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1-102C-CancerGrowth-ModelingScenario

Author(s): Jennie D'Ambroise1, Jue Wang

SUNY Old Westbury, Old Westbury NY USA

Keywords: optimization cancer logistic exponential prediction tumor growth data fitting power law Bertanffy Gompertz

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Abstract

Resource Image This module guides students in the use of differential equation models to predict cancer growth and study treatment outcomes. Several classical models for cancer growth are presented including exponential, power law, Bertalanffy, logistic, and Gompertz.

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Article Context

Resource Type
Differential Equation Type
Technique
Qualitative Analysis
Application Area
Course
Course Level
Lesson Length
Technology
Approach
Skills
Key Scientific Process Skills
Assessment Type
Pedagogical Approaches
Vision and Change Core Competencies - Ability
Principles of How People Learn
Bloom's Cognitive Level

Description

Students solve first-order differential equations using guess and check. They examine the behaviors of the equations and solutions through qualitative techniques. Furthermore, students evaluate how cancer treatments affect tumor growth. Real cancer data are provided to give options for data fitting and prediction of cancer growth. It addresses some main topics in differential equations, such as initial value problems, analytic solutions, qualitative approaches, and numerical approximation. Through step-by-step investigation, conjecturing, predicting, and analyzing, students discover how their knowledge can be used to address complex and real problems, and improve problem solving ability and mathematical reasoning skills.

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Authors

Author(s): Jennie D'Ambroise1, Jue Wang

SUNY Old Westbury, Old Westbury NY USA

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