Pub Date : 2016-03-01DOI: 10.1109/LLS.2016.2568259
Xiaodong Yang;Aifeng Ren;Tianqiao Zhu;Fangming Hu
Digital phantoms are vital for various biomedical researches. Traditional phantoms include theoretical models and voxel models reconstructed from medical images. It has been demonstrated that the homogeneous phantom filled with uniform tissue is accurate enough for wearable antenna design, body-centric channel modeling, etc. Therefore, it is interesting and necessary to investigate the novel approach of generating digital phantoms using an optical noncontact measurement system. In this letter, the point cloud data are first obtained; then, they are simplified via principal component analysis; finally, by applying surface reconstruction and mesh simplification techniques, a digital Chinese phantom is established. To verify the usability of the phantom, numerical calculation is performed to check E-fields at different positions on the body. Results sufficiently prove the feasibility of the train of thought presented in this letter.
{"title":"A Novel Digital Phantom Using an Optical Noncontact Measurement System","authors":"Xiaodong Yang;Aifeng Ren;Tianqiao Zhu;Fangming Hu","doi":"10.1109/LLS.2016.2568259","DOIUrl":"https://doi.org/10.1109/LLS.2016.2568259","url":null,"abstract":"Digital phantoms are vital for various biomedical researches. Traditional phantoms include theoretical models and voxel models reconstructed from medical images. It has been demonstrated that the homogeneous phantom filled with uniform tissue is accurate enough for wearable antenna design, body-centric channel modeling, etc. Therefore, it is interesting and necessary to investigate the novel approach of generating digital phantoms using an optical noncontact measurement system. In this letter, the point cloud data are first obtained; then, they are simplified via principal component analysis; finally, by applying surface reconstruction and mesh simplification techniques, a digital Chinese phantom is established. To verify the usability of the phantom, numerical calculation is performed to check E-fields at different positions on the body. Results sufficiently prove the feasibility of the train of thought presented in this letter.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2568259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49950757","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 : 2016-01-01DOI: 10.1109/LLS.2016.2644645
Marc Martin-Casas;Ali Mesbah
Computational models are useful for quantitative elucidation of the dynamical behavior of biological systems. Oftentimes, several competing models (i.e., hypotheses) are proposed to describe the underlying molecular mechanisms of a biological system. Selecting the most representative model is imperative for obtaining meaningful quantitative insights into the dynamics of the system of interest. However, the discrimination between competing models poses a significant challenge due to heterogeneity that is intrinsic to biological systems. This letter demonstrates the effectiveness of a probabilistic approach to optimal experiment design for model discrimination in the presence of time-invariant cell-to-cell differences within a cell population. The JAK2/STAT5 signaling pathway, which is involved in proliferation and differentiation of hematopoietic stem cells, is used as a case study.
{"title":"Discrimination Between Competing Model Structures of Biological Systems in the Presence of Population Heterogeneity","authors":"Marc Martin-Casas;Ali Mesbah","doi":"10.1109/LLS.2016.2644645","DOIUrl":"https://doi.org/10.1109/LLS.2016.2644645","url":null,"abstract":"Computational models are useful for quantitative elucidation of the dynamical behavior of biological systems. Oftentimes, several competing models (i.e., hypotheses) are proposed to describe the underlying molecular mechanisms of a biological system. Selecting the most representative model is imperative for obtaining meaningful quantitative insights into the dynamics of the system of interest. However, the discrimination between competing models poses a significant challenge due to heterogeneity that is intrinsic to biological systems. This letter demonstrates the effectiveness of a probabilistic approach to optimal experiment design for model discrimination in the presence of time-invariant cell-to-cell differences within a cell population. The JAK2/STAT5 signaling pathway, which is involved in proliferation and differentiation of hematopoietic stem cells, is used as a case study.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"2 3","pages":"23-26"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2644645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49909177","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}
The Synthetic Biology Open Language (SBOL) is an emerging data standard for representing synthetic biology designs. The goal of SBOL is to improve the reproducibility of these designs and their electronic exchange between researchers and/or genetic design automation tools. The latest version of the standard, SBOL 2.0, enables the annotation of a large variety of biological components (e.g., DNA, RNA, proteins, complexes, small molecules, etc.) and their interactions. SBOL 2.0 also allows researchers to organize components into hierarchical modules, to specify their intended functions, and to link modules to models that describe their behavior mathematically. To support the use of SBOL 2.0, we have developed the libSBOLj 2.0 Java library, which provides an easy to use Application Programming Interface (API) for developers, including manipulation of SBOL constructs, serialization to and from an RDF/XML file format, and migration support in the form of conversion from the prior SBOL 1.1 standard to SBOL 2.0. This letter describes the libSBOLj 2.0 library and key engineering decisions involved in its design.
{"title":"libSBOLj 2.0: A Java Library to Support SBOL 2.0","authors":"Zhen Zhang;Tramy Nguyen;Nicholas Roehner;Göksel Misirli;Matthew Pocock;Ernst Oberortner;Meher Samineni;Zach Zundel;Jacob Beal;Kevin Clancy;Anil Wipat;Chris J. Myers","doi":"10.1109/LLS.2016.2546546","DOIUrl":"https://doi.org/10.1109/LLS.2016.2546546","url":null,"abstract":"The Synthetic Biology Open Language (SBOL) is an emerging data standard for representing synthetic biology designs. The goal of SBOL is to improve the reproducibility of these designs and their electronic exchange between researchers and/or genetic design automation tools. The latest version of the standard, SBOL 2.0, enables the annotation of a large variety of biological components (e.g., DNA, RNA, proteins, complexes, small molecules, etc.) and their interactions. SBOL 2.0 also allows researchers to organize components into hierarchical modules, to specify their intended functions, and to link modules to models that describe their behavior mathematically. To support the use of SBOL 2.0, we have developed the \u0000<monospace>libSBOLj 2.0</monospace>\u0000 Java library, which provides an easy to use Application Programming Interface (API) for developers, including manipulation of SBOL constructs, serialization to and from an RDF/XML file format, and migration support in the form of conversion from the prior SBOL 1.1 standard to SBOL 2.0. This letter describes the \u0000<monospace>libSBOLj 2.0</monospace>\u0000 library and key engineering decisions involved in its design.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 4","pages":"34-37"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2546546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49978815","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 : 2015-12-01DOI: 10.1109/LLS.2016.2546546
Zhen Zhang, Tramy Nguyen, Nicholas Roehner, Goksel Misirli, M. Pocock, Ernst Oberortner, Meher Samineni, Z. Zundel, J. Beal, Kevin Clancy, A. Wipat, C. Myers
The Synthetic Biology Open Language (SBOL) is an emerging data standard for representing synthetic biology designs. The goal of SBOL is to improve the reproducibility of these designs and their electronic exchange between researchers and/or genetic design automation tools. The latest version of the standard, SBOL 2.0, enables the annotation of a large variety of biological components (e.g., DNA, RNA, proteins, complexes, small molecules, etc.) and their interactions. SBOL 2.0 also allows researchers to organize components into hierarchical modules, to specify their intended functions, and to link modules to models that describe their behavior mathematically. To support the use of SBOL 2.0, we have developed the libSBOLj 2.0 Java library, which provides an easy to use Application Programming Interface (API) for developers, including manipulation of SBOL constructs, serialization to and from an RDF/XML file format, and migration support in the form of conversion from the prior SBOL 1.1 standard to SBOL 2.0. This letter describes the libSBOLj 2.0 library and key engineering decisions involved in its design.
{"title":"libSBOLj 2.0: A Java Library to Support SBOL 2.0","authors":"Zhen Zhang, Tramy Nguyen, Nicholas Roehner, Goksel Misirli, M. Pocock, Ernst Oberortner, Meher Samineni, Z. Zundel, J. Beal, Kevin Clancy, A. Wipat, C. Myers","doi":"10.1109/LLS.2016.2546546","DOIUrl":"https://doi.org/10.1109/LLS.2016.2546546","url":null,"abstract":"The Synthetic Biology Open Language (SBOL) is an emerging data standard for representing synthetic biology designs. The goal of SBOL is to improve the reproducibility of these designs and their electronic exchange between researchers and/or genetic design automation tools. The latest version of the standard, SBOL 2.0, enables the annotation of a large variety of biological components (e.g., DNA, RNA, proteins, complexes, small molecules, etc.) and their interactions. SBOL 2.0 also allows researchers to organize components into hierarchical modules, to specify their intended functions, and to link modules to models that describe their behavior mathematically. To support the use of SBOL 2.0, we have developed the libSBOLj 2.0 Java library, which provides an easy to use Application Programming Interface (API) for developers, including manipulation of SBOL constructs, serialization to and from an RDF/XML file format, and migration support in the form of conversion from the prior SBOL 1.1 standard to SBOL 2.0. This letter describes the libSBOLj 2.0 library and key engineering decisions involved in its design.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 1","pages":"34-37"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2546546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509735","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 : 2015-12-01DOI: 10.1109/LLS.2016.2557938
This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.
{"title":"2015 Index IEEE Life Sciences Letters Vol. 1","authors":"","doi":"10.1109/LLS.2016.2557938","DOIUrl":"https://doi.org/10.1109/LLS.2016.2557938","url":null,"abstract":"This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 4","pages":"38-40"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2016.2557938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49978816","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}
Aberrant DNA methylation has long been implicated in cancers. In this letter, we present a highly discriminative DNA methylation biomarker for non-small cell lung cancers (NSCLCs) and 14 other cancers. Based on 69 NSCLC cell lines and 257 cancer-free lung tissues, we identified a CpG island in SCT gene promoter, which was verified by qMSP experiment in 15 NSCLC cell lines and three immortalized human respiratory epithelium cells. In addition, we found that the SCT promoter was methylated in 23 cancer cell lines involving >10 cancer types profiled by ENCODE. We found that the SCT promoter is hypermethylated in primary tumors from TCGA lung cancer cohort. In addition, we found that SCT promoter is methylated at high frequencies in 15 malignancies and is not methylated in ~1000 non-cancerous tissues across >30 organ types. This letter indicates that SCT promoter methylation is a highly discriminative biomarker for lung and many other cancers.
{"title":"SCT Promoter Methylation Is a Highly Discriminative Biomarker for Lung and Many Other Cancers","authors":"Adwait Sathe;Yu-An Zhang;Xiaotu Ma;Pradipta Ray;Daniela Cadinu;Yi-Wei Wang;Xiao Yao;Xiaoyun Liu;Hao Tang;Yunfei Wang;Ying Huang;Changning Liu;Jin Gu;Martin Akerman;Yifan Mo;Chao Cheng;Zhenyu Xuan;Lei Chen;Guanghua Xiao;Yang Xie;Luc Girard;Hongyang Wang;Stephen Lam;Ignacio I. Wistuba;Li Zhang;Adi F. Gazdar;Michael Q. Zhang","doi":"10.1109/LLS.2015.2488438","DOIUrl":"10.1109/LLS.2015.2488438","url":null,"abstract":"Aberrant DNA methylation has long been implicated in cancers. In this letter, we present a highly discriminative DNA methylation biomarker for non-small cell lung cancers (NSCLCs) and 14 other cancers. Based on 69 NSCLC cell lines and 257 cancer-free lung tissues, we identified a CpG island in SCT gene promoter, which was verified by qMSP experiment in 15 NSCLC cell lines and three immortalized human respiratory epithelium cells. In addition, we found that the SCT promoter was methylated in 23 cancer cell lines involving >10 cancer types profiled by ENCODE. We found that the SCT promoter is hypermethylated in primary tumors from TCGA lung cancer cohort. In addition, we found that SCT promoter is methylated at high frequencies in 15 malignancies and is not methylated in ~1000 non-cancerous tissues across >30 organ types. This letter indicates that SCT promoter methylation is a highly discriminative biomarker for lung and many other cancers.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 3","pages":"30-33"},"PeriodicalIF":0.0,"publicationDate":"2015-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2015.2488438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25510567","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}
Pub Date : 2015-09-29DOI: 10.1109/LLS.2015.2483820
Eduardo Sontag, Abhyudai Singh
We introduce a class of chemical reaction networks for which all moments can be computed by finite-dimensional linear differential equations. This class allows second and higher order reactions, subject to certain assumptions on structure and/or conservation laws.
{"title":"Exact Moment Dynamics for Feedforward Nonlinear Chemical Reaction Networks","authors":"Eduardo Sontag, Abhyudai Singh","doi":"10.1109/LLS.2015.2483820","DOIUrl":"https://doi.org/10.1109/LLS.2015.2483820","url":null,"abstract":"We introduce a class of chemical reaction networks for which all moments can be computed by finite-dimensional linear differential equations. This class allows second and higher order reactions, subject to certain assumptions on structure and/or conservation laws.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 1","pages":"26-29"},"PeriodicalIF":0.0,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2015.2483820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509661","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 : 2015-08-28DOI: 10.1109/LLS.2015.2465870
Andrew T. Magis;Cory C. Funk;Nathan D. Price
The process of converting raw RNA sequencing (RNA-seq) data to interpretable results can be circuitous and time-consuming, requiring multiple steps. We present an RNA-seq mapping algorithm that streamlines this process. Our algorithm utilizes a hash table approach to leverage the availability and the power of high memory machines. SNAPR, which can be run on a single library or thousands of libraries, can take compressed or uncompressed FASTQ and BAM files, and output a sorted BAM file, individual read counts, and gene fusions, and can identify exogenous RNA species in a single step. SNAPR also does native Phred score filtering of reads. SNAPR is also well suited for future sequencing platforms that generate longer reads. We show how we can analyze data from hundreds of TCGA samples in a matter of hours while identifying gene fusions and viral events at the same time. With the reference genome and transcriptome undergoing periodic updates and the need for uniform parameters when integrating multiple data sets, there is great need for a streamlined process for RNA-seq analysis. We demonstrate how SNAPR does this efficiently and accurately.
{"title":"SNAPR: A Bioinformatics Pipeline for Efficient and Accurate RNA-Seq Alignment and Analysis","authors":"Andrew T. Magis;Cory C. Funk;Nathan D. Price","doi":"10.1109/LLS.2015.2465870","DOIUrl":"10.1109/LLS.2015.2465870","url":null,"abstract":"The process of converting raw RNA sequencing (RNA-seq) data to interpretable results can be circuitous and time-consuming, requiring multiple steps. We present an RNA-seq mapping algorithm that streamlines this process. Our algorithm utilizes a hash table approach to leverage the availability and the power of high memory machines. SNAPR, which can be run on a single library or thousands of libraries, can take compressed or uncompressed FASTQ and BAM files, and output a sorted BAM file, individual read counts, and gene fusions, and can identify exogenous RNA species in a single step. SNAPR also does native Phred score filtering of reads. SNAPR is also well suited for future sequencing platforms that generate longer reads. We show how we can analyze data from hundreds of TCGA samples in a matter of hours while identifying gene fusions and viral events at the same time. With the reference genome and transcriptome undergoing periodic updates and the need for uniform parameters when integrating multiple data sets, there is great need for a streamlined process for RNA-seq analysis. We demonstrate how SNAPR does this efficiently and accurately.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 2","pages":"22-25"},"PeriodicalIF":0.0,"publicationDate":"2015-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2015.2465870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35682017","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}
Pub Date : 2015-08-19DOI: 10.1109/LLS.2015.2465839
Robert Gooding-Townsend, S. Ten Holder, B. Ingalls
Bacterial plasmids employ copy number control systems to ensure that they do not overburden their hosts. Plasmid incompatibility is caused by shared components of copy number control systems, resulting in mutual inhibition of replication. Incompatible plasmids cannot be stably maintained within a host cell. Unilateral incompatibility, in which the plasmid replicons are compatible but one plasmid encodes for the replication inhibitor of the other, leads to rapid displacement of the inhibited plasmid. Thus, we propose that the unilateral incompatibility can be used to eradicate an undesirable plasmid from a population. To investigate this process, we developed deterministic and stochastic models of plasmid dynamics. An analysis of these models provides predictions about the efficacy of plasmid displacement.
{"title":"Displacement of Bacterial Plasmids by Engineered Unilateral Incompatibility","authors":"Robert Gooding-Townsend, S. Ten Holder, B. Ingalls","doi":"10.1109/LLS.2015.2465839","DOIUrl":"https://doi.org/10.1109/LLS.2015.2465839","url":null,"abstract":"Bacterial plasmids employ copy number control systems to ensure that they do not overburden their hosts. Plasmid incompatibility is caused by shared components of copy number control systems, resulting in mutual inhibition of replication. Incompatible plasmids cannot be stably maintained within a host cell. Unilateral incompatibility, in which the plasmid replicons are compatible but one plasmid encodes for the replication inhibitor of the other, leads to rapid displacement of the inhibited plasmid. Thus, we propose that the unilateral incompatibility can be used to eradicate an undesirable plasmid from a population. To investigate this process, we developed deterministic and stochastic models of plasmid dynamics. An analysis of these models provides predictions about the efficacy of plasmid displacement.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 1","pages":"19-21"},"PeriodicalIF":0.0,"publicationDate":"2015-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2015.2465839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62509647","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 : 2015-08-01DOI: 10.1109/LLS.2015.2483820
Eduardo D. Sontag;Abhyudai Singh
We introduce a class of chemical reaction networks for which all moments can be computed by finite-dimensional linear differential equations. This class allows second and higher order reactions, subject to certain assumptions on structure and/or conservation laws.
{"title":"Exact Moment Dynamics for Feedforward Nonlinear Chemical Reaction Networks","authors":"Eduardo D. Sontag;Abhyudai Singh","doi":"10.1109/LLS.2015.2483820","DOIUrl":"https://doi.org/10.1109/LLS.2015.2483820","url":null,"abstract":"We introduce a class of chemical reaction networks for which all moments can be computed by finite-dimensional linear differential equations. This class allows second and higher order reactions, subject to certain assumptions on structure and/or conservation laws.","PeriodicalId":87271,"journal":{"name":"IEEE life sciences letters","volume":"1 2","pages":"26-29"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LLS.2015.2483820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49913811","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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