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Titlebook: Computational Modeling of Signaling Networks; Lan K. Nguyen Book 2023 Springer Science+Business Media, LLC, part of Springer Nature 2023 C

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https://doi.org/10.1007/978-94-011-5100-9dentifiability analysis to analyze the identifiability of parameters. We use the ultradian endocrine model for glucose-insulin interaction as the example to demonstrate all these methods and their implementation.
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Multi-Dimensional Classical Hardy Spaces,rom random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
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Sourav S. Bhowmick,Boon-Siew Seahters, the use of this system to ask diverse experimental questions and screen drugs blocking intracellular communication, data collection, and the use of computational approaches to model and interpret these experiments.
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Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed dentifiability analysis to analyze the identifiability of parameters. We use the ultradian endocrine model for glucose-insulin interaction as the example to demonstrate all these methods and their implementation.
发表于 2025-3-26 01:46:03 | 显示全部楼层
Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathwayrom random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
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Live-Cell Sender-Receiver Co-cultures for Quantitative Measurement of Paracrine Signaling Dynamics, ters, the use of this system to ask diverse experimental questions and screen drugs blocking intracellular communication, data collection, and the use of computational approaches to model and interpret these experiments.
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