標(biāo)題: Titlebook: Computational Modeling of Signaling Networks; Lan K. Nguyen Book 2023 Springer Science+Business Media, LLC, part of Springer Nature 2023 C [打印本頁] 作者: 粘上 時(shí)間: 2025-3-21 19:38
書目名稱Computational Modeling of Signaling Networks影響因子(影響力)
書目名稱Computational Modeling of Signaling Networks影響因子(影響力)學(xué)科排名
書目名稱Computational Modeling of Signaling Networks網(wǎng)絡(luò)公開度
書目名稱Computational Modeling of Signaling Networks網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Modeling of Signaling Networks被引頻次
書目名稱Computational Modeling of Signaling Networks被引頻次學(xué)科排名
書目名稱Computational Modeling of Signaling Networks年度引用
書目名稱Computational Modeling of Signaling Networks年度引用學(xué)科排名
書目名稱Computational Modeling of Signaling Networks讀者反饋
書目名稱Computational Modeling of Signaling Networks讀者反饋學(xué)科排名
作者: Aspirin 時(shí)間: 2025-3-21 20:43 作者: 小卒 時(shí)間: 2025-3-22 01:52
https://doi.org/10.1007/978-1-0716-3008-2Cell signaling; Disease-relevant data; Novel drug targets; Signaling pathways; Personalized medicine作者: 龍蝦 時(shí)間: 2025-3-22 08:03 作者: 狂怒 時(shí)間: 2025-3-22 12:42 作者: 柔軟 時(shí)間: 2025-3-22 13:48 作者: 柔軟 時(shí)間: 2025-3-22 18:20
J. Eriksen,M. D. Murphy,E. Schnug of kinetic parameters and state variables. Depending on the specific parameter values, a network can display one of a variety of possible dynamic behaviors such as monostable fixed point, damped oscillation, sustained oscillation, and/or bistability. Understanding how a network behaves under a part作者: 自制 時(shí)間: 2025-3-23 00:04 作者: Blood-Vessels 時(shí)間: 2025-3-23 01:21
Food Production and Plant Nutrient Sulphur,oduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to suppo作者: 虛弱 時(shí)間: 2025-3-23 07:34 作者: Memorial 時(shí)間: 2025-3-23 12:44
,Fünfzehnerspiel und Zauberwürfel,en these fluctuations dictate the outcome of a cell-fate decision-making event. Thus, having an accurate estimate of these fluctuations for any biological network is extremely important. There are well-established theoretical and numerical methods to quantify the intrinsic fluctuation present within作者: triptans 時(shí)間: 2025-3-23 15:45
Raten, Beweisen, Wundern und Zaubern,ables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-d作者: 我的巨大 時(shí)間: 2025-3-23 19:13
https://doi.org/10.1007/978-3-319-74648-7ls in its ability to model complex features of cellular signalling pathways including stochasticity, spatial effects, and heterogeneity, thus improving our understanding of critical decision processes in biology. Yet, particle-based modelling is computationally prohibitive to implement. We recently 作者: Licentious 時(shí)間: 2025-3-24 00:11 作者: Between 時(shí)間: 2025-3-24 03:21
The ,-Dimensional Dyadic Derivative, molecular mechanisms. Over the past decade, mathematical models have been developed based on quantitative observations, such as live-cell imaging and biochemical assays. However, it is difficult to directly integrate next-generation sequencing (NGS) data. Although highly dimensional, NGS data mostl作者: 等待 時(shí)間: 2025-3-24 06:41
https://doi.org/10.1007/978-3-319-54621-6mprehensive understanding of cellular responses, identifying points of interaction between the underlying molecular networks is essential. Here, we present an approach that allows the systematic prediction of such interactions by perturbing one pathway and quantifying the concomitant alterations in 作者: 娘娘腔 時(shí)間: 2025-3-24 11:01
Sourav S. Bhowmick,Boon-Siew Seahmethod for quantitatively measuring paracrine signaling dynamics, and resultant gene expression changes, in living cells using genetically encoded signaling reporters and fluorescently tagged gene loci. We discuss considerations for selecting paracrine “sender-receiver” cell pairs, appropriate repor作者: glamor 時(shí)間: 2025-3-24 15:14
https://doi.org/10.1007/978-3-642-72025-3 programing. Here I present a protocol for decoding cell fates through systematic interrogation with optogenetics and visualization of signaling with live biosensors. Specifically, this is written for Erk control of cell fates using the optoSOS system in mammalian cells or . embryos, though it is in作者: Obsessed 時(shí)間: 2025-3-24 20:06
J. A. Kengmogne Tchakam,H. -G. Reimerdesl model of the RAS signaling network that has previously been developed and applied to specific RAS mutants will be adapted for the process of computational random mutagenesis. By using this model to computationally investigate the range of RAS signaling outputs that would be anticipated over a wide作者: 膽大 時(shí)間: 2025-3-25 01:59 作者: angina-pectoris 時(shí)間: 2025-3-25 07:24
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.作者: diabetes 時(shí)間: 2025-3-25 09:07
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.作者: 充滿人 時(shí)間: 2025-3-25 13:08
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.作者: 反饋 時(shí)間: 2025-3-25 19:50 作者: Bombast 時(shí)間: 2025-3-25 21:40
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.作者: coalition 時(shí)間: 2025-3-26 01:46
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.作者: acclimate 時(shí)間: 2025-3-26 07:21
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.作者: 旁觀者 時(shí)間: 2025-3-26 10:59 作者: 颶風(fēng) 時(shí)間: 2025-3-26 16:06 作者: Indent 時(shí)間: 2025-3-26 18:37 作者: Evolve 時(shí)間: 2025-3-27 00:53 作者: 懦夫 時(shí)間: 2025-3-27 02:30 作者: Radiation 時(shí)間: 2025-3-27 05:52
1064-3745 ation advice from the experts.This volume focuses on the computational modeling of cell signaling networks and the application of these models and model-based analysis to systems and personalized medicine. Chapters guide readers through various modeling approaches for signaling networks, new methods作者: 節(jié)省 時(shí)間: 2025-3-27 11:56 作者: Rodent 時(shí)間: 2025-3-27 15:20
Computational Random Mutagenesis to Investigate RAS Mutant Signalingtional random mutagenesis. By using this model to computationally investigate the range of RAS signaling outputs that would be anticipated over a wide range of the relevant parameter space, one can gain intuition about the types of behaviors that would be demonstrated by biological RAS mutants.作者: 偉大 時(shí)間: 2025-3-27 18:09 作者: Admonish 時(shí)間: 2025-3-28 00:14 作者: Organization 時(shí)間: 2025-3-28 06:09 作者: encyclopedia 時(shí)間: 2025-3-28 09:39
A Practical Guide to Reproducible Modeling for Biochemical Networksoduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to suppo作者: Barrister 時(shí)間: 2025-3-28 12:35
Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulato mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between t作者: Homocystinuria 時(shí)間: 2025-3-28 15:29
Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulationsen these fluctuations dictate the outcome of a cell-fate decision-making event. Thus, having an accurate estimate of these fluctuations for any biological network is extremely important. There are well-established theoretical and numerical methods to quantify the intrinsic fluctuation present within作者: 信徒 時(shí)間: 2025-3-28 20:56 作者: ethereal 時(shí)間: 2025-3-28 23:17
Rapid Particle-Based Simulations of Cellular Signalling with the FLAME-Accelerated Signalling Tool (ls in its ability to model complex features of cellular signalling pathways including stochasticity, spatial effects, and heterogeneity, thus improving our understanding of critical decision processes in biology. Yet, particle-based modelling is computationally prohibitive to implement. We recently 作者: FLAGR 時(shí)間: 2025-3-29 05:46 作者: 共和國 時(shí)間: 2025-3-29 10:08 作者: committed 時(shí)間: 2025-3-29 11:32 作者: 確定方向 時(shí)間: 2025-3-29 19:33
Live-Cell Sender-Receiver Co-cultures for Quantitative Measurement of Paracrine Signaling Dynamics, method for quantitatively measuring paracrine signaling dynamics, and resultant gene expression changes, in living cells using genetically encoded signaling reporters and fluorescently tagged gene loci. We discuss considerations for selecting paracrine “sender-receiver” cell pairs, appropriate repor作者: 連累 時(shí)間: 2025-3-29 20:07 作者: dictator 時(shí)間: 2025-3-30 00:43
Computational Random Mutagenesis to Investigate RAS Mutant Signalingl model of the RAS signaling network that has previously been developed and applied to specific RAS mutants will be adapted for the process of computational random mutagenesis. By using this model to computationally investigate the range of RAS signaling outputs that would be anticipated over a wide作者: ticlopidine 時(shí)間: 2025-3-30 05:49 作者: 為現(xiàn)場 時(shí)間: 2025-3-30 08:31
A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Mod作者: 憎惡 時(shí)間: 2025-3-30 15:16
Design Principles Underlying Robust Adaptation of Complex Biochemical Networksosable into just two types of network building-blocks—opposer modules and balancer modules. Here we present an overview of the design principles that characterize all RPA-capable network topologies through a detailed examination of a collection of simple examples. We also introduce a diagrammatic me作者: HEW 時(shí)間: 2025-3-30 16:44
Multi-Dimensional Analysis of Biochemical Network Dynamics Using pyDYVIPACal to the field of synthetic biology. In this chapter, we will present a practical guide to the multidimensional exploration, analysis, and visualization of network dynamics using pyDYVIPAC, which is a tool ideally suited to these purposes implemented in Python. The utility of pyDYVIPAC will be demo作者: 泥土謙卑 時(shí)間: 2025-3-30 21:21
Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulato regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogrammin作者: 敲竹杠 時(shí)間: 2025-3-31 00:52
Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulations estimate these extrinsic fluctuations for experimentally constructed bidirectional transcriptional reporter systems along with the intrinsic variability. We use the Nanog transcriptional regulatory network and its variants to illustrate our numerical method. Our method reconciled experimental obser作者: biopsy 時(shí)間: 2025-3-31 07:03
Meta-Dynamic Network Modelling for Biochemical Networkseals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems t作者: 注視 時(shí)間: 2025-3-31 10:55 作者: MIME 時(shí)間: 2025-3-31 14:52 作者: 個(gè)阿姨勾引你 時(shí)間: 2025-3-31 18:04
Resolving Crosstalk Between Signaling Pathways Using Mathematical Modeling and Time-Resolved Single of p53 to genotoxic stress using time-resolved single cell data and perturbed NF-κB signaling by inhibiting the kinase IKK2. Employing a subpopulation-based modeling approach enabled us to identify multiple interaction points that are simultaneously affected by perturbation of NF-κB signaling. Henc
