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

Frontiers in Biology, ISSN: 1674-7992, Vol: 12, Issue: 1, Page: 7-18
2017
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Review Description

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. Conclusion: 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 systemslevel modeling and analysis in understanding complex behavior of such networks.

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