Nurturing the scientific ecosystem: new preprint

Biosystems Analytics

The debate about the future of the independent scientific career has become arid and sterile, focusing almost entirely on accessing tenure-track jobs in universities (often collectively referred to as the academic “pipeline”). An unstated assumption of much of the discussion is that “early career” scientists who wish to become “independent” must either adapt to this rigid pipeline or “leave science” (or move to yet another career “pipeline”) and that a permanent position in the academic hierarchy should be the ultimate goal.  It is also taken almost as axiom that all changes must be driven by senior leaders in a top-down manner within existing scientific institutions. This seems unlikely given that the status quo disproportionately benefits those in senior positions, as well as extremely slow, given the glacial pace of institutional change.

We need a vastly enlarged conception of the future of science. Myself and Ronin Institute colleagues, Anne Thessen and Arika…

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A spring surprise: computational analysis unearths potential prions in plants

Biosystems Analytics

With warm weather finally kicking in in New England, it was nice to get a Spring surprise in the form of a new paper on prions seeing the light of day.  Prions are particular kind of protein that propagate by imbuing an altered shape, or “confirmation”, on other proteins of the same type.  They essentially act as a kind of protein “zombie”, and there has been some speculation that they may support a kind of “memory function” due to their ability to transmit state across generations.  Prions were first discovered in the context of diseases like scrapie and variant Creutzfeldt–Jakob Disease (vCJD) but have shown up in all kinds of unexpected places, such as yeast and possibly – providing the aforementioned Spring suprise – plants.  Scientific American has a nice blog post on a recently-published study  from the Whitehead Institute, authored by Sohini Chakrabortee, Can Kayatekin, Greg…

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“Adventures in Transcription Factor Networks”: preview chapter from our book

Biosystems Analytics

As I blogged about previously my Amber Biology colleague, Gordon Webster and I are writing a book Python For The Life Sciences, and today we are releasing a sample chapter.

amazing-adventures 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…

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Evolution finds a way: resistance to antibiotic of last resort

Biosystems Analytics

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…

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Healthcare technology is not software

Biosystems Analytics

Healthcare technology, and the biotechnology world in general, doesn’t quite work the same way as software.  First, it has to deal with the messy analog world of biology: from proteins and cells to tissues and organs, always a complicated proposition at the best of times.  Second: the stakes are higher: the costs of getting it wrong go beyond a badly designed user interface, or an app that keeps crashing, they can be matters of life and death.  My Amber Biology colleague, Gordon Webster, has a good summary over on the Digital Biologist, of the recent issues at the troubled blood test company, Theranos.

It seems to me that at least of some of the hype that can arise in both software and healthcare sectors is a result of the way investors, the media and even peers unwittingly buy into certain “myths of innovation”, a subject I recently blogged about. …

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