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Titlebook: Computational Methods in Systems Biology; 21st International C Jun Pang,Joachim Niehren Conference proceedings 2023 The Editor(s) (if appli

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书目名称Computational Methods in Systems Biology
副标题21st International C
编辑Jun Pang,Joachim Niehren
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Methods in Systems Biology; 21st International C Jun Pang,Joachim Niehren Conference proceedings 2023 The Editor(s) (if appli
描述.This book constitutes the refereed proceedings of the 21st International Conference on Computational Methods in Systems Biology, CMSB 2023, held in Luxembourg City, Luxembourg, during September 13–15, 2023...The 14 full papers and 3 tool papers presented in this book were carefully reviewed and selected from 28 submissions. CMSB focuses on modeling, simulation, analysis, design and control of biological systems and covers the broad field of computational methods and tools in systems and synthetic biology and their applications.  .
出版日期Conference proceedings 2023
关键词systems biology; formal methods; reaction networks; Boolean networks; mathematical models; hybrid models;
版次1
doihttps://doi.org/10.1007/978-3-031-42697-1
isbn_softcover978-3-031-42696-4
isbn_ebook978-3-031-42697-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,Cell-Level Pathway Scoring Comparison with a Biologically Constrained Variational Autoencoder, learned by the model as proxies of the signaling activity at the single-cell level. We check the performance of the scoring model using a known scRNA-seq public dataset with a clearly established ground truth. Although both models capture the relevant signals, the most pronounced differences are be
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,Harissa: Stochastic Simulation and Inference of Gene Regulatory Networks Based on Transcriptional Bin this context. Therefore, Harissa can be used both as an inference tool, to reconstruct biologically relevant networks from time-course scRNA-seq data, and as a simulation tool, to generate quantitative gene expression profiles in a non-trivial way through gene interactions.
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