How little do we know about Biology and Neuroscience

How little do we know about Biology and Neuroscience

I spent the Christmas Holidays in Naples with my parents. A few days after the traditional family meetings were over, I organized a lunch with friends (and friends of friends) with backgrounds in Engineering, Computer Science and Complex Systems. Some of them are teaching in Italy and the US, others have start-up companies. This kind of lunch is good to exchange ideas and get up to date with what they have been doing.

Among other things, we discussed about biology and complex systems, and the lack of understanding we have about how biology and complex systems should be modeled. We smiled about Engineers trying to model cells (or what happens inside a cell) with differential equations, just because they know how to use them!

All this reminds me of a discussion I had with Tony Bell, about two years ago in Sardinia at a Neuromorphic Engineering Workshop organized by ETH Zurich. I found a lecture, recorded a few days ago in Berkeley, where Tony tries to convey a sense of how little do we know about Biology, Neuroscience and what we call complex systems. I agree that we do not have the mathematical tools to model Biology yet, and that the traditional horizontal approach is doomed to fail.

Kudos to Charlie Stross who summarizes all this nicely in his year ending post:

There's been enormous progress in genomics; we're now on the threshold of truly understanding how little we understand. While the anticipated firehose of genome-based treatments hasn't materialized, we now know why it hasn't materialized, and it's possible to start filling in the gaps in the map. Turns out that sequencing the human genome was merely the start. (It's not a blueprint; it's not even an algorithm for generating a human being. Rather, it's like a snapshot of the static data structures embedded in an executing process. Debug that.)

To the above I would add that the static data structure embedded in the executing process could be a piece of cake compared to the complexity of biology, which is not based on the Turing machine abstraction.