Pub Date : 2017-09-14DOI: 10.11145/j.biomath.2017.12.169
J. Samson, Nicholas A. Battista, S. Khatri, L. Miller
Effective methods of fluid transport vary across scale. A commonly used dimensionless number for quantifying the effective scale of fluid transport is the Reynolds number, Re, which gives the ratio of inertial to viscous forces. What may work well for one Re regime may not produce significant flows for another. These differences in scale have implications for many organisms, ranging from the mechanics of how organisms move through their fluid environment to how hearts pump at various stages in development. Some organisms, such as soft pulsing corals, actively contract their tentacles to generate mixing currents that enhance photosynthesis. Their unique morphology and intermediate scale where both viscous and inertial forces are significant make them a unique model organism for understanding fluid mixing. In this paper, 3D fluid-structure interaction simulations of a pulsing soft coral are used to quantify fluid transport and fluid mixing across a wide range of Re. The results show that net transport is negligible for $Re<10$, and continuous upward flow is produced for $Regeq 10$.
{"title":"Pulsing corals: A story of scale and mixing","authors":"J. Samson, Nicholas A. Battista, S. Khatri, L. Miller","doi":"10.11145/j.biomath.2017.12.169","DOIUrl":"https://doi.org/10.11145/j.biomath.2017.12.169","url":null,"abstract":"Effective methods of fluid transport vary across scale. A commonly used dimensionless number for quantifying the effective scale of fluid transport is the Reynolds number, Re, which gives the ratio of inertial to viscous forces. What may work well for one Re regime may not produce significant flows for another. These differences in scale have implications for many organisms, ranging from the mechanics of how organisms move through their fluid environment to how hearts pump at various stages in development. Some organisms, such as soft pulsing corals, actively contract their tentacles to generate mixing currents that enhance photosynthesis. Their unique morphology and intermediate scale where both viscous and inertial forces are significant make them a unique model organism for understanding fluid mixing. In this paper, 3D fluid-structure interaction simulations of a pulsing soft coral are used to quantify fluid transport and fluid mixing across a wide range of Re. The results show that net transport is negligible for $Re<10$, and continuous upward flow is produced for $Regeq 10$.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"390 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115981916","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 : 2017-08-28DOI: 10.4172/2329-9533.1000137
S. Khoshnaw
Mathematical modelling and numerical simulations of interaction populations are crucial topics in systems biology. The interactions of ecological models may occur among individuals of the same species or individuals of different species. Describing the dynamics of such models occasionally requires some techniques of model analysis. Choosing appropriate techniques of model analysis is often a difficult task. We define a prey (mouse) and predator (cat) model. The system is modeled by a pair of non-linear ordinary differential equations using mass action law, under constant rates. A proper scaling is suggested to minimize the number of parameters. More interestingly, we propose a homotopy technique with n expanding parameters for finding some analytical approximate solutions. Numerical simulations are provided using Matlab for different parameters and initial conditions.
{"title":"Dynamic Analysis of a Predator and Prey Model with some Computational Simulations","authors":"S. Khoshnaw","doi":"10.4172/2329-9533.1000137","DOIUrl":"https://doi.org/10.4172/2329-9533.1000137","url":null,"abstract":"Mathematical modelling and numerical simulations of interaction populations are crucial topics in systems biology. The interactions of ecological models may occur among individuals of the same species or individuals of different species. Describing the dynamics of such models occasionally requires some techniques of model analysis. Choosing appropriate techniques of model analysis is often a difficult task. We define a prey (mouse) and predator (cat) model. The system is modeled by a pair of non-linear ordinary differential equations using mass action law, under constant rates. A proper scaling is suggested to minimize the number of parameters. More interestingly, we propose a homotopy technique with n expanding parameters for finding some analytical approximate solutions. Numerical simulations are provided using Matlab for different parameters and initial conditions.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124278","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}
M. D. Gioacchino, G. Campi, N. Poccia, A. Bianconi
While the ultrastructure of myelin is considered a quasi-crystalline stable system, nowadays its multiscale complex dynamics appear to play a key role in its functionality, degeneration and repair processes following neurological diseases and trauma. In this work, we investigated the fluctuation of the myelin supramolecular assembly by measuring the spatial distribution of orientation fluctuations of axons in a Xenopus Laevis sciatic nerve associated with nerve functionality. To this end, we used scanning micro X-ray diffraction (SμXRD), a non-invasive technique that has already been applied to other heterogeneous systems presenting complex geometries from microscale to nanoscale. We found that the orientation of the spatial fluctuations of fresh axons show a Levy flight distribution, which is a clear indication of correlated disorder. We found that the Levy flight distribution was missing in the aged nerve prepared in an unfresh state. This result shows that the spatial distribution of axon orientation fluctuations in unfresh nerve state loses the correlated disorder and assumes a random disorder behavior. This work provides a deeper understanding of the ultrastructure-function nerve relation and paves the way for the study of other materials and biomaterials using the SμXRD technique to detect fluctuations in their supramolecular structure.
{"title":"Correlated Disorder in Myelinated Axons Orientational Geometry and Structure","authors":"M. D. Gioacchino, G. Campi, N. Poccia, A. Bianconi","doi":"10.3390/CONDMAT2030029","DOIUrl":"https://doi.org/10.3390/CONDMAT2030029","url":null,"abstract":"While the ultrastructure of myelin is considered a quasi-crystalline stable system, nowadays its multiscale complex dynamics appear to play a key role in its functionality, degeneration and repair processes following neurological diseases and trauma. In this work, we investigated the fluctuation of the myelin supramolecular assembly by measuring the spatial distribution of orientation fluctuations of axons in a Xenopus Laevis sciatic nerve associated with nerve functionality. To this end, we used scanning micro X-ray diffraction (SμXRD), a non-invasive technique that has already been applied to other heterogeneous systems presenting complex geometries from microscale to nanoscale. We found that the orientation of the spatial fluctuations of fresh axons show a Levy flight distribution, which is a clear indication of correlated disorder. We found that the Levy flight distribution was missing in the aged nerve prepared in an unfresh state. This result shows that the spatial distribution of axon orientation fluctuations in unfresh nerve state loses the correlated disorder and assumes a random disorder behavior. This work provides a deeper understanding of the ultrastructure-function nerve relation and paves the way for the study of other materials and biomaterials using the SμXRD technique to detect fluctuations in their supramolecular structure.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123233283","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 : 2017-07-25DOI: 10.1007/978-3-319-99719-3_28
J. Vass, S. Krylov
{"title":"A Computational Resolution of the Inverse Problem of Kinetic Capillary Electrophoresis (KCE)","authors":"J. Vass, S. Krylov","doi":"10.1007/978-3-319-99719-3_28","DOIUrl":"https://doi.org/10.1007/978-3-319-99719-3_28","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131181902","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 : 2017-07-19DOI: 10.1007/978-981-13-0347-0_13
A. Alam, Naaila Tamkeen, N. Imam, Anam Farooqui, Mohd Murshad Ahmed, Shahnawaz Ali, M. Malik, R. Ishrat
{"title":"Pharmacokinetic and Molecular Docking Studies of Plant-Derived Natural Compounds to Exploring Potential Anti-Alzheimer Activity","authors":"A. Alam, Naaila Tamkeen, N. Imam, Anam Farooqui, Mohd Murshad Ahmed, Shahnawaz Ali, M. Malik, R. Ishrat","doi":"10.1007/978-981-13-0347-0_13","DOIUrl":"https://doi.org/10.1007/978-981-13-0347-0_13","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106811","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 : 2017-06-11DOI: 10.1007/978-981-10-5122-7_211
Filippo Rossi, Gianvittorio Luria, Sara Sommariva, A. Sorrentino
{"title":"Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings","authors":"Filippo Rossi, Gianvittorio Luria, Sara Sommariva, A. Sorrentino","doi":"10.1007/978-981-10-5122-7_211","DOIUrl":"https://doi.org/10.1007/978-981-10-5122-7_211","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132641184","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}
Identifying disease genes from human genome is an important and fundamental problem in biomedical research. Despite many publications of machine learning methods applied to discover new disease genes, it still remains a challenge because of the pleiotropy of genes, the limited number of confirmed disease genes among whole genome and the genetic heterogeneity of diseases. Recent approaches have applied the concept of 'guilty by association' to investigate the association between a disease phenotype and its causative genes, which means that candidate genes with similar characteristics as known disease genes are more likely to be associated with diseases. However, due to the imbalance issues (few genes are experimentally confirmed as disease related genes within human genome) in disease gene identification, semi-supervised approaches, like label propagation approaches and positive-unlabeled learning, are used to identify candidate disease genes via making use of unknown genes for training - typically in the scenario of a small amount of confirmed disease genes (labeled data) with a large amount of unknown genome (unlabeled data). The performance of Disease gene prediction models are limited by potential bias of single learning models and incompleteness and noise of single biological data sources, therefore ensemble learning models are applied via combining multiple diverse biological sources and learning models to obtain better predictive performance. In this thesis, we propose three computational models for identifying candidate disease genes.
{"title":"Computational approaches for disease gene identification","authors":"Peng Yang","doi":"10.32657/10356/59238","DOIUrl":"https://doi.org/10.32657/10356/59238","url":null,"abstract":"Identifying disease genes from human genome is an important and fundamental problem in biomedical research. Despite many publications of machine learning methods applied to discover new disease genes, it still remains a challenge because of the pleiotropy of genes, the limited number of confirmed disease genes among whole genome and the genetic heterogeneity of diseases. Recent approaches have applied the concept of 'guilty by association' to investigate the association between a disease phenotype and its causative genes, which means that candidate genes with similar characteristics as known disease genes are more likely to be associated with diseases. However, due to the imbalance issues (few genes are experimentally confirmed as disease related genes within human genome) in disease gene identification, semi-supervised approaches, like label propagation approaches and positive-unlabeled learning, are used to identify candidate disease genes via making use of unknown genes for training - typically in the scenario of a small amount of confirmed disease genes (labeled data) with a large amount of unknown genome (unlabeled data). The performance of Disease gene prediction models are limited by potential bias of single learning models and incompleteness and noise of single biological data sources, therefore ensemble learning models are applied via combining multiple diverse biological sources and learning models to obtain better predictive performance. In this thesis, we propose three computational models for identifying candidate disease genes.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116309645","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 : 2017-03-24DOI: 10.1016/B978-0-12-802971-8.00011-0
H. Jayamohan, Valentin Romanov, Huizhong Li, Jiyoung Son, R. Samuel, J. Nelson, B. Gale
{"title":"Advances in Microfluidics and Lab-on-a-Chip Technologies","authors":"H. Jayamohan, Valentin Romanov, Huizhong Li, Jiyoung Son, R. Samuel, J. Nelson, B. Gale","doi":"10.1016/B978-0-12-802971-8.00011-0","DOIUrl":"https://doi.org/10.1016/B978-0-12-802971-8.00011-0","url":null,"abstract":"","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126804629","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 : 2017-01-22DOI: 10.11648/j.bsi.20170201.11
R. Isea
In scientific literature, there are many programs that predict linear B-cell epitopes from a protein sequence. Each program generates multiple B-cell epitopes that can be individually studied. This paper defines a function called that combines results from five different prediction programs concerning the linear B-cell epitopes (ie., BebiPred, EPMLR, BCPred, ABCPred and Emini Prediction) for selecting the best B-cell epitopes. We obtained 17 potential linear B cells consensus epitopes from Glycoprotein E from serotype IV of the dengue virus for exploring new possibilities in vaccine development. The direct implication of the results obtained is to open the way to experimentally validate more epitopes to increase the efficiency of the available treatments against dengue and to explore the methodology in other diseases.
{"title":"Quantitative Prediction of Linear B-Cell Epitopes","authors":"R. Isea","doi":"10.11648/j.bsi.20170201.11","DOIUrl":"https://doi.org/10.11648/j.bsi.20170201.11","url":null,"abstract":"In scientific literature, there are many programs that predict linear B-cell epitopes from a protein sequence. Each program generates multiple B-cell epitopes that can be individually studied. This paper defines a function called that combines results from five different prediction programs concerning the linear B-cell epitopes (ie., BebiPred, EPMLR, BCPred, ABCPred and Emini Prediction) for selecting the best B-cell epitopes. We obtained 17 potential linear B cells consensus epitopes from Glycoprotein E from serotype IV of the dengue virus for exploring new possibilities in vaccine development. The direct implication of the results obtained is to open the way to experimentally validate more epitopes to increase the efficiency of the available treatments against dengue and to explore the methodology in other diseases.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129333990","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 : 2016-11-16DOI: 10.1103/PhysRevX.8.021007
Srividya Iyer-Biswas, Herman Gudjonson, Charles S. Wright, Jedidiah Riebling, Emma R. Dawson, Klevin Lo, Aretha Fiebig, S. Crosson, A. Dinner
How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to replication-competent (stalked) stage of the {em Caulobacter crescentus} lifecycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For {em C. crescentus} cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time, and thus yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell age distribution, and the quiescence timescale.
单个细胞的随机生长和分裂的颗粒细节如何反映在种群数量的平滑确定性增长中?我们通过制定一个数据验证的理论框架来解释单细胞和种群尺度上的观察结果，提供了一个集成的、多尺度的微生物生长动力学视角。对于对称和非对称细胞分裂，我们推导出细胞年龄分布和人口增长率作为潜在分裂时间分布的函数的精确解析完整的时间依赖解。这些结果为随机单细胞动力学对种群增长的惊人影响提供了见解。利用我们对不对称分裂的结果，我们推断了{em Caulobacter crescent}生命周期从繁殖静止(群集)到繁殖能力(跟踪)阶段的过渡时间。值得注意的是，种群数量可以随时间自发地振荡。我们阐明了导致这些种群振荡的物理原理。对于{em C. crescentus}细胞，我们表明，在给定的生长条件下，对种群增长率的简单测量足以表征特定条件的细胞时间单位，从而产生平均(单细胞)生长和分裂时间尺度，细胞分裂时间的波动，细胞年龄分布和静止时间尺度。
{"title":"Bridging the time scales of single-cell and population dynamics","authors":"Srividya Iyer-Biswas, Herman Gudjonson, Charles S. Wright, Jedidiah Riebling, Emma R. Dawson, Klevin Lo, Aretha Fiebig, S. Crosson, A. Dinner","doi":"10.1103/PhysRevX.8.021007","DOIUrl":"https://doi.org/10.1103/PhysRevX.8.021007","url":null,"abstract":"How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to replication-competent (stalked) stage of the {em Caulobacter crescentus} lifecycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For {em C. crescentus} cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time, and thus yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell age distribution, and the quiescence timescale.","PeriodicalId":119149,"journal":{"name":"arXiv: Quantitative Methods","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798125","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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