What standards are needed in computational systems biology? The results of a survey on this topic have been recently summarized by Edda Klipp and colleagues (Klipp et al, 2007, Nat Biotech 4:390). Even if it is somewhat unclear how representative the sample of respondents were with respect to the larger systems biology community, some of the top priorities identified are interesting (I list only the three first):
Respondents expressed most strongly the need for standardization in the following fields: first, graphical representation of biochemical networks; second, experimental procedures; third, model encoding and model exchange;...
How to encourage standards in biological graphical notations? One way would be to provide tools that facilitate the use of standardized notations by a wide range of users, beyond the circle of modelers. I was wondering whether a visual wiki could not help in this regard. An idea would be to have an open-source plugin for a common wiki software that would allow embedding of editable diagrams and would be specially tailored to draw biological diagrams, for example as Kitano's process diagrams or Kohn's interaction maps.
Gliffy proposes a graphical plugin for the Confluence wiki software and mxGraph is a Javascript library for web-based diagramming. Unfortunately, none of them are open-source. Cumulatedraw (see illustration) is perhaps less sophisticated but is licensed under a Creative Commons Attribution-Non Commercial-Share Alike license.
Would it be possible or easy to customize such software to specialize it for drawing biological diagrams? What are the existing solutions for visual wikis? Would a visual encyclopedia of biological processes be something that may drive biologists to use standardized notations?
Image manipulation and selective reporting of images have been recently in the focus of a healthy debate on the issue of scientific data manipulation. Of course, as editors tell us how they detect fakes ("What's in a picture? The temptation of image manipulation", Rossner and Yamada, JCB 2004 166:11), there is a danger that authors will be tempted to upgrade their ploys. More importantly, I think that these issues are symptomatic of a much larger problem in Cell & Molecular Biology which is the lack of protocols for getting from raw data to conclusions. Contrast this with genomics and 3D-structural biology where raw data are available in databases, not just a few typical photos, but every scrap of data (e.g. sequence trace data, diffraction intensities, raw RNA array data). The algorithms for getting to the conclusions are public and other labs are encouraged to redo the trajectories from data to conclusions using the original or new algorithms. This is mostly missing from Cell & Molecular Biology papers, with some excuse like "too expensive". Maybe it’s too expensive NOT to do it – not just because of data faking, but because it sends the message that we’re too lazy (or too busy) to describe in detail how we do our science and how to systematically improve it.