The current longest non-stop flight in the world is the Qantas route from Sydney to Dallas: 14.5 hours in the air in an Airbus A-380. A couple of weeks ago I was sitting on that Airbus flipping through the inflight entertainment system in that semi-catatonic state that all long-haul flights seem to induce, when I stumbled across an intriguingly-titled television series: The Secret River. It turned out to be a two-part mini-series originally broadcast by the Australian Broadcasting Corporation based on a novel by author Kate Grenville. The blurb promised an exploration of an emancipated convict in the early days of the colony of New South Wales, carving out a new life on the Hawkesbury River (the Secret River of the title).
I thought to myself, this seem promising, and settled back expecting a mildly diverting period piece about early Australian history that I had never seen dramatized. I imagined it might be a little dry and slow, but would have great images of the bushland that I was familiar with growing up (the Hawkesbury is just a 20-30 minute drive away from where I grew up), I was interested to see how the producers recreated the early Australian colony, and at the very least it would while away about 3 of the remaining hours until touchdown in Dallas. Instead I found myself watching a graphic and unsentimental depiction of the often brutal confrontation between the early European settlers and the indigenous people, the Australian Aborigines.Read More »
In this chapter we show you how to extract and examine data generated from cellular interaction networks, sometimes affectionately known as “hairball” data. In particular, we’ll show you an example of reading in data on transcription factor networks from yeast. We will take you through the steps of reading in files; creation of set data structures and simple queries. To give you a flavour, here’s a brief extract from the Chapter (the full sample chapter is available as a PDF download):
A set, as you might recall from distantly remembered introductory maths classes, contains only unique members. In the context of the data structures for transcription networks, this means for each transcription factor, we only need…
In the original Jurassic Park movie, Jeff Goldblum’s character, mathematician Ian Malcolm, became known for the phrase: “life finds a way“, referring to the chaotic behaviour of even the most simple systems. Indeed, the entire movie becomes an exercise in unpacking the meaning of that statement when applied to reviving an entire ecosystem of dinosaurs. The increasing resistance of pathogens to antibiotics over the last few decades, could be considered a real-world case of evolution finding a way. It is also a good example of why evolution isn’t just about events of the dim, distant past, but very much the here and now. I’m often puzzled that the word “evolution” isn’t used more often when describing this significant threat to global public health: a quick Google search shows approximately 11,000 mentions of the phrase “developing antibiotic resistance“, compared with only about 1,800 for “evolving…
My Amber Biology colleague, Gordon Webster, and I are working on an accessible introduction for biologists interested in getting into programming. Python for the Life Scientists will cover an array of topics to introduce Python and also serve as inspiration for your own research projects.
But we’d also like to hear from you.
What are the life science research problems that you would tackle computationally, if you were able to use code?
You can contact us here in the comments, on firstname.lastname@example.org or on the more detailed post:
Building computational models in any discipline has many challenges starting at inclusion (what goes in, what’s left out), through to representation (are we keeping track of aggregate numbers, or actual individuals), implementation (efficiency, cost) and finally verification and validation (is it correct?). Creating entire modeling softwareplatforms intended for end-user scientists within a discipline brings an entirely new level of challenge. Cognitive issues of representation within the modeling platform – always present when trying to communicate the content of a model to others – become one of the most central challenges. To create modeling platforms that, say, a biologist might want to use, requires paying close attention to the idioms and metaphors used at the most granular level of biology: at the whiteboard, the bench, or even in the field.
Constructing such software with appropriate metaphors, visual or otherwise, requires close collaboration with working scientists at every…
The promise of the Internet as a means to “level the playing field’ has seriously gone off the rails. A two-day conference at The New School that just wound up this last weekend, explored the emergence of platform cooperativism. Platform cooperativism aims to return the democratic promise of the Internet away from the rapacious, heavily-leveraged extractive models of the so-called “sharing economy” such as Uber and AirBnB, and towards models of true user ownership and governance. As pointed out in a set of 5 summary essays that appeared in The Nation, these are not (mainly) technical challenges but legal and political ones. An example is FairCoop:
FairCoop is one among a whole slew of new projects attempting to create a more democratic Internet, one that serves as a global commons. These projects include user-owned cooperatives, “open value” companies structured like a wiki, and forms of community-based financing. Part of what distinguishes them from mainstream tech culture is the determination to put real control and ownership in the hands of the users. When you do that, the platform becomes what it always should have been: a tool for those who use it, not a means of exploiting them.
Many of these efforts will face an uphill battle, and as pointed out by Astra Taylor at the conference (she follows Douglas Rushkoff’s presentation in the video link), will probably be fiercely resisted by the newly entrenched platforms of Google, Facebook and the like. But we can also say the same thing about those platforms many of which were just small upstarts back in the 1990s. The real challenge is one that is familiar to evolutionary biologists in game theory: building systems that reduce the chance of “invaders” or “cheaters” (in this case, rapacious VC firms and super-capitalism in general) from swamping a population of mutually beneficial co-operators (or turning those cooperators into cheaters). It doesn’t have to be, and could never be, perfect: you’ll never reduce the population of cheaters to zero, but at least keep them from taking over your population completely.
Read more about platform cooperativism at The Nation…
On-demand computing, often known as “cloud computing” provides access to the computing power of a large data center without having to maintain an in-house high performance computing (HPC) cluster, with attendent management and maintenance costs. As even the most casual observers of the tech world will know, cloud computing is growing in any many sectors of the economy, including scientific research. Cheap “computing as a utility” has the potential to bring many large-scale analyses within reach of smaller organizations that may lack the means or infrastructure to run a traditional HPC. These organizations or individuals could include smaller clinics, hospitals, colleges, non-profit organizations and even individual independent researchers or groups of researchers. But beyond the industry enthusiasm, how much can cloud computing really help enable low-cost scientific analyses?