2015英特尔ISEF获奖作品摘要 计算生物与生物信息学 Intel Finalist Abstract Computational Biology and Bioinformatics

获奖作品基本信息

年份 2015
学科 计算生物与生物信息学 Computational Biology and Bioinformatics
国家/州 United States of America

获奖作品名称

Discrete Markov Chains: A Novel Approach to Tumor Angiogenesis

获奖作品摘要

Angiogenesis, the development of new vasculature, is a critical process in the growth of new tumors. Driven by a goal to understand this aspect of cancer proliferation, I developed a discrete computationally optimized mathematical model of angiogenesis that specializes in intercellular interactions. Using parameters calculated through Sensitivity Analysis and experimentally observed data, I modeled vascular endothelial growth factor spread and dynamics of endothelial cell movement in a competitive environment.

Simulation testing yields the critical limits of angiogenesis to be 102 um and 153 um respectively, beyond which angiogenesis will not successfully occur. Cell density in the surrounding region and the concentration of extracellular matrix fibers are also found to directly inhibit angiogenesis. Through these three factors, I postulate a method for establishing criticality of a tumor based upon the likelihood of angiogenesis completing.

This research expands on other work by choosing factors that are patient-dependent through an specialized Cellular Potts model , which serves to optimize and increase accuracy of the model. By doing such, this model establishes a theoretical framework for analyzing lesions using angiogenic properties, with the ability to potentially compute the criticality of tumors with the aid of medical imaging technology.

 


ISEF 简介

英特尔国际科学与工程大奖赛,简称 "ISEF",由美国 Society for Science and the Public(科学和公共服务协会)主办,英特尔公司冠名赞助,是全球规模最大、等级最高的中学生的科研科创赛事。ISEF 的竞赛学科包括了所有数学、自然科学、工程的全部领域和部分社会科学。ISEF 素有全球青少年科学竞赛的“世界杯”之美誉,旨在鼓励学生团队协作,开拓创新,长期专一深入地研究自己感兴趣的课题。

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英特尔 ISEF 竞赛详细介绍

英特尔 ISEF 全程指导方案

· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·

学科简介:计算生物与生物信息学 Computational Biology and Bioinformatics

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling (MOD): Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD): The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology (EVO): A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience (NEU): A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology (PHA): A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics (GEN): The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other (OTH)Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.

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