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Intracellular organelle networks： Understanding their organization and communication through systems-level modeling and analysis

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Frontiers in Biology 2017年第1期12卷 7-18页

作者： qinle ba Ge Yang Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA 15213 USA Department of Computational Biology Carnegie Mellon University Pittsburgh PA 15213 USA

baCKGROUND： Membrane-bound intracellular organelles are biochemically distinct compartments used by eukaryotic cells for serving specialized physiological functions and organizing their internal environment. Recent studies revealed surprisingly extensive communication between these organelles and highlighted the network nature of their organization and communication. Since organization and communication of the organelles are carried out at the systems level through their networks, systems-level studies are essential for understanding the underlying mechanisms. METHODS： We reviewed recent studies that used systems-level quantitative modeling and analysis to understand organization and communication of intracellular organelle networks. RESULTS： We first review modeling and analysis studies on how fusion/fission and degradation/biogenesis, two essential and closely related classes of activities of individual organelles, collectively mediate the dynamic organization of their networks. We then turn to another important aspect of the dynamic organization of the organelle networks, namely how organelles are physically connected within their networks, a property referred to as the topology of the networks in mathematics, and summarize some of their distinct properties. Lastly, we briefly review modeling and analysis studies that aim to understand communication between different organelle networks, focusing on cellular calcium homeostasis as an example. We conclude with a brief discussion of future directions for research in this area. CONCLUSIONS： Together, the reviewed studies provide critical insights into how diverse activities of individual organelles collectively mediate the organization and communication of their networks. They demonstrate the essential role of systems- level modeling and analysis in understanding complex behavior of such networks.

关键词： intracellular organelle organelle network organelle communication network analysis systems modeling

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Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology

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Frontiers of Electrical and Electronic Engineering in China 2018年第4期6卷 344-358页

作者： Xiaoqi Chai qinle ba Ge Yang Department of Biomedical Engineering Carnegie Mellon UniversityPittsburghPA 15213USA Department of Computational Biology Carnegie Mellon UniversityPittsburghPA 15213USA

Quantitative analysis of mitochondrial morphology plays important roles in studies of mitochondrial biology. The analysis depends critically on segmentation of mitochondria, the image analysis process of extracting mitochondrial morphology from images. The main goal of this study is to characterize the performance of convolutional neural networks (CNNs) in segmentation of mitochondria from fluorescence microscopy images. Recently, CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines. So far, however, our knowledge of their performance in segmenting biological images remains limited. In particular, we know little about their robustness, which defines their capability of segmenting biological images of different conditions, and their sensitivity, which defines their capability of detecting subtle morphological changes of biological objects. We have developed a method that uses realistic synthetic images of different conditions to characterize the robustness and sensitivity of CNNs in segmentation of mitochondria. Using this method, we compared performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U-Net. We further compared the two networks against the adaptive active-mask (AAM) algorithm, a representative of high-performance conventional segmentation algorithms. The FCN and the U-Net consistently outperformed the AAM in accuracy, robustness, and sensitivity, often by a significant margin. The U-Net provided overall the best performance. Our study demonstrates superior performance of the U-Net and the FCN in segmentation of mitochondria. It also provides quantitative measurements of the robustness and sensitivity of these networks that are essential to their applications in quantitative analysis of mitochondrial morphology.

关键词： convolutional neural network mitochondrial morphology image segmentation robustness sensitivity

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