Skip to main content


In Other Words: Synthetic Biology + Data Infrastructure + Machine Learning = ?

Recent posts

The State of Synthetic Biology

Last month an interesting article got posted at CBinsights about the current state of synthetic biology that I didn't have time to write about until now. What I really like:

Even the superficial overview teaser article is much more comprehensive than my review of companies in the field which actually had a slightly different focus.It is also more up-to-date.

Reading the article, I am very excited because there are so many interesting companies to be excited about like for example:

Benchling - a company that builds dna managing, and editing software.
GAE Enzymes - a company that wants to make enzymes more rapidly
Glowee -  a company that wants to make biological lighting
Gincko Bioworks -  a microbe design company
Twist - a DNA synthesis company

One interesting thing was who was not mentioned in the public article: Amyris. I guess this has to do with the fact that Amyris is no longer new and "exciting", but a known quantity. I think this is positive because it means that…

Sustainable Living: One man's trash...

Since Earth Week is starting tomorrow, I wanted share with you some concrete ways of how individuals like you and me can make an impact on a wider scale. I then also wanted to use this example to challenge everyone to think creatively about the larger context.

So you know how the saying goes: "One man's trash is another one's treasure." Today, I want to talk to you about garbage. Plastic garbage specifically. Plastic is quite a wondrous material. Made from oil by man with just a few additives can turn this polymer into so many different sorts of plastics with so many different properties from thin and flimsy plastic bags, to the carpet on which I am standing, to this plastic bottle from which I am drinking.

Freely-Speaking: On the need to act with urgency.

I just read this article on the Great Barrier Reef suffering irreversible damage from climate disruption. It moved me so much that I just had to quickly post an appeal to anyone who happened to be reading this blog:

The changes happening to our environment are real, massive, and definitely caused in very large parts by human action (e.g. burning of fossil fuels for transportation, and energy, deforestation etc.) and made worse by inaction (e.g.: governments twiddling their thumbs and ignoring the problem, or afraid of shaking up the status quo).

There is some good news to all of this too though: Since it is humans causing this problem, it is also up to us to do everything in our power to fix these problems. And since Earth Week is also coming up, I would like to appeal to everyone to move to action.

Freely Speaking: Programming Biology - Eric Clavins

Today's post refers to an inspiring talk by Eric Calvins who talks about programming the biology lab to program biological entities. I'll leave it to the reader to see what the implications of this vision are.

Freely Speaking: What does SciPy have to do with bio-based ideas??!

I was recently asked the above question. And it's a totally valid question as SciPy is somewhat outside of what I usually write about (biological topics, sustainable topics). There is a logical connection though, and it has to do with what I do at work.

Building biological entities is difficult because unlike car design, biological entities like to "misbehave". I say misbehave but it actually only means that we don't sufficiently understand microorganisms well enough to model them perfectly.

This is where data science comes in of course. Through collection of large data sets data science and related fields can help us uncover patterns not seen before. these patterns then help make a better yeast model. Better models = faster product development. Faster product development = faster route to a sustainable business = more products with a positive impact.

So there is a link. Simple, right?

Recap: SciPy 2015 - Synopsis

SciPy 2015 has come and gone. If I step back, what are some of the learning lessons?

There were certain themes that recurred from talk to talk:

Speed. One of the perceived limitations of Python seems to be speed of execution which is important to process very large datasets. Many talks dealt with this topic in various ways. Some of the approaches included enabling process parallelization (Dask, DistArray), GPU-acceleration (VisPy), or acceleration via some means of compilation - sometimes just-in-times (Numba). With these tools, Python is no longer slow. It's impressive that the combination of these approaches has enabled data scientists to process 60+ GB data sets as if they would be loaded into memory on one small laptop that actually only has 8-16 GB of memory.Visualization was a theme. So many talks dealt with making complex data sets visible, and they did so to address different issues: Serialization to enable interactivity (Bokeh, matplotlib), visualizing large dynamic datase…