{"title":"The fundamentals of statistical data analysis","authors":"W. Stewart","doi":"10.1201/9781315119847-3","DOIUrl":"https://doi.org/10.1201/9781315119847-3","url":null,"abstract":"","PeriodicalId":153035,"journal":{"name":"Systems Immunology","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130603589","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}
{"title":"Introduction to basic concepts in immunology","authors":"Roxana Khazen, S. Valitutti","doi":"10.1201/9781315119847-1","DOIUrl":"https://doi.org/10.1201/9781315119847-1","url":null,"abstract":"","PeriodicalId":153035,"journal":{"name":"Systems Immunology","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134064905","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}
ed appropriately, the specific form of a mathematical model is irrelevant insofar as the numerical optimization of the cost function is concerned, as long as it can evaluate the least-squares deviation of a model from data for a given set of parameters θ . Optimizing an arbitrary nonlinear function of a set of variables is a widespread problem throughout all of science, and accordingly, much algorithmic and development work has been devoted to producing numerical tools capable of carrying out this essential computational task. Numerical optimization is something of an art: there is a vast set of different algorithms that one might possibly make use of, and determining which is most appropriate for a given problem can require a bit of experimentation. Perhaps the most relevant distinguishing feature among different algorithms are those that are capable of identifying global optima and those that make do with finding local optima. In some cases, there can be
{"title":"Zen and the art of parameter estimation in systems biology","authors":"C. Myers","doi":"10.1201/9781315119847-8","DOIUrl":"https://doi.org/10.1201/9781315119847-8","url":null,"abstract":"ed appropriately, the specific form of a mathematical model is irrelevant insofar as the numerical optimization of the cost function is concerned, as long as it can evaluate the least-squares deviation of a model from data for a given set of parameters θ . Optimizing an arbitrary nonlinear function of a set of variables is a widespread problem throughout all of science, and accordingly, much algorithmic and development work has been devoted to producing numerical tools capable of carrying out this essential computational task. Numerical optimization is something of an art: there is a vast set of different algorithms that one might possibly make use of, and determining which is most appropriate for a given problem can require a bit of experimentation. Perhaps the most relevant distinguishing feature among different algorithms are those that are capable of identifying global optima and those that make do with finding local optima. In some cases, there can be","PeriodicalId":153035,"journal":{"name":"Systems Immunology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122526061","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 : 2018-09-03DOI: 10.1201/9781315119847-16
A. Smith, R. Ribeiro, A. Perelson
{"title":"Population dynamics of host and pathogens","authors":"A. Smith, R. Ribeiro, A. Perelson","doi":"10.1201/9781315119847-16","DOIUrl":"https://doi.org/10.1201/9781315119847-16","url":null,"abstract":"","PeriodicalId":153035,"journal":{"name":"Systems Immunology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125029857","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 : 2018-09-03DOI: 10.1201/9781315119847-10
Derya Altıntan, J. Diemer, H. Koeppl
{"title":"Analysis and modeling of single cell data","authors":"Derya Altıntan, J. Diemer, H. Koeppl","doi":"10.1201/9781315119847-10","DOIUrl":"https://doi.org/10.1201/9781315119847-10","url":null,"abstract":"","PeriodicalId":153035,"journal":{"name":"Systems Immunology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129648925","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}
Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at this http URL
{"title":"An introduction to rule-based modeling of immune receptor signaling","authors":"John A. P. Sekar, J. Faeder","doi":"10.1201/9781315119847-5","DOIUrl":"https://doi.org/10.1201/9781315119847-5","url":null,"abstract":"Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at this http URL","PeriodicalId":153035,"journal":{"name":"Systems Immunology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128440876","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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