Tool Tales: Leukippos – Synthetic Biology Lab in the Cloud

ORIGINAL: Nature
by Soapbox Science Editor
01 May 2012

Science Online New York (SoNYC) encourages audience participation in the discussion of how science is carried out and communicated online. To celebrate our first birthday, we are handing the mic over to the audience so that anyone who would like to participate will get five minutes to show off their favourite online tool, application or website that makes science online fun. To complement the celebrations, we’re hosting a series of guest posts on Soapbox Science where a range of scientists share details about what’s in their online science toolkits. Why not let us know how they compare to the tools that you use in the comment threads?

Gerd Moe-Behrens has a PhD from the Faculty of Medicine, University of Oslo, Norway. During a brief Post Doc at the Max Planck Institute for Molecular Genetics, Berlin, Germany his scientific focus was on Induced Pluripotent Stem Cells. This work introduced him to Systems Biology. This was the starting point for his interest in Synthetic Biology with a special focus on biocomputing. He started to join the Apple developer program, focused on dry lab work and work on an Apple platform (xcode, dashcode, objective c, html5, css3, java). He then founded the Leukippos Institute for Synthetic Biology, a research institute solely in the cloud. He has a strong interest to explore novel forms of scientific work on a web platform. His personal research interests are computer assisted design, morphogenesis and cellular computers.

21st century science faces two challenges:

I believe that there are two major digital trends driving recent innovations in science and technology; both are changing the way we perform and look at research. (See here for a detailed review). 

How to deal with the enormous amount of data? 

The first trend is the enormous storage and processing capacity which has emerged. We talk about a magnitude in the “petabyte” scale, which is equivalent to 1000 terabytes (TB) = 1 quadrillion bytes = 10E+15 bytes. Google processes about 24 petabytes per day. Within the last decade or so, scientific research (such as research in biology, bioinformatics, and medicine, to name a few) has increasingly produced vast amounts of data from high throughput experiments. We have also witnessed an exponential increase in the number and/or size of data sets, in particular in biology and bioinformatics research. For example, the 1000 Genomes Project has so far produced 200 TB publicly available data sets since its inception. Moreover, the output in scientific literature has become so vast and complex, that it has become difficult to read, assimilate and process such research production.

2. How to use and benefit from collaborative intelligence found in the Cloud for research?

The second trend is that many people participate in social networks. These platforms provide the potential option of planetary scale connectivity among researchers and the ability to organize research projects solely in the cloud. The availability of high-performing computing resources, such as online cloud computing and storage platforms, grid-enabled platforms and communication channels, provides the context necessary for important innovations in modern science. As a consequence, social networks provide a context for the enormous amount of data. This dramatically expands our combined brain power, because a group of people is more likely to solve a complex problem (Nielsen). Moreover, collaboration becomes independent of our physical location, reducing the transaction costs to zero (Treuille). This new kind of research collaboration has different names such as crowdsourcing or crowdfunding, depending on the type of collaboration. A good example to that effect is the FoldIT and EteRNA games (Cooper, Treuille). You can also check out the Soapbox Science series on crowdfunding here. 

I am very excited to explore what the move into the digital age means for science, helping to figure out potential practical solutions to the challenges above. Born from this drive, I came up with an idea for a web application: The Leukippos Web App. This app is currently in the development stages but I shall outline where we are at now; feedback is always welcomed.

Designing the app

My personal starting point for the application development was systems theory thinking (Fig.1, Ashby, Bertalanffy, Wiener).

Fig 1 The concept of data-driven science 

Fig 1 explainer: A datapool or database is the engine of the data-driven research approach. This uses a filter, an algorithm that determines which subset of data will be chosen from the data pool. The filter process results in a pattern, which is the system we examine in our research. This pattern is subject to a scientific evaluation, for example in experiments, and this pattern evaluation results in new data, which is placed back in the data pool as part of a feedback loop. Constant evaluation of the patterns in social networks dynamically fills the data pool and changes the pattern anatomy. A constant flow of data can be observed, and the process will be very fast, because of the large size of the network.

The next step was to write an application definition statement (ADS). Thus I wrote down:

Leukippos: An iterative application that structures big data and utilizes collaborative intelligence for all people interested in doing in silico synthetic biology research in the cloud. 

After I had worked out the basic concept of the application, I started the design process, thinking about the target audience:

The Leukippos App is designed for everybody interested in doing cutting-edge synthetic biology dry lab research, both professionals and amateurs, and also for people interested in developing this web platform dynamically.

The Leukippos project aims to solve the collaboration code; that is, how to engage people to permanently collaborate outside the traditional safe academic context. The first release will be a scaffold with an easy user interface and the starting point for the iterative development process.

The app will obviously need many rounds of user feedback and this feedback will come from both experts and non-experts. This will allow us to improve and adapt the app until we reach a design allowing for real collaboration. Gamification following the model of Foldit and EteRNA will be one of the first iteration attempts in order to make the app more engaging (Cooper, Treuille).

The Leukippos App will offer a web platform for performing in silico synthetic biology work in the cloud. The limitation to in silico work allows everybody with access to a modern web browser the opportunity to participate. The app has four main user interfaces (see Fig 2).

Fig 2 The main menu of the Leukippos App

• Data: A hierarchical search engine with patterns as output. These patterns can be exported for discussion.
• Social: Pattern/discussion subjects can be selected, and discussed in a group.
• Idea: Submit the research idea you wish to discuss.
• Lab: A wiki platform to create, edit, and feed your group page, mailing list, blog, calendar, RSS, etc. Moreover, a SynBioAppSelector: a page where you will find all software you need.

After finishing the basic design, I soon figured out that this project was far too big for just me! I needed collaborative intelligence. So it is now a free and open software project between people interested in collaborating in the design of in silico synthetic biology softwares to be enabled on the cloud. You can find a complete list of the people involved here.

You can follow the online conversation on Twitter with the #ToolTales hashtag and you can read Mary Mangan’s Tool Tale here, Dr Peter Etchells’s Tool Tale here, Alan Cann’s here, Jerry Sheehan’s here, Boris Adryan’s here, Anthony Salvagno’s here, Daniel Burgarth and Matt Leifer’s here, Zen Faulkes’s here, Jenn Cable’s here , Mike Biocchi’s here, Susanna Speier’s here, Derek Hennen’s here, Musa Akbari’s here, Benedict Noel’s here and Chris Surridge’s here.