2015英特爾ISEF獲獎(jiǎng)作品摘要 計(jì)算生物與生物信息學(xué) Intel Finalist Abstract Computational Biology and Bioinformatics

獲獎(jiǎng)作品基本信息

年份	2015
學(xué)科	計(jì)算生物與生物信息學(xué) Computational Biology and Bioinformatics
國(guó)家/州	United States of America

獲獎(jiǎng)作品名稱(chēng)

Prognostic and Diagnostic Measure for Circuit Disruption in Alzheimer's Disease

獲獎(jiǎng)作品摘要

Alzheimer's disease (AD) is a neurodegenerative disease known for significant brain atrophy and pathological lesions that are thought to damage synaptic connections between cells. In particular, gamma-aminobutyric acid (GABA) based synapses that inhibit neurons are thought to be disrupted in AD, causing hyper-excited circuitry and cognitive impairment. Currently, AD diagnosis methodologies are unclear until advanced stages, or via post-mortem analysis. The purpose and modeling goal was to create a dynamic causal model (DCM) to serve as a prognostic and diagnostic measure for early state AD. Another goal was to determine a drug that would be most effective in pharmacological intervention to increase GABAergic function and memory performance.??

The DCM was created in STELLA by isee systems with differential equations describing the state of cell population at a given time. The parameters of firing variance and GABA receptor conductivity (GRC) underwent sensitivity runs, and results of a power spectral density analysis showed that these parameters controlled gamma power in a non-linear fashion. The firing variance should be larger than about 150 to increase gamma power, but the GRC must be specifically four S/m to reach maximum power. This suggests that a drug combination that induces these effects such as Ketamine of GABA agonists could improve memory function by enhancing gamma power. For diagnosis, this DCM can be applied to suspected patients and use the variance and conductivity parameters as biomarkers. Both goals were successful because the gamma spectra, which relate to cognitive performance, were found to be sensitive to the model parameters that can be altered by currently utilized pharmacological interventions.

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ISEF 簡(jiǎn)介

英特爾國(guó)際科學(xué)與工程大獎(jiǎng)賽，簡(jiǎn)稱(chēng) "ISEF"，由美國(guó) Society for Science and the Public（科學(xué)和公共服務(wù)協(xié)會(huì)）主辦，英特爾公司冠名贊助，是全球規(guī)模最大、等級(jí)最高的中學(xué)生的科研科創(chuàng)賽事。ISEF 的學(xué)術(shù)活動(dòng)學(xué)科包括了所有數(shù)學(xué)、自然科學(xué)、工程的全部領(lǐng)域和部分社會(huì)科學(xué)。ISEF 素有全球青少年科學(xué)學(xué)術(shù)活動(dòng)的“世界杯”之美譽(yù)，旨在鼓勵(lì)學(xué)生團(tuán)隊(duì)協(xié)作，開(kāi)拓創(chuàng)新，長(zhǎng)期專(zhuān)一深入地研究自己感興趣的課題。

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英特爾 ISEF 學(xué)術(shù)活動(dòng)詳細(xì)介紹

英特爾 ISEF 全程指導(dǎo)方案

· 數(shù)學(xué) · 物理 · 化學(xué) · 生物 · 計(jì)算機(jī) · 工程 ·

學(xué)科簡(jiǎn)介：計(jì)算生物與生物信息學(xué) 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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