I had the very great luck to study control systems as an undergraduate with James Collins, one of the people responsible for the first big "genetic toggle switch" results in 2000 : https://www.nature.com/articles/35002131

The authors of this piece note:

Yet despite our progress so far, genetic circuit design has often been characterized by a manual and failure-prone process. Engineers often spend years creating a functional design through trial-and-error.

This is still absolutely true.

Part of the challenge of the synbio field is to do better than this while still being on the sane side of the "andy grove fallacy", which is loosely stated as "hey biologists, transistors are easy now; what's the holdup?" . The holdup, of course, is that 4.5 billion years of monkeypatching makes for a really hard slog.

This sounds like what Tom Knight and friends have been getting up to for a couple of decades now:

http://www.wired.co.uk/article/at-home-with-the-dna-hackers

As a cell biologist turned programmer, i am certainly interested to see if anyone can make something really work in this domain.

But also a bit frustrated that people are focusing on genetic circuits, which are slow and boring compared to protein-based signalling networks!

There is an "International Genetically Engineered Machine Competition" called iGem[1] that I think you'd find interesting if you like this article.

[1] http://igem.org/

After listening to one of the world's top organic chemists, one realises that humanity is still in the stone age when it comes to industrial and technological systems when we look at what is going on inside a single biological cell.

This includes chemical transport, energy transfer, system construction, chemical processing and purification, adverse chemical reduction and removal, etc.

The more I look at the information obtained about biological cells and their operation the more it becomes obvious that certain popular models related to biological systems are in the category of "the emperor has no clothes" ideas.

Biological systems are so complex and so integrated and so coordinated that we still have so little actual knowledge on how and why they work the way they do. So duplicating the various industrial processes that occur within a cell with any finesse is still a long, long way off.

Highly relevant little piece from several years ago here: https://bml.bioe.uic.edu/BML/Stuff/Stuff_files/biologist%20f...

I think about this sort of thing a lot, especially in regards to software. What is one's level of understanding of a system? Of the general architectural principles of similar systems in general? In software we still have incredibly crude tools to describe architecture which typically gloss over almost all relevant details. Between the raw source code and your typical multi-layer diagram there is a gulf of understanding and missed opportunity. Biology has an even worse problem, with even more complicated machinery that is even more difficult to understand (you can't simply step through code in a debugger to see what it's doing, and the "code" was evolved so you don't even really know what the operations are (such as gene expression and suppression)).

You can model gene regulatory networks at a high level with recurrent neural networks. It's interesting to think that nature re-invented the same computational principles with different implementation details.

Relevant SHA2017 talk - DNA: The Code of Life: https://www.youtube.com/watch?v=EcGM_cNzQmE