Modeling time-varying networks with applications to neural flow and genetic regulation.

機譯：建模時變網絡，并將其應用于神經流和遺傳調控。

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Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural growth during learning or changes in genetic regulation during cell differentiation. Approaches are needed that explicitly model networks as they change in time and that characterize the nature of those changes.;In this work, we develop a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. We first present the model, explain how to derive it from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily generalizes under varying levels of uncertainty about the data generation process. We then characterize the nature of evolving networks in several biological datasets.;We initially focus on learning how neural information flow networks change in songbirds with implanted electrodes. We characterize how they change in response to different sound stimuli and during the process of habituation. We continue to explore the neurobiology of songbirds by identifying changes in neural information flow in another habituation experiment using fMRI data. Finally, we briefly examine evolving genetic regulatory networks involved in Drosophila muscle differentiation during development.;We conclude by describing experimental methods for testing some of our results and suggesting new experimental directions and statistical extensions to the model for predicting novel neural flow results.

機譯：許多生物過程被有效地建模為網絡，但是經常假設這些網絡在數據收集過程中不會改變。但是，該假設不適用于許多現象，例如學習過程中的神經生長或細胞分化過程中遺傳調控的變化。需要一些方法來對網絡隨時間的變化進行顯式建模，并表征這些變化的性質。在這項工作中，我們開發了一類新的圖形模型，其中允許基礎數據生成過程的條件依存結構用于隨著時間的推移而變化。我們首先介紹該模型，解釋如何從貝葉斯網絡中導出該模型，并開發一種有效的MCMC采樣算法，該算法可以輕松地在數據生成過程的各種不確定性水平下進行概括。然后，我們在幾個生物學數據集中描述了進化網絡的性質。我們最初專注于學習神經信息流網絡如何在植入電極的鳴禽中發生變化。我們描述了它們如何響應于不同的聲音刺激以及在習慣化過程中發生變化。我們通過使用fMRI數據在另一個習慣化實驗中識別神經信息流的變化，繼續探索鳴禽的神經生物學。最后，我們簡要地檢查了發育過程中與果蠅肌肉分化有關的不斷發展的遺傳調控網絡。最后，我們描述了用于測試某些結果的實驗??方法，并提出了預測新神經流結果的模型的新實驗方向和統計擴展。

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作者
Robinson, Joshua Westly.;
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作者單位

Duke University.;

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授予單位 Duke University.;
學科 Biology Neuroscience.;Computer Science.
學位 Ph.D.
年度 2010
頁碼 199 p.
總頁數 199
原文格式 PDF
正文語種 eng
中圖分類
關鍵詞
入庫時間 2022-08-17 11:36:57

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Modeling time-varying networks with applications to neural flow and genetic regulation.

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