McDermott Jason E, Oehmen Christopher S, McCue Lee Ann, Hill Eric, Choi Daniel M, Stöckel Jana, Liberton Michelle, Pakrasi Himadri B, Sherman Louis A
Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, MSIN: J4-33, Richland, WA 99352, USA.
Mol Biosyst. 2011 Aug;7(8):2407-18. doi: 10.1039/c1mb05006k. Epub 2011 Jun 23.
Systems biology attempts to reconcile large amounts of disparate data with existing knowledge to provide models of functioning biological systems. The cyanobacterium Cyanothece sp. ATCC 51142 is an excellent candidate for such systems biology studies because: (i) it displays tight functional regulation between photosynthesis and nitrogen fixation; (ii) it has robust cyclic patterns at the genetic, protein and metabolomic levels; and (iii) it has potential applications for bioenergy production and carbon sequestration. We have represented the transcriptomic data from Cyanothece 51142 under diurnal light/dark cycles as a high-level functional abstraction and describe development of a predictive in silico model of diurnal and circadian behavior in terms of regulatory and metabolic processes in this organism. We show that incorporating network topology into the model improves performance in terms of our ability to explain the behavior of the system under new conditions. The model presented robustly describes transcriptomic behavior of Cyanothece 51142 under different cyclic and non-cyclic growth conditions, and represents a significant advance in the understanding of gene regulation in this important organism.
系统生物学试图将大量不同的数据与现有知识相结合，以提供生物系统功能的模型。蓝细菌蓝藻属（Cyanothece sp.）ATCC 51142是进行此类系统生物学研究的理想候选对象，原因如下：（i）它在光合作用和固氮作用之间表现出紧密的功能调节；（ii）它在基因、蛋白质和代谢组水平上具有强大的循环模式；（iii）它在生物能源生产和碳封存方面具有潜在应用。我们将蓝藻属51142在昼夜光/暗循环下的转录组数据表示为一种高级功能抽象，并根据该生物体中的调节和代谢过程描述了昼夜和昼夜节律行为的预测性计算机模型的开发。我们表明，将网络拓扑结构纳入模型可提高我们在新条件下解释系统行为的能力方面的性能。所提出的模型有力地描述了蓝藻属51142在不同循环和非循环生长条件下的转录组行为，代表了对这一重要生物体基因调控理解的重大进展。
