Pub Date : 2020-08-14DOI: 10.1101/2020.08.14.244897
Ramana Chilakamarti
Respiratory virus infection is one of the leading causes of death in the world. Activation of the Jak-Stat pathway by Interferon-alpha/beta (IFN-/{beta}) in lung epithelial cells is critical for innate immunity to respiratory viruses. Genetic and biochemical studies have shown that transcriptional regulation by IFN-/{beta} required the formation of Interferon-stimulated gene factor-3 (ISGF-3) complex consisting of Stat1, Stat2, and Irf9 transcription factors. Furthermore, IFN /{beta} receptor activates multiple signal transduction pathways in parallel to the Jak-Stat pathway and induces several transcription factors at mRNA levels resulting in the secondary and tertiary rounds of transcription. Transcriptional factor profiling in the transcriptome and RNA analysis revealed that Early growth response-1 (Egr-1) was rapidly induced by IFN-/{beta} and Toll-like receptor (TLR) ligands in multiple cell types. Studies in mutant cell lines lacking components of the ISGF-3 complex revealed that IFN-{beta} induction of Egr-1 was independent of Stat1, Stat2, or Irf9. Activation of the Mek/Erk-1/2 pathway was implicated in the rapid induction of Egr-1 by IFN-{beta} in serum-starved mouse lung epithelial cells. Interrogation of multiple microarray datasets revealed that respiratory viruses including coronaviruses regulated Egr-1 expression in human lung cell lines. Furthermore, Egr-1 inducible genes including transcription factors, mediators of cell growth, and chemokines were differentially regulated in the human lung cell lines after coronavirus infection, and in the lung biopsies of COVID-19 patients. Rapid induction by interferons, TLR ligands, and respiratory viruses suggests a critical role for Egr-1 in antiviral response and inflammation with potential implications for human health and disease.
{"title":"Regulation of early growth response-1 (Egr-1) gene expression by Stat1-independent type I interferon signaling and respiratory viruses","authors":"Ramana Chilakamarti","doi":"10.1101/2020.08.14.244897","DOIUrl":"https://doi.org/10.1101/2020.08.14.244897","url":null,"abstract":"Respiratory virus infection is one of the leading causes of death in the world. Activation of the Jak-Stat pathway by Interferon-alpha/beta (IFN-/{beta}) in lung epithelial cells is critical for innate immunity to respiratory viruses. Genetic and biochemical studies have shown that transcriptional regulation by IFN-/{beta} required the formation of Interferon-stimulated gene factor-3 (ISGF-3) complex consisting of Stat1, Stat2, and Irf9 transcription factors. Furthermore, IFN /{beta} receptor activates multiple signal transduction pathways in parallel to the Jak-Stat pathway and induces several transcription factors at mRNA levels resulting in the secondary and tertiary rounds of transcription. Transcriptional factor profiling in the transcriptome and RNA analysis revealed that Early growth response-1 (Egr-1) was rapidly induced by IFN-/{beta} and Toll-like receptor (TLR) ligands in multiple cell types. Studies in mutant cell lines lacking components of the ISGF-3 complex revealed that IFN-{beta} induction of Egr-1 was independent of Stat1, Stat2, or Irf9. Activation of the Mek/Erk-1/2 pathway was implicated in the rapid induction of Egr-1 by IFN-{beta} in serum-starved mouse lung epithelial cells. Interrogation of multiple microarray datasets revealed that respiratory viruses including coronaviruses regulated Egr-1 expression in human lung cell lines. Furthermore, Egr-1 inducible genes including transcription factors, mediators of cell growth, and chemokines were differentially regulated in the human lung cell lines after coronavirus infection, and in the lung biopsies of COVID-19 patients. Rapid induction by interferons, TLR ligands, and respiratory viruses suggests a critical role for Egr-1 in antiviral response and inflammation with potential implications for human health and disease.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62310925","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}
Pub Date : 2020-07-16DOI: 10.1101/2020.07.16.20155036
Wolfgang Bock, Thomas Götz, Y. Jayathunga, R. Rockenfeller
Abstract At the end of 2019, an outbreak of a new coronavirus, called SARS–CoV–2, was reported in China and later in other parts of the world. First infection reported in Germany by the end of January 2020 and on March 16th, 2020 the federal government announced a partial lockdown in order to mitigate the spread. Since the dynamics of new infections started to slow down, German states started to relax the confinement measures as to May 6th, 2020. As a fall back option, a limit of 50 new infections per 100,000 inhabitants within seven days was introduced for each district in Germany. If a district exceeds this limit, measures to control the spread of the virus should be taken. Based on a multi–patch SEAIRD–type model, we will simulate the effect of choosing a specific upper limit for new infections. We investigate, whether the politically motivated bound is low enough to detect new outbreaks at an early stage. Subsequently, we introduce an optimal control problem to tackle the multi–criteria problem of finding a bound for new infections that is low enough to avoid new outbreaks, which might lead to an overload of the health care system, but is large enough to curb the expected economic losses.
{"title":"Are the upper bounds for new SARS-CoV-2 infections in Germany useful?","authors":"Wolfgang Bock, Thomas Götz, Y. Jayathunga, R. Rockenfeller","doi":"10.1101/2020.07.16.20155036","DOIUrl":"https://doi.org/10.1101/2020.07.16.20155036","url":null,"abstract":"Abstract At the end of 2019, an outbreak of a new coronavirus, called SARS–CoV–2, was reported in China and later in other parts of the world. First infection reported in Germany by the end of January 2020 and on March 16th, 2020 the federal government announced a partial lockdown in order to mitigate the spread. Since the dynamics of new infections started to slow down, German states started to relax the confinement measures as to May 6th, 2020. As a fall back option, a limit of 50 new infections per 100,000 inhabitants within seven days was introduced for each district in Germany. If a district exceeds this limit, measures to control the spread of the virus should be taken. Based on a multi–patch SEAIRD–type model, we will simulate the effect of choosing a specific upper limit for new infections. We investigate, whether the politically motivated bound is low enough to detect new outbreaks at an early stage. Subsequently, we introduce an optimal control problem to tackle the multi–criteria problem of finding a bound for new infections that is low enough to avoid new outbreaks, which might lead to an overload of the health care system, but is large enough to curb the expected economic losses.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"9 1","pages":"242 - 260"},"PeriodicalIF":0.0,"publicationDate":"2020-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46247182","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}
Pub Date : 2020-05-20DOI: 10.1101/2020.05.15.20103010
Jean Dolbeault, Gabriel Turinici
Abstract The goal of the lockdown is to mitigate and if possible prevent the spread of an epidemic. It consists in reducing social interactions. This is taken into account by the introduction of a factor of reduction of social interactions q, and by decreasing the transmission coefficient of the disease accordingly. Evaluating q is a difficult question and one can ask if it makes sense to compute an average coefficient q for a given population, in order to make predictions on the basic reproduction rate ℛ0, the dynamics of the epidemic or the fraction of the population that will have been infected by the end of the epidemic. On a very simple example, we show that the computation of ℛ0 in a heterogeneous population is not reduced to the computation of an average q but rather to the direct computation of an average coefficient ℛ0. Even more interesting is the fact that, in a range of data compatible with the Covid-19 outbreak, the size of the epidemic is deeply modified by social heterogeneity, as is the height of the epidemic peak, while the date at which it is reached mainly depends on the average ℛ0 coefficient. This paper illustrates more technical results that can be found in [4], with new numerical computations. It is intended to draw the attention on the role of heterogeneities in a population in a very simple case, which might be difficult to apprehend in more realistic but also more complex models.
{"title":"Social heterogeneity and the COVID-19 lockdown in a multi-group SEIR model","authors":"Jean Dolbeault, Gabriel Turinici","doi":"10.1101/2020.05.15.20103010","DOIUrl":"https://doi.org/10.1101/2020.05.15.20103010","url":null,"abstract":"Abstract The goal of the lockdown is to mitigate and if possible prevent the spread of an epidemic. It consists in reducing social interactions. This is taken into account by the introduction of a factor of reduction of social interactions q, and by decreasing the transmission coefficient of the disease accordingly. Evaluating q is a difficult question and one can ask if it makes sense to compute an average coefficient q for a given population, in order to make predictions on the basic reproduction rate ℛ0, the dynamics of the epidemic or the fraction of the population that will have been infected by the end of the epidemic. On a very simple example, we show that the computation of ℛ0 in a heterogeneous population is not reduced to the computation of an average q but rather to the direct computation of an average coefficient ℛ0. Even more interesting is the fact that, in a range of data compatible with the Covid-19 outbreak, the size of the epidemic is deeply modified by social heterogeneity, as is the height of the epidemic peak, while the date at which it is reached mainly depends on the average ℛ0 coefficient. This paper illustrates more technical results that can be found in [4], with new numerical computations. It is intended to draw the attention on the role of heterogeneities in a population in a very simple case, which might be difficult to apprehend in more realistic but also more complex models.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"9 1","pages":"14 - 21"},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42950388","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}
Pub Date : 2020-01-01DOI: 10.1101/2020.05.18.20105411
S. Chitanvis
Abstract Background Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. Methods We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.
{"title":"Dynamical model for social distancing in the U.S. during the COVID-19 epidemic","authors":"S. Chitanvis","doi":"10.1101/2020.05.18.20105411","DOIUrl":"https://doi.org/10.1101/2020.05.18.20105411","url":null,"abstract":"Abstract Background Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. Methods We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"141 - 149"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43199948","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}
Abstract We present a methodology to connect an ordinary differential equation (ODE) model of interacting entities at the individual level, to an open Markov chain (OMC) model of a population of such individuals, via a stochastic differential equation (SDE) intermediate model. The ODE model here presented is formulated as a dynamic change between two regimes; one regime is of mean reverting type and the other is of inverse logistic type. For the general purpose of defining an OMC model for a population of individuals, we associate an Ito processes, in the form of SDE to ODE system of equations, by means of the addition of Gaussian noise terms which may be thought to model non essential characteristics of the phenomena with small and undifferentiated influences. The next step consists on discretizing the SDE and using the discretized trajectories computed by simulation to define transitions of a finite valued Markov chain; for that, the state space of the Ito processes is partitioned according to some rule. For the example proposed for illustration, the state space of the ODE system referred – corresponding to a model of a viral infection – is partitioned into six infection classes determined by some of the critical points of the ODE system; we detail the evolution of some infected population in these infection classes.
{"title":"From ODE to Open Markov Chains, via SDE: an application to models for infections in individuals and populations","authors":"M. Esquível, P. Patrício, Gracinda R. Guerreiro","doi":"10.1515/cmb-2020-0110","DOIUrl":"https://doi.org/10.1515/cmb-2020-0110","url":null,"abstract":"Abstract We present a methodology to connect an ordinary differential equation (ODE) model of interacting entities at the individual level, to an open Markov chain (OMC) model of a population of such individuals, via a stochastic differential equation (SDE) intermediate model. The ODE model here presented is formulated as a dynamic change between two regimes; one regime is of mean reverting type and the other is of inverse logistic type. For the general purpose of defining an OMC model for a population of individuals, we associate an Ito processes, in the form of SDE to ODE system of equations, by means of the addition of Gaussian noise terms which may be thought to model non essential characteristics of the phenomena with small and undifferentiated influences. The next step consists on discretizing the SDE and using the discretized trajectories computed by simulation to define transitions of a finite valued Markov chain; for that, the state space of the Ito processes is partitioned according to some rule. For the example proposed for illustration, the state space of the ODE system referred – corresponding to a model of a viral infection – is partitioned into six infection classes determined by some of the critical points of the ODE system; we detail the evolution of some infected population in these infection classes.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"180 - 197"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48973431","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}
Abstract We observe that fullerene graphs are one-skeletons of polyhedra, which can be realized with all dihedral angles equal to π /2 in a hyperbolic 3-dimensional space. One of the most important invariants of such a polyhedron is its volume. We are referring this volume as a hyperbolic volume of a fullerene. It is known that some topological indices of graphs of chemical compounds serve as strong descriptors and correlate with chemical properties. We demonstrate that hyperbolic volume of fullerenes correlates with few important topological indices and so, hyperbolic volume can serve as a chemical descriptor too. The correlation between hyperbolic volume of fullerene and its Wiener index suggested few conjectures on volumes of hyperbolic polyhedra. These conjectures are confirmed for the initial list of fullerenes.
{"title":"On correlation of hyperbolic volumes of fullerenes with their properties","authors":"A. Egorov, A. Vesnin","doi":"10.1515/cmb-2020-0108","DOIUrl":"https://doi.org/10.1515/cmb-2020-0108","url":null,"abstract":"Abstract We observe that fullerene graphs are one-skeletons of polyhedra, which can be realized with all dihedral angles equal to π /2 in a hyperbolic 3-dimensional space. One of the most important invariants of such a polyhedron is its volume. We are referring this volume as a hyperbolic volume of a fullerene. It is known that some topological indices of graphs of chemical compounds serve as strong descriptors and correlate with chemical properties. We demonstrate that hyperbolic volume of fullerenes correlates with few important topological indices and so, hyperbolic volume can serve as a chemical descriptor too. The correlation between hyperbolic volume of fullerene and its Wiener index suggested few conjectures on volumes of hyperbolic polyhedra. These conjectures are confirmed for the initial list of fullerenes.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"150 - 167"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48085044","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}
Abstract Nuclear Magnetic Resonance (NMR) experiments can be used to calculate 3D protein structures and geometric properties of protein molecules allow us to solve the problem iteratively using a combinatorial method, called Branch-and-Prune (BP). The main step of BP algorithm is to intersect three spheres centered at the positions for atoms i − 3, i − 2, i − 1, with radii given by the atomic distances di−3,i, di−2,i, di−1,i, respectively, to obtain the position for atom i. Because of uncertainty in NMR data, some of the distances di−3,i should be represented as interval distances [ d_i-3,i,d¯i-3,i {underline{d}_{i - 3,i}},{bar d_{i - 3,i}} ], where d_i-3,i≤di-3,i≤d¯i-3,i {underline{d}_{i - 3,i}} le {d_{i - 3,i}} le {bar d_{i - 3,i}} . In the literature, an extension of the BP algorithm was proposed to deal with interval distances, where the idea is to sample values from [ d_i-3,i,d¯i-3,i {underline{d}_{i - 3,i}},{bar d_{i - 3,i}} ]. We present a new method, based on conformal geometric algebra, to reduce the size of [ d_i-3,i,d¯i-3,i {underline{d}_{i - 3,i}},{bar d_{i - 3,i}} ], before the sampling process. We also compare it with another approach proposed in the literature.
{"title":"NMR Protein Structure Calculation and Sphere Intersections","authors":"C. Lavor, R. Alves, M. Souza, Luis Aragón José","doi":"10.1515/cmb-2020-0103","DOIUrl":"https://doi.org/10.1515/cmb-2020-0103","url":null,"abstract":"Abstract Nuclear Magnetic Resonance (NMR) experiments can be used to calculate 3D protein structures and geometric properties of protein molecules allow us to solve the problem iteratively using a combinatorial method, called Branch-and-Prune (BP). The main step of BP algorithm is to intersect three spheres centered at the positions for atoms i − 3, i − 2, i − 1, with radii given by the atomic distances di−3,i, di−2,i, di−1,i, respectively, to obtain the position for atom i. Because of uncertainty in NMR data, some of the distances di−3,i should be represented as interval distances [ d_i-3,i,d¯i-3,i {underline{d}_{i - 3,i}},{bar d_{i - 3,i}} ], where d_i-3,i≤di-3,i≤d¯i-3,i {underline{d}_{i - 3,i}} le {d_{i - 3,i}} le {bar d_{i - 3,i}} . In the literature, an extension of the BP algorithm was proposed to deal with interval distances, where the idea is to sample values from [ d_i-3,i,d¯i-3,i {underline{d}_{i - 3,i}},{bar d_{i - 3,i}} ]. We present a new method, based on conformal geometric algebra, to reduce the size of [ d_i-3,i,d¯i-3,i {underline{d}_{i - 3,i}},{bar d_{i - 3,i}} ], before the sampling process. We also compare it with another approach proposed in the literature.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"89 - 101"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45673505","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}
Pub Date : 2020-01-01Epub Date: 2020-02-17DOI: 10.1515/cmb-2020-0001
David Bramer, Guo-Wei Wei
Recently, persistent homology has had tremendous success in biomolecular data analysis. It works by examining the topological relationship or connectivity of a group of atoms in a molecule at a variety of scales, then rendering a family of topological representations of the molecule. However, persistent homology is rarely employed for the analysis of atomic properties, such as biomolecular flexibility analysis or B-factor prediction. This work introduces atom-specific persistent homology to provide a local atomic level representation of a molecule via a global topological tool. This is achieved through the construction of a pair of conjugated sets of atoms and corresponding conjugated simplicial complexes, as well as conjugated topological spaces. The difference between the topological invariants of the pair of conjugated sets is measured by Bottleneck and Wasserstein metrics and leads to an atom-specific topological representation of individual atomic properties in a molecule. Atom-specific topological features are integrated with various machine learning algorithms, including gradient boosting trees and convolutional neural network for protein thermal fluctuation analysis and B-factor prediction. Extensive numerical results indicate the proposed method provides a powerful topological tool for analyzing and predicting localized information in complex macromolecules.
{"title":"Atom-specific persistent homology and its application to protein flexibility analysis.","authors":"David Bramer, Guo-Wei Wei","doi":"10.1515/cmb-2020-0001","DOIUrl":"https://doi.org/10.1515/cmb-2020-0001","url":null,"abstract":"<p><p>Recently, persistent homology has had tremendous success in biomolecular data analysis. It works by examining the topological relationship or connectivity of a group of atoms in a molecule at a variety of scales, then rendering a family of topological representations of the molecule. However, persistent homology is rarely employed for the analysis of atomic properties, such as biomolecular flexibility analysis or B-factor prediction. This work introduces atom-specific persistent homology to provide a local atomic level representation of a molecule via a global topological tool. This is achieved through the construction of a pair of conjugated sets of atoms and corresponding conjugated simplicial complexes, as well as conjugated topological spaces. The difference between the topological invariants of the pair of conjugated sets is measured by Bottleneck and Wasserstein metrics and leads to an atom-specific topological representation of individual atomic properties in a molecule. Atom-specific topological features are integrated with various machine learning algorithms, including gradient boosting trees and convolutional neural network for protein thermal fluctuation analysis and B-factor prediction. Extensive numerical results indicate the proposed method provides a powerful topological tool for analyzing and predicting localized information in complex macromolecules.</p>","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"1-35"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39197128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jead M. Macalisang, Mark Caay, J. P. Arcede, Randy L. Caga-anan
Abstract Building on an SEIR-type model of COVID-19 where the infecteds are further divided into symptomatic and asymptomatic, a system incorporating the various possible interventions is formulated. Interventions, also referred to as controls, include transmission reduction (e.g., lockdown, social distancing, barrier gestures); testing/isolation on the exposed, symptomatic and asymptomatic compartments; and medical controls such as enhancing patients’ medical care and increasing bed capacity. By considering the government’s capacity, the best strategies for implementing the controls were obtained using optimal control theory. Results show that, if all the controls are to be used, the more able the government is, the more it should implement transmission reduction, testing, and enhancing patients’ medical care without increasing hospital beds. However, if the government finds it very difficult to implement the controls for economic reasons, the best approach is to increase the hospital beds. Moreover, among the testing/isolation controls, testing/isolation in the exposed compartment is the least needed when there is significant transmission reduction control. Surprisingly, when there is no transmission reduction control, testing/isolation in the exposed should be optimal. Testing/isolation in the exposed could seemingly replace the transmission reduction control to yield a comparable result to that when the transmission reduction control is being implemented.
{"title":"Optimal Control for a COVID-19 Model Accounting for Symptomatic and Asymptomatic","authors":"Jead M. Macalisang, Mark Caay, J. P. Arcede, Randy L. Caga-anan","doi":"10.1515/cmb-2020-0109","DOIUrl":"https://doi.org/10.1515/cmb-2020-0109","url":null,"abstract":"Abstract Building on an SEIR-type model of COVID-19 where the infecteds are further divided into symptomatic and asymptomatic, a system incorporating the various possible interventions is formulated. Interventions, also referred to as controls, include transmission reduction (e.g., lockdown, social distancing, barrier gestures); testing/isolation on the exposed, symptomatic and asymptomatic compartments; and medical controls such as enhancing patients’ medical care and increasing bed capacity. By considering the government’s capacity, the best strategies for implementing the controls were obtained using optimal control theory. Results show that, if all the controls are to be used, the more able the government is, the more it should implement transmission reduction, testing, and enhancing patients’ medical care without increasing hospital beds. However, if the government finds it very difficult to implement the controls for economic reasons, the best approach is to increase the hospital beds. Moreover, among the testing/isolation controls, testing/isolation in the exposed compartment is the least needed when there is significant transmission reduction control. Surprisingly, when there is no transmission reduction control, testing/isolation in the exposed should be optimal. Testing/isolation in the exposed could seemingly replace the transmission reduction control to yield a comparable result to that when the transmission reduction control is being implemented.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"168 - 179"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48946138","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}
Abstract The COVID-19 pandemic has resulted in more than 65.5 million infections and 15,14,695 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced, thereby decreasing the mortality. Also population level control strategies can reduce the spread of the COVID-19 substantially. Motivated by these observations, in this work we propose and study a multi scale model linking both within-host and between-host dynamics of COVID-19. Initially the natural history dealing with the disease dynamics is studied. Later comparative effectiveness is performed to understand the efficacy of both the within-host and population level interventions. Findings of this study suggest that a combined strategy involving treatment with drugs such as Arbidol, remdesivir, Lopinavir/Ritonavir that inhibits viral replication and immunotherapies like monoclonal antibodies, along with environmental hygiene and generalized social distancing proved to be the best and optimal in reducing the basic reproduction number and environmental spread of the virus at the population level.
{"title":"Control Intervention Strategies for Within-Host, Between-Host and their Efficacy in the Treatment, Spread of COVID-19 : A Multi Scale Modeling Approach","authors":"Bhanu Prakash, D. Vamsi, D. Rajesh, C. Sanjeevi","doi":"10.1515/cmb-2020-0111","DOIUrl":"https://doi.org/10.1515/cmb-2020-0111","url":null,"abstract":"Abstract The COVID-19 pandemic has resulted in more than 65.5 million infections and 15,14,695 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced, thereby decreasing the mortality. Also population level control strategies can reduce the spread of the COVID-19 substantially. Motivated by these observations, in this work we propose and study a multi scale model linking both within-host and between-host dynamics of COVID-19. Initially the natural history dealing with the disease dynamics is studied. Later comparative effectiveness is performed to understand the efficacy of both the within-host and population level interventions. Findings of this study suggest that a combined strategy involving treatment with drugs such as Arbidol, remdesivir, Lopinavir/Ritonavir that inhibits viral replication and immunotherapies like monoclonal antibodies, along with environmental hygiene and generalized social distancing proved to be the best and optimal in reducing the basic reproduction number and environmental spread of the virus at the population level.","PeriodicalId":34018,"journal":{"name":"Computational and Mathematical Biophysics","volume":"8 1","pages":"198 - 210"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cmb-2020-0111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44739209","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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