Systems biology graphical notation (SBGN)

Published on November 4, 2014   42 min

Other Talks in the Series: Systems Biology

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So the title of this talk is Systems Biology Graphical Notation. My name is Huaiyu Mi. I work at the Division of Bioinformatics at Tech School of Medicine in University of Southern California.
So what is SBGN? SBGN is a standard graphical representation of biochemical and cellular events. It is an unambiguous way to graphically describe and interpret pathway network knowledge by both computer and human. It is a representation of logical mechanistic models, biochemical pathways, at different levels of granularity. It has a set of defined symbols together with rules such as semantics, syntax, and also layout rules. You can find detailed information and the website listed here. And today in this talk, I'm going to focus on the specification of SBGN. On the website you can also find software support and also a parallel project called libSBGN, which I'm not going to talk about in this talk. On the website you can also find detailed technical specifications, precise data models, and growing software support. I just want to emphasize that I'm talking on behalf of the large community and this project has been developed over eight years by a very diverse community. And so the work was not done just by myself. It's really it reflects the work from the entire community.
So what is the motivations to start this project?
So, this is a schematic diagram showing the general paradigms for biochemical research. We always start with the biological hypothesis. And then we design experiments and run experiments in nowadays in the post-genome era. Most of the experiments we've done are high throughput experiments. So what you're going to again are all the high density data results. And then you use softwares and tools and database to analyze this results. What you expect is you can get some functional implications from these results. Once you get these functional results, oftentimes you're going to postulate another hypothesis and runs through this cycle again. After several iterations at the end, you will find probably what you expect the cure for a certain disease spread.