The OME Framework for genome-scale systems biology
- Univ. of California, San Diego, CA (United States)
The life sciences are undergoing continuous and accelerating integration with computational and engineering sciences. The biology that many in the field have been trained on may be hardly recognizable in ten to twenty years. One of the major drivers for this transformation is the blistering pace of advancements in DNA sequencing and synthesis. These advances have resulted in unprecedented amounts of new data, information, and knowledge. Many software tools have been developed to deal with aspects of this transformation and each is sorely needed. However, few of these tools have been forced to deal with the full complexity of genome-scale models along with high throughput genome-scale data. This particular situation represents a unique challenge, as it is simultaneously necessary to deal with the vast breadth of genome-scale models and the dizzying depth of high-throughput datasets. It has been observed time and again that as the pace of data generation continues to accelerate, the pace of analysis significantly lags behind. It is also evident that, given the plethora of databases and software efforts, it is still a significant challenge to work with genome-scale metabolic models, let alone next-generation whole cell models. We work at the forefront of model creation and systems scale data generation. The OME Framework was borne out of a practical need to enable genome-scale modeling and data analysis under a unified framework to drive the next generation of genome-scale biological models. Here we present the OME Framework. It exists as a set of Python classes. However, we want to emphasize the importance of the underlying design as an addition to the discussions on specifications of a digital cell. A great deal of work and valuable progress has been made by a number of communities towards interchange formats and implementations designed to achieve similar goals. While many software tools exist for handling genome-scale metabolic models or for genome-scale data analysis, no implementations exist that explicitly handle data and models concurrently. The OME Framework structures data in a connected loop with models and the components those models are composed of. This results in the first full, practical implementation of a framework that can enable genome-scale design-build-test. Over the coming years many more software packages will be developed and tools will necessarily change. However, we hope that the underlying designs shared here can help to inform the design of future software.
- Research Organization:
- Univ. of California, San Diego, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- SC0004917
- OSTI ID:
- 1169326
- Report Number(s):
- DOE-UCSD-8701
- Country of Publication:
- United States
- Language:
- English
Similar Records
Data Intensive Analysis of Biomolecular Simulations
Data Intensive Analysis of Biomolecular Simulations