Analysis and control of genetic regulatory systems with switched drug inputs

2018

Cancer Inform

Self Cite

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.

“…Genetic regulation in a pathway with drug perturbation can be modeled with rate equations that express the differences between production and degradation rates, 11 , 28 , 29 as follows:…”

Section: Multiscale Model Formulationmentioning

confidence: 99%

Section: Part Ii: Proliferation and Survival Pathway Modelingmentioning

confidence: 99%

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

2018

Cancer Inform

Self Cite

Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach

IEEE Trans. Biomed. Eng.

Chen

et al. 2018

Time-Based Switching Control of Genetic Regulatory Networks: Toward Sequential Drug Intake for Cancer Therapy

Chen

et al. 2017

Cancer Inform

Self Cite

As cancer growth and development typically involves multiple genes and pathways, combination therapy has been touted as the standard of care in the treatment of cancer. However, drug toxicity becomes a major concern whenever a patient takes 2 or more drugs simultaneously at the maximum tolerable dosage. A potential solution would be administering the drugs in a sequential or alternating manner rather than concurrently. This study therefore examines the feasibility of such an approach from a switched system control perspective. Particularly, we study how genetic regulatory systems respond to sequential (switched) drug inputs using the time-based switching mechanism. The design of the time-driven drug switching function guarantees the stability of the genetic regulatory system and the repression of the diseased genes. Simulation results using proof-of-concept models and the proliferation and survival pathways with sequential drug inputs show the effectiveness of the proposed approach.