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Quantitative analysis for complex biological models using qualitative data: Applications in developmental biology.

机译：使用定性数据对复杂生物学模型进行定量分析：在发育生物学中的应用。

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摘要

Better understanding the many complex processes governing living organisms relies on the combination of efficient experimentation and careful consideration in a theoretical framework. Informed by experimental data, mathematical modeling offers many tools to aid comprehension of complex systems, providing critical support throughout biological sciences. However, the technical challenges to performing precise experiments and making many molecular measurements, all in fragile living systems, limit the ability to quantify data. Much biological data is instead qualitative, especially in fields such as developmental biology, which emphasizes imaging molecular distributions across many cells or whole tissues. In contrast with quantitative measurements, there is an absence of tools to incorporate information from these qualitative data into the mathematical models used to understand complex interactions, compare and distinguish hypotheses, predict behavior, and plan experiments. The work presented in this dissertation develops strategies to address the technical limitations to quantitative modeling with qualitative data, applied in the context of developmental biology. Two parallel objectives are discussed. The theoretical objective is the development of a parameter estimation procedure for complex models that accommodates qualitative information, based on existing qualitative and quantitative techniques. The biological objective is the elucidation of stem cell regulatory mechanisms through study of the Drosophila germarium, a stem cell niche in the ovary. Mathematical representations of the germarium system are formulated based on experimental evidence, and employed to evaluate the viability and potential effects of several proposed mechanisms. Through the newly developed parameter estimation procedure, multiple hypothetical mechanisms are compared based on a compilation of published qualitative data from wild type flies and genetic mutants. The extent to which these experiments can distinguish hypotheses is shown, and the quantitatively tuned models are used to estimate the utility of feasible future experiments to refine models and better discriminate among them. The framework and procedure developed herein offer benefits to many applications of mathematical modeling in biology, biotechnology and other fields where qualitative data are prevalent.
机译：更好地理解控制生物体的许多复杂过程，需要在理论框架中结合有效的实验和仔细的考虑。借助实验数据，数学建模提供了许多有助于理解复杂系统的工具，为整个生物学提供了关键支持。但是，在脆弱的生命系统中进行精确实验和进行许多分子测量的技术挑战限制了对数据进行量化的能力。相反，许多生物学数据是定性的，尤其是在诸如发育生物学等领域，其强调成像许多细胞或整个组织中的分子分布。与定量测量相反，没有工具将这些定性数据中的信息整合到用于理解复杂相互作用，比较和区分假设，预测行为和计划实验的数学模型中。本文提出的工作提出了解决定性数据定量建模技术局限性的策略，并在发展生物学的背景下应用。讨论了两个并行的目标。理论上的目标是根据现有的定性和定量技术，为容纳定性信息的复杂模型开发参数估计程序。生物学目的是通过研究果蝇果蝇（果蝇在卵巢中的一个小生境）来阐明干细胞调节机制。细菌系统的数学表示是根据实验证据制定的，并用于评估几种拟议机制的可行性和潜在影响。通过新开发的参数估计程序，基于来自野生型果蝇和遗传突变体的已发布定性数据的汇编，比较了多种假设机制。显示了这些实验可以区分假设的程度，并使用定量调整的模型来估算可行的未来实验的效用，以完善模型并更好地区分模型。本文开发的框架和过程为数学建模在生物学，生物技术和定性数据盛行的其他领域的许多应用提供了好处。

著录项

  • 作者

    Pargett, Michael.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 185 p.
  • 总页数 185
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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