits that are being created as a result of performed computer analysis. The test run of the program was done during the search for a specific primer kit that is meant to be used for performing loop-mediated isothermal amplification of genome with a goal of detection of novel coronavirus infection SARS-CoV-2, a virus that triggers a dangerous disease, COVID-19. The software was developed using Python with BioPython and Pyahocorasick libraries and available at the link: https://cloud.mail.ru/public/C7av/QCkSiUomz.
{"title":"Application of the Aho-Korasik Algorithm for the Selection of Primers for Loop Isothermal Amplification","authors":"L. Akhmetzianova, T. Davletkulov, R. Garafutdinov, I. Gubaydullin","doi":"10.17537/2022.17.250","DOIUrl":"https://doi.org/10.17537/2022.17.250","url":null,"abstract":"\u0000 This paper presents a program which allows user to do primer design for identifying DNA target site or a whole genome with a goal of performing loopmediated isothermal amplification. The review of the most popular existing primer design programs for LAMP is carried out. Recommended conditions are presented in the paper. They are required to be taken in consideration during the process of primer design for loop-mediated isothermal amplification. These are the conditions: primer’s length, GC-content, amplicon average size, annealing temperature and distance between primers. A search for primer positions in genome is needed since loop-mediated isothermal amplification requires primer kits that consist of 6 primers in order for primer design to be done. The Aho–Corasick algorithm was proposed for a search implementation. This algorithm is capable of simultaneous search for a number of sample (primer) entries in a longer sequence (a fragment or a whole genome). This software allows the search for primers in genomes of various length and it groups primers by kits, which in turn could be applied in laboratory experiments. These kits are formed according both to the recommended conditions of primer selection for performing loop-mediated isothermal amplification and to the initial conditions, which are determined by the user before the process. After that, the user may choose the best option for their case from a list of primer kits that are being created as a result of performed computer analysis. The test run of the program was done during the search for a specific primer kit that is meant to be used for performing loop-mediated isothermal amplification of genome with a goal of detection of novel coronavirus infection SARS-CoV-2, a virus that triggers a dangerous disease, COVID-19. The software was developed using Python with BioPython and Pyahocorasick libraries and available at the link: https://cloud.mail.ru/public/C7av/QCkSiUomz.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79035609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Starunova, S. Rudnev, A. Ivanova, V. G. Semenova, V. Starodubov
An empirical Benford's law which describes the probability of the appearance of certain first significant digits in many distributions taken from real life, is used to identify anomalies in various kinds of data. Our aim was to test Benford's law to assess the quality of mass preventive screening data on the example of bioelectrical impedance analysis (BIA) data from Moscow health centers. As was shown earlier, such a data is characterized by a high level of contamination by artificially generated and falsified data. A generated 2010–2019 database of BIA measurements contained 1361019 measurement records in the age range of the examined persons from 5 to 96 years. Application of the expert quality assessment algorithm, which was used as a reference for evaluation of the effectiveness of Benford analysis, revealed a high percentage of incorrect data (66.5 %) which was dominated by falsified data. To characterize the degree of the data compliance with Benford's law, the mean absolute deviations of the frequency distributions of the first and first two significant digits deviations from the proper values and chi-squared statistics for the tenth powers of the standardized resistance, reactance, and resistance index values were assessed for each health center. A significant correlation was observed between the data deviation from Benford's law and the percentage of incorrect data as provided by the expert quality assessment algorithm (ρmax = 0.66 and 0.62 for the mean absolute deviations and χ2 statistics, respectively, based on the resistance value and the first significant digit). It is suggested that deviation of the BIA data from Benford's law serves as a sufficient, but not a necessary, condition for their contamination. For those health centers, in which most of the incorrect data were represented by multiple measurements of the same person under the guise of different ones, the data were in good agreement with Benford's law. If the structure of incorrect data was dominated by measurements of the calibration block, software emulations of BIA measurements and outliers, then the use of Benford's law made it possible to effectively rank health centers by the level of data authenticity.
{"title":"Application of Benford's Law for Quality Assessment of Preventive Screening Data","authors":"O. Starunova, S. Rudnev, A. Ivanova, V. G. Semenova, V. Starodubov","doi":"10.17537/2022.17.230","DOIUrl":"https://doi.org/10.17537/2022.17.230","url":null,"abstract":"\u0000 An empirical Benford's law which describes the probability of the appearance of certain first significant digits in many distributions taken from real life, is used to identify anomalies in various kinds of data. Our aim was to test Benford's law to assess the quality of mass preventive screening data on the example of bioelectrical impedance analysis (BIA) data from Moscow health centers. As was shown earlier, such a data is characterized by a high level of contamination by artificially generated and falsified data. A generated 2010–2019 database of BIA measurements contained 1361019 measurement records in the age range of the examined persons from 5 to 96 years. Application of the expert quality assessment algorithm, which was used as a reference for evaluation of the effectiveness of Benford analysis, revealed a high percentage of incorrect data (66.5 %) which was dominated by falsified data. To characterize the degree of the data compliance with Benford's law, the mean absolute deviations of the frequency distributions of the first and first two significant digits deviations from the proper values and chi-squared statistics for the tenth powers of the standardized resistance, reactance, and resistance index values were assessed for each health center. A significant correlation was observed between the data deviation from Benford's law and the percentage of incorrect data as provided by the expert quality assessment algorithm (ρmax = 0.66 and 0.62 for the mean absolute deviations and χ2 statistics, respectively, based on the resistance value and the first significant digit). It is suggested that deviation of the BIA data from Benford's law serves as a sufficient, but not a necessary, condition for their contamination. For those health centers, in which most of the incorrect data were represented by multiple measurements of the same person under the guise of different ones, the data were in good agreement with Benford's law. If the structure of incorrect data was dominated by measurements of the calibration block, software emulations of BIA measurements and outliers, then the use of Benford's law made it possible to effectively rank health centers by the level of data authenticity.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"49 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83212337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Kiryanova, I. Kiryanov, B. Kuluev, R. Garafutdinov, A. V. Chemeris, I. Gubaydullin
In this work, we propose a new method for identifying organisms of multiplex polymerase chain reaction (PCR) with arbitrary primers in silico (multiplex in silico RAPD-analysis) for the unique identification of living organisms. The results of computer modeling search of possible primer annealing sites in genomic DNA, and their convertation into the genomic barcode format, are proposed. These data with information about used primers that can be unique for genomes. A comparative analysis of genomic barcodes of species of related plant species was carried out in order to classify them on the level of species and lines in the future. A pairwise analysis of the location of the same or similar amplicons within different subgenomes and genomes is presented. The genomes of wheat and Aegilops in FASTA files format are presented as the research samples. The proposed method makes possible to predict the success of the multiplex polymerase chain reaction using special primers in the laboratory. This technology allows the analysis of the entire genomic DNA, rather than fragments of the genome.
{"title":"Multiplex in silico RAPD-Analysis for Genome Barcoding","authors":"O. Kiryanova, I. Kiryanov, B. Kuluev, R. Garafutdinov, A. V. Chemeris, I. Gubaydullin","doi":"10.17537/2022.17.208","DOIUrl":"https://doi.org/10.17537/2022.17.208","url":null,"abstract":"\u0000 In this work, we propose a new method for identifying organisms of multiplex polymerase chain reaction (PCR) with arbitrary primers in silico (multiplex in silico RAPD-analysis) for the unique identification of living organisms. The results of computer modeling search of possible primer annealing sites in genomic DNA, and their convertation into the genomic barcode format, are proposed. These data with information about used primers that can be unique for genomes. A comparative analysis of genomic barcodes of species of related plant species was carried out in order to classify them on the level of species and lines in the future. A pairwise analysis of the location of the same or similar amplicons within different subgenomes and genomes is presented. The genomes of wheat and Aegilops in FASTA files format are presented as the research samples. The proposed method makes possible to predict the success of the multiplex polymerase chain reaction using special primers in the laboratory. This technology allows the analysis of the entire genomic DNA, rather than fragments of the genome.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87178851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N.A. Shuldau, A. Yushkevich, K. V. Furs, A. Tuzikov, A. Andrianov
Two generative deep learning models have been developed for the computer-aided design of potential inhibitors of the SARS-CoV-2 main protease (MPro), an enzyme critically important for the virus replication and transcription, and, therefore, presenting a promising target for the design of effective antiviral drugs. To solve this problem, we formed a training library of small molecules containing structural elements capable of providing specific and effective interactions of potential ligands with the SARS-CoV-2 MPro catalytic site. The architecture of generative models was developed and implemented to generate new high-affinity ligands of this functionally important SARS-CoV-2 protein. The neural network was trained and tested on the compounds from the training library, and the results of training and operation in two different generation modes were evaluated. The use of generative models in conjunction with the molecular docking demonstrated their great potential for filling the unexplored regions of the chemical space with novel molecules with pre-defined properties, which is confirmed by the obtained results according to which out of 4805 compounds generated by the neural network only one compound was present in the original data set.
{"title":"Development of a Deep Learning Generative Neural Network for Computer-Aided Design of Potential SARS-Cov-2 Inhibitors","authors":"N.A. Shuldau, A. Yushkevich, K. V. Furs, A. Tuzikov, A. Andrianov","doi":"10.17537/2022.17.188","DOIUrl":"https://doi.org/10.17537/2022.17.188","url":null,"abstract":"\u0000 Two generative deep learning models have been developed for the computer-aided design of potential inhibitors of the SARS-CoV-2 main protease (MPro), an enzyme critically important for the virus replication and transcription, and, therefore, presenting a promising target for the design of effective antiviral drugs. To solve this problem, we formed a training library of small molecules containing structural elements capable of providing specific and effective interactions of potential ligands with the SARS-CoV-2 MPro catalytic site. The architecture of generative models was developed and implemented to generate new high-affinity ligands of this functionally important SARS-CoV-2 protein. The neural network was trained and tested on the compounds from the training library, and the results of training and operation in two different generation modes were evaluated. The use of generative models in conjunction with the molecular docking demonstrated their great potential for filling the unexplored regions of the chemical space with novel molecules with pre-defined properties, which is confirmed by the obtained results according to which out of 4805 compounds generated by the neural network only one compound was present in the original data set.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87488543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RBCK1 deficiency is a rare congenital autoinflammatory disease that causes inflammatory disruption on the molecular level. This deficiency has three major clinical manifestations: increased sensitivity to bacterial infections, autoinflammation syndrome, and the accumulation of amylopectin in skeletal muscle. The amylopectinosis causes myopathy and cardiomyopathy. The pathogenesis of the disease is poorly investigated and may include unnoticed relationships. We performed gene expression analysis on patients with RBCK1 deficiency and three other autoinflammatory diseases. The identification of differentially expressed genes revealed a large number of downregulated genes that are involved in the activation of essential metabolic and immune pathways, including NF-kB and Pi3k-Akt-mTOR. Signaling pathways were analysed using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology resource. Predicted protein-protein interactions were retrieved from the STRING (Search Tool for the Retrieval of Interacting proteins database). Besides the primary involvement of RBCK1 in disease pathology, several downregulated pathways aggravate symptoms of myopathy, cardiomyopathy, and bacterial disease. The studied pathways may serve as new targets for the development of compensatory therapies for patients with RBCK1 deficiency.
{"title":"Additional Pathogenic Pathways in RBCK1 Deficiency","authors":"E. Demicheva, K. Shinwari, K. Ushenin, M. Bolkov","doi":"10.17537/2022.17.174","DOIUrl":"https://doi.org/10.17537/2022.17.174","url":null,"abstract":"\u0000 RBCK1 deficiency is a rare congenital autoinflammatory disease that causes inflammatory disruption on the molecular level. This deficiency has three major clinical manifestations: increased sensitivity to bacterial infections, autoinflammation syndrome, and the accumulation of amylopectin in skeletal muscle. The amylopectinosis causes myopathy and cardiomyopathy. The pathogenesis of the disease is poorly investigated and may include unnoticed relationships. We performed gene expression analysis on patients with RBCK1 deficiency and three other autoinflammatory diseases. The identification of differentially expressed genes revealed a large number of downregulated genes that are involved in the activation of essential metabolic and immune pathways, including NF-kB and Pi3k-Akt-mTOR. Signaling pathways were analysed using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology resource. Predicted protein-protein interactions were retrieved from the STRING (Search Tool for the Retrieval of Interacting proteins database). Besides the primary involvement of RBCK1 in disease pathology, several downregulated pathways aggravate symptoms of myopathy, cardiomyopathy, and bacterial disease. The studied pathways may serve as new targets for the development of compensatory therapies for patients with RBCK1 deficiency.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88970828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today in many articles charge propagation in biopolymers, for example, in DNA, have been modeled with different variants of boundary conditions – free ends or ring. It is assumed that for long chains, the ends practically do not affect the charge dynamics, and this is true in most cases. In this note, we discuss the case when these boundary conditions lead to significantly different results.
{"title":"A note on the formation of polaron states in a homogeneous chain","authors":"V. Lakhno, N. Fialko","doi":"10.17537/2022.17.171","DOIUrl":"https://doi.org/10.17537/2022.17.171","url":null,"abstract":"\u0000 Today in many articles charge propagation in biopolymers, for example, in DNA, have been modeled with different variants of boundary conditions – free ends or ring. It is assumed that for long chains, the ends practically do not affect the charge dynamics, and this is true in most cases. In this note, we discuss the case when these boundary conditions lead to significantly different results. \u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76330447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Here we present a stochastic model of the spread of Covid-19 infection in a certain region. The model is a continuous-discrete random process that takes into account a number of parallel processes, such as the non-stationary influx of latently infected individuals into the region, the passage by individuals of various stages of an infectious disease, the vaccination of the population, and the re-infection of some of the recovered and vaccinated individuals. The duration of stay of individuals in various stages of an infectious disease is described using distributions other than exponential. An algorithm for numerical statistical modeling of the dynamics of the spread of infection among the population of the region based on the Monte Carlo method has been developed. To calibrate the model, we used data describing the level of seroprevalence of the population of the Novosibirsk Region in the first wave of the Covid-19 epidemic in 2020. The results of computational experiments with the model are presented for studying the dynamics of the spread of infection under vaccination of the population of the region.
{"title":"Stochastic Modeling of Dynamics of the Spread of COVID-19 Infection Taking Into Account the Heterogeneity of Population According To Immunological, Clinical and Epidemiological Criteria","authors":"N. Pertsev, K. Loginov, A. Lukashev, Y. Vakulenko","doi":"10.17537/2022.17.43","DOIUrl":"https://doi.org/10.17537/2022.17.43","url":null,"abstract":"\u0000 Here we present a stochastic model of the spread of Covid-19 infection in a certain region. The model is a continuous-discrete random process that takes into account a number of parallel processes, such as the non-stationary influx of latently infected individuals into the region, the passage by individuals of various stages of an infectious disease, the vaccination of the population, and the re-infection of some of the recovered and vaccinated individuals. The duration of stay of individuals in various stages of an infectious disease is described using distributions other than exponential. An algorithm for numerical statistical modeling of the dynamics of the spread of infection among the population of the region based on the Monte Carlo method has been developed. To calibrate the model, we used data describing the level of seroprevalence of the population of the Novosibirsk Region in the first wave of the Covid-19 epidemic in 2020. The results of computational experiments with the model are presented for studying the dynamics of the spread of infection under vaccination of the population of the region.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79929154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Method named as variant approach to recognizing genus of coronavirus that is based on frequency of codon distribution in viral ORF1ab and genes of structural proteins (S, M and N) was proposed in the work. This method uses modified statistics whose efficiency was demonstrated earlier for flavivirus species recognition. To recognize genus of coronavirus the variant approach considers both various combinations of several structural coronavirus genes and individual structural genes. Finally, coronavirus genus is determined in the result of analysis of all variants considered. The method proposed was developed with the help of learning sample from prototype viral variants of Alphacoronavirus, Betacoronavirus, Deltacoronavirus and Gammacoronavirus genus. Application of the variant approach to recognizing genus of coronavirus has demonstrated the approach high assurance at level of 95 %. Among all variants of joint analysis, the most reliability (98 %) in recognizing genus has been achieved if codon frequency of the ORF1ab was used. Variant approach has revealed a phenomenon of mosaic structure in coronavirus genomes, i.e., when the results of genus recognition for a few genes differ from final conclusion about coronavirus genus. It seems that such phenomenon reflects homologous recombinations of the genes between various species of the coronaviruses and plasticity of their genomes in evolutionary processes.
{"title":"Coronavirus Genus Recognition Based on Prototype Virus Variants","authors":"M. Chaley, V. Kutyrkin","doi":"10.17537/2022.17.10","DOIUrl":"https://doi.org/10.17537/2022.17.10","url":null,"abstract":"\u0000Method named as variant approach to recognizing genus of coronavirus that is based on frequency of codon distribution in viral ORF1ab and genes of structural proteins (S, M and N) was proposed in the work. This method uses modified statistics whose efficiency was demonstrated earlier for flavivirus species recognition. To recognize genus of coronavirus the variant approach considers both various combinations of several structural coronavirus genes and individual structural genes. Finally, coronavirus genus is determined in the result of analysis of all variants considered. The method proposed was developed with the help of learning sample from prototype viral variants of Alphacoronavirus, Betacoronavirus, Deltacoronavirus and Gammacoronavirus genus. Application of the variant approach to recognizing genus of coronavirus has demonstrated the approach high assurance at level of 95 %. Among all variants of joint analysis, the most reliability (98 %) in recognizing genus has been achieved if codon frequency of the ORF1ab was used. Variant approach has revealed a phenomenon of mosaic structure in coronavirus genomes, i.e., when the results of genus recognition for a few genes differ from final conclusion about coronavirus genus. It seems that such phenomenon reflects homologous recombinations of the genes between various species of the coronaviruses and plasticity of their genomes in evolutionary processes.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89830186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A complex of programs has been developed for computer modeling of multichannel time series recorded in various experiments on electromagnetic fields created by the human body. Sets of coordinates and directions of sensors for magnetic encephalographs of several types, electroencephalographs and magnetic cardiographs are used as models of devices. To study the human brain, magnetic resonance tomograms are used as head models; to study the heart, a body model in the form of a half-space with a flat boundary is used. The sources are placed in the model space, for them the direct problem is solved in the physical model corresponding to the device used. For a magnetic encephalograph and an electroencephalograph, an equivalent current dipole model in a spherical conductor is used, for a magnetic cardiograph, an equivalent current dipole model in a flat conductor or a magnetic dipole model is used. For each source, a time dependence is set and a multichannel time series is calculated. Then the time series from all sources are summed and the noise component is added. The program consists of three modules: an input-output module, a calculation module and a visualization module. The input-output module is responsible for loading device models, brain models, and field source parameters. The calculation module is responsible for directly calculating the field and transforming coordinates between the index system and the head system. The visualization module is responsible for the image of the brain model, the position of the field sources, a graphical representation of the amplitude-time dependence of the field sources and the calculated values of the total field. The user interface has been developed. The software package provides: interactive placement of field sources in the head or body space and editing of the amplitude-time dependence; batch loading of a large number of sources; noise modeling; simulation of low-channel planar magnetometers of various orders, specifying the shape of the device, the number of sensors and their parameters. Magnetic and electric fields produced by sources in the brain areas responsible for processing speech stimuli are considered. The resulting multichannel signal can be used to test various data analysis methods and for the experiment planning.
{"title":"A Software Package for the Modeling of Electrophysiological Activity Data","authors":"A. Boyko, S. Rykunov, M. Ustinin","doi":"10.17537/2022.17.1","DOIUrl":"https://doi.org/10.17537/2022.17.1","url":null,"abstract":"\u0000A complex of programs has been developed for computer modeling of multichannel time series recorded in various experiments on electromagnetic fields created by the human body. Sets of coordinates and directions of sensors for magnetic encephalographs of several types, electroencephalographs and magnetic cardiographs are used as models of devices. To study the human brain, magnetic resonance tomograms are used as head models; to study the heart, a body model in the form of a half-space with a flat boundary is used. The sources are placed in the model space, for them the direct problem is solved in the physical model corresponding to the device used. For a magnetic encephalograph and an electroencephalograph, an equivalent current dipole model in a spherical conductor is used, for a magnetic cardiograph, an equivalent current dipole model in a flat conductor or a magnetic dipole model is used. For each source, a time dependence is set and a multichannel time series is calculated. Then the time series from all sources are summed and the noise component is added. The program consists of three modules: an input-output module, a calculation module and a visualization module. The input-output module is responsible for loading device models, brain models, and field source parameters. The calculation module is responsible for directly calculating the field and transforming coordinates between the index system and the head system. The visualization module is responsible for the image of the brain model, the position of the field sources, a graphical representation of the amplitude-time dependence of the field sources and the calculated values of the total field. The user interface has been developed. The software package provides: interactive placement of field sources in the head or body space and editing of the amplitude-time dependence; batch loading of a large number of sources; noise modeling; simulation of low-channel planar magnetometers of various orders, specifying the shape of the device, the number of sensors and their parameters. Magnetic and electric fields produced by sources in the brain areas responsible for processing speech stimuli are considered. The resulting multichannel signal can be used to test various data analysis methods and for the experiment planning.\u0000","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77301250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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