Hyvisual: A Hybrid System Modeling Framework Based on Ptolemy II11This work was supported in part by the Center for Hybrid and Embedded Software Systems (CHESS) at UC Berkeley, which receives support from the National Science Foundation (NSF award No. CCR-0225610): the State of California Micro Program, and the following companies: Agilent, DGIST, General Motors, Hewlett Packard, Infineon: Microsoft, and Toyota.

2006 ◽  
pp. 270-271 ◽  
Author(s):  
Edward A. Lee ◽  
Haiyang Zheng
Keyword(s):  
Hybrid System ◽  
National Science Foundation ◽  
System Modeling ◽  
Embedded Software ◽  
The State ◽  
General Motors ◽  
Software Systems ◽  
Modeling Framework ◽  
Hewlett Packard ◽  
National Science

scholarly journals Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

2018 ◽  
Vol 17 ◽  
pp. 117693511879026 ◽  
Author(s):  
Wasiu Opeyemi Oduola ◽  
Xiangfang Li
Keyword(s):  
Hybrid System ◽  
Spatial Scales ◽  
System Modeling ◽  
Modeling Framework ◽  
Stochastic Hybrid System ◽  
Cell Behaviors ◽  
Pharmacodynamic Profile ◽  
Drug Pharmacokinetic ◽  
Drug Treatments ◽  
Tumor Modeling

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

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