Ah… there is a (geeky) youtube video of a population migrating:

(For details, check the link above)

Lets be honest, computer scientists are trained to work for banks and insurance companies, to make web sites, software for cars and things like that. Those domains are actually very simple. A bank might be a gigantic institution, but it is possible to capture, granted with a lot a effort, all its processes inside a computer program. This creates a mental setting: Everything that we need to know is possible to be known: we just decide when to stop.

Now think about those simplistic (this is an understatement) mathematical and computational models for scientific problems (differential equations, Monte Carlo processes, Markov Chains, …). They model the “important parts” of the issue under study. These models are much simpler than the models working in computers to sustain day to day banking chores. Somehow it strikes me as strange that something as mechanic as a bank needs a more complex model than “nature”.

In the context of nature and making mathematical and computational models about it, I have a few things in mind:

First of all, in many problems in the natural world we don’t know what are the important parts to start with. This is very different from the “bank mentality” when you can know everything if you try hard. In my personal case, when I model malarial artesunate resistance, I am modeling something that people speculate how it works, and even if the speculation is correct most of the fundamental parameters are unknown. I am still to read a paper modeling something related to malarial drug use that doesn’t have a phrase like: “the relation between this value is and reality is assumed to be this (no citation – or citing something unpublished – or rationale provided)”.

But the cornerstone of my reasoning is that, in complex processes, the devil is in the details and in the interactions between participating factors (most of which we
are unaware of). Soft sciences are holistic by nature. The property of the whole system comes from the everything and everywhere. The “banking” and “hard science” mentality are no good here, we cannot know everything, what we know is probably not enough, and most simplifications will lose something fundamental.

Does this means that I am suggesting that we should stop modeling and all theoretical work? By no means, but we should refocus:

• This is not hard science, don’t try to mask it as such. Hard rules, sensitivity analysis are mostly artifacts to make things look more “serious” and more “demonstrated”. This is biology (or even “worse”, economy or sociology), you don’t c.q.d. here.
• Think you can forecast the future? You think you can… thaen bring me a always correct forecast of the weather in 2 months and I will listen to you. Most models that exist to forecast the future are there because they are very hard to disprove TODAY: climate (as opposed to weather) models, epidemiology, … . The vast majority of models that can be tested fail (think mathematical finance and the current subprime crisis in the USA, think weather predictions…).
• Theoretical work, although not being able predict the future (or explain the past) might help create a cognitive and linguistic framework for discussion: present the fundamental concepts and narratives underlying the research process, make the discourse clearer, less cloudy, point dangerous imprecisions. This is actually the inverse that what happens now: theoreticians speak in a language that most people struggle to understand.
• Theoretical work can create interesting questions for field scientists to try to answer: It is the precise inversion of what happens now: We don’t want models that are cheated to look realistic. We want reasonable models that fail miserably so that we can ask field scientists: This is failing, why do you think this happens? Have you considered this other hypothesis? What about testing it?

<sarcasm>
The existing modeling culture is quite good in the current scientific setting: Makes theoreticians look intelligent with all those complicated mathematics and computer programs (and associated publications) and excuses “practical” scientists of even trying to use their brains: They just apply the existing theory in a process that is more industrial then creative to their research questions. The biggest example that I know of this is phylogenetic analysis: Get data from the field, compute a mutation model from the premise that a small genetic distance is better, burn CPU cycles, publish – You don’t even need a human for this – a trained monkey is probably enough.
</sarcasm>

In economics things are a bit worse: elaborate game theories and such are presented as a “hard, undisputed” justification for an economic theory serving some nice agenda. Nothing more than a authoritarian argument.

PS – If you work in an hard science like physics or chemistry you might be thinking that I am smoking something very strong. I don’t think that this post applies to hard sciences, that is a different game altogether.

You can only fight the environment with the tools the genome has given you.

In a very indirect way, yes, but well… no. I don’t want to take this to those interesting discussions on nature versus nurture: I think it would be easy to make the case for social interactions (both for us and other species), drugs (which might suppress some genome changes – think cancer), etc tweaking our genome… A stronger case could be put forward by reminding that we will be able to change our genome in even more radical ways than getting rid of cancer cells. In the future we will be able to create genomes (or am I seeing too much sci-fi?). Of course it could be counter-argued that at the end this is the product of our genome (in the sense that this is all products of our “genomic-based” intelligence) to start with, but I think that would be an argument of a more rhetorical than practical form.

This reminds me of that idea (and getting to the point that concerns me the most) that “You can only do what physics allows you to do” (the comparison is not perfect as the “physics case” is easier to defend). Yes, you can only do what is physically possible, but does that matter in practice? When you want to describe, say genetic based problems, you could resort only to reductionist physics arguments, but that would make the whole description and study of genetics’ problems cumbersome and very very verbose. To put it a CS way of expressing things: The level of abstraction is not enough. The same goes (in fact the whole idea that we can only use the tools that the genome gave us is more debatable, while the idea that we are all physics is much less so) to the “genome”. Yes, we might be all genome definable and maybe we cannot escape that but I think it would be a fallacy to try to see all things from the “eye of the genome”.

Paulo might complain that I am misinterpreting his words (I doubt we wants to see the world only through the “eye of the genome”), for that I apologize. But I was feeling the need to complain against “reductionist” approaches and so I used him as a scapegoat .

If you look at simpler living beings (I am currently looking myself at p. falciparum, the cause of the most mortal form of malaria) they tend to genetically evolve very rapidly as genetics is their fundamental way to adapt to new environments. Not only their numbers are so big that mutations can appear in the population more frequently, but also, it is one of the few ways they have to adapt. And of course, their environment might change rather rapidly in very stressful ways (in my study case, with Man bringing in drugs to which p. falciparum seems to adapt very fast… too fast).

More complex beings have other ways to adapt, the most extreme example that I can think of is ourselves (we adapt culturally, linguistically, …) but other “intermediate” forms also have more “degrees of freedom” than just genetics (like a body that can bend, a muscle that can grow larger, …).

Also, in complex beings different parts of the genome are subjected to very different adaptive stresses (think MHC against brain related genes).

We are at an evolutive step (not only biological, but also scientifically and culturally) that genetic evolution (through mutation) is normally a dreadful thing (think cancer).

Therefore I would speculate that evolution tends to breed genetic stability.

As a side note, from this philosophical point I think it is quite easy to guess what I think about things like “molecular clocks” or, in fact, the current underlying assumptions on which phylogenetics is based.