[DavidPBL]

The day to day in lab really depends on the type of lab you are working in.

If you are working in analytics, running assays (tests) on things like blood or urine samples (hospitals or clinical trials) or a more recent example would be a covid clinic, the day can be very monotonous. There is a lot of paperwork involved due to the regulations you need to adhere to (GMP, GCP, GLP). This is one of the reasons I didn't like working in pharma. It's better now to things becoming digital, but the point is the work can be very repetitive.

If you are working in an R&D lab things are more dynamic. You might be running similar experiments from one day to the next, but the context is always different. Even though you hit a roadblock and get stuck for a day, a week or a month, as things progress the type of work will change as the project evolves/progresses.

You can work in industry in either of the above environments, both provide valuable experience. Industry is stricter and more rigid than academic labs.

Day to day it's still very hands on. Things are progressing such that you spend less and less time in the lab as things become automated and the workflow becomes digitised, but you still need to go into the lab even if it is to setup the robot. We don't yet have robots to control the robots, although maybe sooner than we think. At high level, most R&D lab employ some sort of design, build, test, learn (DBTL) workflow, even if they don't call it that. Depending on what the focus is, each step in that cycle will be slightly different.

The amount of software is growing every day for all applications. You have everything from basic software like Lab Information Management Systems (LIMS) to help with basic ops to more complex software to help plan workflows and analyse data (Synthace) to much more specific software like protein modelling (Rosetta) or genetic manipulation (Geneious) and the list goes on. I am barely scratching the surface here. I regret not having more training in python.

edit: not a perfect article, but to give you more of a flavor for software in synbio/biotech, check this out: https://www.builtwithbiology.com/read/the-synbio-stack-part-...

[tptacek]

This is the industry at large (and thank you so much for that!) but I'm actually really curious about what the work at Phase looks like in particular; I assume it's mostly not running assays on urine samples. :)

[DavidPBL]

My bad! We are engineering our microorganisms which means we are assembling DNA parts into plasmids which are used to deliver the DNA into the host. So there is the design and then the actual build part before the actual testing.

Depending on what you want to do you build a different style of plasmid. If its genetic modification (ex. using CRISPR) you use one type, if it's testing a new pathway, you build another. You use software to help with the design of everything and to define and explore the solution space.

To make it high throughput we usually test things using in vitro (cell-free systems) before actually moving into the host. In vitro work has a faster DBTL cycle than in vivo work. We test strains in smaller experiments (20-100 ml) before moving to bioreactors (1-2L).

We would like to automate more and build a more robust R&D pipeline to support faster DBTL cycles, but you can be limited by the epuipment available. Doing highthroughput automated work is great for productivity, but it costs more. So has been challenging to implement everywhere we would like due to resources.

[tptacek]

Thanks! This is great stuff. Best of luck to all of you!