Life scientists: what are you looking to code?

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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 info@amberbiology.com  or on the more detailed post:

“Are you still using calculators and spreadsheets for research projects that would be much better tackled with computer code?” on the Digital Biologist.

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Connecting the cognitive dots in systems biology modeling

Biosystems Analytics

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…

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Resisting cheaters will be the big challenge for “platform cooperativism”

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

Quantifying cost-effectiveness of scientific cloud computing in genomics and beyond

Biosystems Analytics

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?

There is now a veritable smorgasbord of offerings from many different vendors, but the big players are Amazon…

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What innovation isn’t

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Innovation.  It’s as American as apple pie.  From the US President on down, everybody is talking about innovation.  From university presidents and corporate leaders to Silicon Valley tycoons, all agree that we need more of it.  Airport bookstores have walls of books on innovation: a quick search on Amazon resulted in 70,140 titles containing the word “innovation”, 711 of which were published in the last 90 days alone.  Many of them are little more than generic business advice books with the word “innovation” shoehorned into the title, including gems such as Creating Innovation Leaders (earning bonus points for including buzzwords “leadership” and “creativity”).  So it was with some trepidation that I recently picked up Scott Berkun’s The Myths of Innovation – first published in 2007 – and found it had a refreshing and unpretentious take on the subject.  Since it has become such an overused buzzword, Berkun argues that…

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