Algae have a very different set of living conditions compared to other organisms that we have tested in the Pioreactor. Algae require both light and CO₂ to grow. We had the foresight to think of algae's unique growing requirements during development, hence the Pioreactor has additional pockets for LEDs in the main body. When these pockets are occupied by white-light LEDs, the Pioreactor turns from a bioreactor into a photo-bioreactor.
We just released a new plugin that may be useful for teams that use Slack. The plugin, Logs2Slack, will publish logs from the Pioreactor to a chosen Slack channel, so you and your team can discuss important events in Slack. Installation is quite easy, too!
Optics again! This time we are discussing LEDs performance in different environments, and a major optics change to the Pioreactor.
The role of stirring in the Pioreactor is important for a few reasons: i) it allows for modest gas exchange between the media and the air, ii) more importantly: it creates a homogenous environment for microbes and nutrients alike. This last point also means that our optical signals won't vary spatially - something that would make the whole system much more complicated.
The past few weeks I've been thinking a lot about optics. Too much. It's given me a headache. But we've characterized some really important details about how the Pioreactor works. Let's first talk about how microbial cells interact with light.
By design, there is no Pioreactor "cloud". That is, all your collected data, the web interface, and computations are hosted locally on the Raspberry Pis. This has a number of benefits for you, the user:
- no additional monthly fees that are associated with a cloud model
- no latency issues
- if we, the company called Pioreactor, disappear, you can still use the Pioreactor (additional bonus: our software is open source so you can continue to develop it)
One of our goals with the Pioreactor is design it such that you don't need to be a biologist, or an electrical engineer, or relevant for this article: a statistician. This article describes our internal algorithm that computes the culture's growth rate, but importantly: you don't need to know this algorithm to use the Pioreactor! We've designed the internal statistical algorithm to be robust enough that you can sit back and watch. This article is for the users who really want to dig deep into how we compute growth rates and the statistics behind it.
This week we investigated some sampling tricks to improve our ADCs, and managed to get a 50% reduction in signal variance, and gain many more bits in our signal, using data science!
This week we explored key-value databases for storing data, and implemented a solution that is designed to get wiped often. At the end of the day, we got some significant performance improvements!
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