Discrete Dynamic Modeling: A Network Approach for Systems Pharmacology

Discrete dynamic modeling of rotating Timoshenko shafts deflecting in axial, shear and bending directions is discussed in this article. The method presented here aims for developing an algorithm by which shafts with transverse cracks can easily be modeled and analyzed. To this end, a modular dynamic network is constructed, consisting of a mass, springs and dampers, resembling shaft segments between cracks. The local changes in shaft stiffness as a result of cracks rotating (opening and closing), is entered into the module models as change in springs and dampers properties. The modular model is then repeated to obtain a complete shaft model. Bond Graph method is used in a similar modular manner to extract dynamic equations of shaft motion. The equations have then been solved for shaft lateral displacement and behavior under external loadings. The method presented here enables the users to develop a model of the shaft to simulate the behavior of shafts having cracks, with today’s computational software.

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Discrete Dynamic Modeling of Cellular Signaling Networks

Methods in Enzymology ◽

10.1016/s0076-6879(09)67011-7 ◽

2009 ◽

pp. 281-306 ◽

Cited By ~ 38

Author(s):

Réka Albert ◽

Rui-Sheng Wang

Keyword(s):

Dynamic Modeling ◽

Cellular Signaling ◽

Signaling Networks ◽

Discrete Dynamic

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Discrete Dynamic Modeling and Change Trend Analysis of Regional Economy Based on Big Data

Discrete Dynamics in Nature and Society ◽

10.1155/2021/5360624 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yang Chen ◽

Yu Yu

Keyword(s):

Big Data ◽

Dynamic Modeling ◽

Regional Economy ◽

Experimental Testing ◽

Modeling Method ◽

Bayesian Regression ◽

Government Support ◽

Regional Economic ◽

Discrete Dynamic ◽

Modeling Technology

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing.

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Discrete Dynamic Modeling of Signal Transduction Networks

Methods in Molecular Biology - Computational Modeling of Signaling Networks ◽

10.1007/978-1-61779-833-7_12 ◽

2012 ◽

pp. 255-272 ◽

Cited By ~ 6

Author(s):

Assieh Saadatpour ◽

Réka Albert

Keyword(s):

Signal Transduction ◽

Dynamic Modeling ◽

Discrete Dynamic ◽

Signal Transduction Networks

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The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

Emerging Topics in Life Sciences ◽

10.1042/etls20180178 ◽

2019 ◽

Vol 3 (1) ◽

pp. 97-105

Author(s):

Mary Zuccato ◽

Dustin Shilling ◽

David C. Fajgenbaum

Keyword(s):

Rare Diseases ◽

Treatment Options ◽

Castleman Disease ◽

High Impact ◽

Collaborative Network ◽

Network Approach ◽

Grant Applications ◽

Research Questions ◽

Novel Strategy ◽

Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

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