Formal Systems in Biology

This stuff got me thinking, if RegExp comes from graphs, why not use it to process graph databases. This paper is about a year old https://arxiv.org/pdf/1904.11653.pdf Can anyone point to open source implementations?

> bioinformatics, systems biology, computational biology

Of those bioinformatics is more specific (usually genomics data); the other two are overlapping and pretty non-specific terms.

For example I started a Sys Bio PhD and ended up in a Comp Bio research group. A friend started the same way but ended up in control theory/microbiology.

The title and even the department are somewhat arbitrary and more to do with the organisation at the university than anything else (e.g. I was in CS but my friend was Engineering I think).

If you can find a good interdisciplinary course they will be familiar with people moving around depending on their interests.

A lot of the stuff in the repo is pretty marginal, from the point of view of mainstream molecular/cell/developmental biology, so i don't think there is a reliable systematic way to find it.

In particular, that repo collects what are basically discrete maths approaches to biology: representing living things as systems of symbols rather than differential equations. I have always found that approach intuitively appealing - something about biological robustness meaning you have an opportunity to ignore a load of quantitative details and focus on the underlying structure. But in twenty-five years of being vaguely interested in it, i have never seen a really productive application of that approach, outside of treating DNA as a string of symbols.

Still, perhaps 'marginal' is just another way of saying 'cutting-edge'. I think it's most likely to show up in elite research institutes where people can do slightly out-there stuff, or in explicitly cross-disciplinary institutes or programmes.

The specific terms you mention have different meanings to me:

bioinformatics - treating DNA, RNA, and protein sequences as text and applying computation to them, eg searching, phylogeny, structure and function prediction

computational biology - various approaches to simulating cells and tissues, usually involving numerically evaluating differential equations at some level, eg how morphogens cause tissue patterning

computational biophysics - computational chemistry but for large biomolecules, eg simulating how proteins work

systems biology - smoke and mirrors used to obtain grants

But biologists aren't really into rigorous definitions and fixed boundaries, so you might find interesting stuff within any of these.

Lots of statistic. Especially anything to do with clustering, and how to analyze and compare gene and protein expression networks from various species, or tissues. Those are particularly important in this COVID-19 era.

In term of general network analysis and visualization, I think logic, symbolism, Petri net, are underutilized. Probably because they require more CS and Math type of trained people to work on those than your typical biologist becoming bioinformatician learned to handle.

I recommend looking at Luca Cardelli's work [1]. He's probably the leading researcher in the intersection of CS and biology, in the sense of modelling biological phenomenon as formal systems. Some of his lectures are online [2]. He's extremely approachable in my experience, so don't hesitate to contact him if you want to educate yourself more in this field.

[1] http://lucacardelli.name/

[2] https://www.youtube.com/watch?v=o8q7kFeGUTM

The lines between all of those fields is super blurry. Blurry lines between human-defined and ultimately somewhat meaningless categories is intrinsic to the field I'm afraid. The syllabus for each course with a listing of the actual topics you study is going to be far more helpful in evaluating what you want to study. The broadest lines I can draw (with the caveat that these are really fuzzy bounds) is that bioinformatics tends to be focused on drawing insights out of big data (lots of the "omics" - genomics,epigenomics, proteomics, microbiomics), while systems biology tends to be about modeling complex systems this work usually involves a lot of bioinformatics. And computational bio is an umbrella term for everything, though sometimes the umbrella is weighted more to bioinformatics than systems stuff.

Might make sense to ak yourself what kind of work you want to do first. As in, do you want to have a few terabytes of data dumped in front of you that you run analytics on to find correlations? Do you want to study interactions between networks in biology and tease apart the bug picture? Or do you want to narrow in on individual protein domains and use neural nets to simulate protein folding to characterize and engineer individual sequences of amino acids that are important to understand? All of those involve computation amd involve bio, and it'll probably be easier to decide on courses/programs after you have something like that in mind and can evaluate the syllabi directly

Where do you live where there is a citizen bio lab? I'm considering a move in the near future and would be interested in having a place like that available to me.

Haha, great aptronym. :)

"Flexible Muscle-Based Locomotion for Bipedal Creatures": https://www.goatstream.com/research/papers/SA2013/

Emergent Tool Use from Multi-Agent Interaction[1]. Also these agents lay a claim to being the most god damned cutest things ever.

[1]https://openai.com/blog/emergent-tool-use/

Colleagues in my research group are working on evolving 'creatures' in physical space, e.g. evolvable robots [0]. They address deep questions regarding the interplay between evolution/the body, learning/the brain and the environment using evolutionary algorithms and machine learning.

[0] https://www.york.ac.uk/robot-lab/are/

I LOVE the first talk, I think I discovered it on HN. I study bioinformatics and had no idea something like that existed.

To limit complexity in complex systems design, you need to be able to create agents which perform simple functions based on well-defined inputs. You can have a few different types of those agents interacting, and each should be discrete and be able to "survive" in an adequate "environment". Then the system, if designed correctly, can become much greater than the sum of its parts but you retain the relative simplicity to monkey around with the internals of the agents as well as the reservoirs to which they're attached, etc. Nature has seemed to become a system where iterative improvement is performed by virtue of the finite life cycle and sexual reproduction (including all the ways that DNA shuttles around the necessary source code).

hey Steve,

What was your thesis about?

Thanks for the note! I have added this to research section.

it should be Stanisław Ulam if we want to be hyper precise :)