In 2008, to make data more accessible for scientific sharing and integration, the Department of Energy came up with the vision for a “Systems Biology Knowledgebase” (also referred to as Kbase) for plant and microbial life. In 2010 the research and development needed to design and implement Kbase was completed by the Genomic Science program, and work on it is currently underway.
Kbase should be a boon both for those who want to gain better understanding of such life forms for the sake of pure science and to those who would apply the Kbase data, metadata, and tools for modeling and predictive technologies to help the production of renewable biofuels and a reduction of carbon in the environment.
According to the Kbase implementation plan, “In general, a knowledgebase is an organized collection of data, organizational methods, standards, analysis tools, and interfaces representing a body of knowledge.”
Michael Schatz, a quantitative biologist involved in the project, observes of the present state of microbial life data: "There are many different 'silos' of information that have been painstakingly collected; and there are a number of existing tools that bring some strands of data into relation. But there is no overarching tool that can be used across silos." The integration of data generated by scientists around the world, combined with computational models, is expected to greatly accelerate scientific advancements.
Source: Genomic Science Program, US Dept. of Energy
Kbase would foster “open community science.” According to Kbase literature, instead of scientists working on their particular areas of investigation with their own limited data, now “any laboratory or project, regardless of size,” will have “free and open access to data, analysis tools, resources for modeling and simulation, and information.” In turn, any one of these labs could also contribute to “a transformative community-wide effort” to advance the body of knowledge and spur innovations in predictive biology.
The plan is for Kbase to start off with seven data centers on ESnet (the Department of Energy Energy Sciences Network). That is one for each of the six defined scientific objectives of Kbase; the seventh is devoted to coordinating the infrastructure development of the project. According to the current timetable, it should take 12 months to get the Kbase hardware platform operational. Version 1.0 is anticipated to be accessible after 18 months and version 2.0 after 36 months; five years is the estimated time to achieve operation and support at target levels.
The idea is to implement a system that can grow as needed and be easily used by scientists without extensive training in applications. It should produce understandable results based on clear scientific assumptions, engage all members of the scientific community, and encourage further discovery, with findings that inspire “new rounds of experiments or lines of research.”
There are three “guiding principles” for Kbase as stated in the implementation plan, and they all begin with the word “open”:
Open Access. Data and methods are available for anyone to use.
Open Source. Source code is freely available to access, modify, and redistribute.
Open Development. Anyone can contribute to the development of Kbase resources by following guidelines defined by the community.
Source: Genomic Science Program, US Dept. of Energy
The hope, of course, is that this will open the way to new discoveries and, possibly, new sources of renewable energy.
— Ariella Brown is a freelance writer, editor, and social media consultant.
Absolutely, this type of technology can be a great asset to healthcare, and it can certainly help the advance of other sciences, as well. Perhaps once the DOE paves the way, others will be able to come up with the knowledgebase systems that will reach their goals in less than 5 years.
Ariella, I wonder what other disciplines will begin to apply the concept of a knowledgebase?
Actually, IBM is trying to apply the knowledgebase of Watson to the healthcare industry in providing doctors with latest database and evidence-based literature for diagnosis, etc.
If we begin to think about the sharing of knowledge and then the patterning, and then use the technology tools developed I believe we will unleash new opportunities of discovery.
You're right, DHager. The genome project also brought that a great deal of data is generated byresearch and that a solution to organizing it to make it accessible could greatly accelerate scientific advances.
Ariella, great blog. That is fascinating; obviously they were stimulated by the recent genome project where they pooled knowledge and now see the benefits of applying technology.
You and jabailo are correct in looking at the engineering structure and comparing with Watson. What I think will be interesting is not only the various data engineering tools, but the models that evolve in the further use and development of artificial intelligence.
Examining Technologies for Database Management Systems that Support Computational Biology and Bioinformatics Applications• Principal Investigator: Victor Markowitz (Lawrence Berkeley National Laboratory and the DOE Joint Genome Institute)This project focused on evaluating new database management system technologies that allow efficient analysis of very large datasets. Prototypes of a large database based on the DOE JGI's Integrated Microbial Genomes (IMG) data management system were implemented using several of these technologies. Performance tests of IMG "all versus all" data were conducted in Hbase on the DOE National Energy Research Scientific Computing Center's Magellan Hadoop cluster and on a smaller departmental Hadoop cluster. Results show that distributed tabular storage has significant long-term potential for Kbase but that it is not yet ready for large-scale production use. Investigators note that Hadoop and Hbase currently are undergoing rapid development, and they anticipate that stability issues will be addressed within the next 2 years. DOE JGI is now implementing the Magellan cloud infrastructure for microbial genome assembly and annotation based on the results of this pilot test.
The same PDF also contains a couple of models of the architecture, which includes several layers: the Kbase core services, Kbase infrastructure services, the unified application interface, the data consistency and experimental design engine, and the Kbase end user web interface.
From what I read that sounds like a lot of the infrastructure that Watson was based on. Hadoop a key part of the multithreading. The OS was SuseLinux for Watson.
However, I guess I'm speaking more to the technologies of knowledge retrieval, data miniing, knowledge representation and natural language understanding that ride on top of that infrastructure.
Kbase does not appear to have a Watson. It has a Kandinsky:
the hardware configuration includes over 0.5 petabytes of storage on local nodes under the direction of the Hadoop Distributed File System.
In addition to supporting Hadoop based applications, support for private cloud virtualization will be added via the Eucalyptus infrastructure software that enables establishment of private cloud computing environments. Eucalyptus is interface-compatible with the Amazon Web Services (AWS) cloud infrastructure, which means users can reuse existing AWS-compatible tools and scripts to manage their own private cloud, run Amazon Machine Images on their private cloud and cloud-burst to other public-clouds (also known as hybrid clouds -- a private on-premise cloud, in this case, a Eucalyptus cloud, working seamlessly with a public cloud).
It was a country fried pork chop with mashed potatoes and hot apple sauce!
Should they be using IBM systems?
Well, more to the point (and maybe I should stick to being a writer instead of a videographer) I am specifically talking about the AI program Watson which dazzled us on Jeopardy. When I heard about multiple lines of logic and inquiry, it sounded exactly what they had to do to get Watson to be able to evaluate across many types of expertise -- they built multiple, multithreaded inference engines to be able to handle all the types.
Just seeing Watson in action, makes we think we need a whole new mindset when we plan any large scale, broad based knowledge systems...starting with looking at Watson itself as an option!
Jabailo, how can you point to your TV dinner tray without telling us exactly what you ate? I mean that's what the internet is all about -- sharing what you have for dinner, at least as far as a lot of Twitter and FB posts go.
I contacted 4 people whose names were connected to this project about their choice of cloud service provider but got no answers. At best, they replied with a friendly email that said they passedo on my queries to the PR person. I even found her contact info myself and emailed her directly. No response. It looks like they are planning to build something very elaborate on their own. Should they be using IBM systems? Possibly, yes. But I didn't get to discuss that with anyone involved in the project.
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