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Comment by astazangasta | Hacker News Reader

As a cancer biologist I feel that the reliance on biomarkers as indicators of biology is a poor way to do science. Biology is about complex, interacting networks; models that attempt to reduce it to a few controlling variables is almost always wrong (as in, the model is insufficiently predictive), and moreover misses the point: we need to understand biology in all of its rich interactions, not pare it down to the math we can handle. Biology is not physics; two or three factor models won't cut it.

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Genuinely curious, couldn't we work with smaller models and adapt/evolve them as we learn more? A bit like physics trying to find the unified theory?

The problem is one of going from a system of two or three variables to one of two or three thousand; this cannot happen via an evolutionary progression, it requires wholly new kinds of thinking (and math).

Which are the main candidates for new paradigms in systemic biology ?

I'm not sure they exist yet; we're still at the stage where we are trying to appreciate the complexity of the systems we are studying. The first step is to catalog. For example we've only recently begun to appreciate the extent of genetic heterogeneity in cancers (and the therapeutic significance of this variation). Now that we have this sense people are beginning to think about new experimental methods for collecting this kind of information; we are still a few years (maybe a decade) out from good models of how to analyze it.

I can't naively hope that very high performance (exascale) computing helps finding the right theories here. But I do hope that people don't waste time digging in the wrong direction. Facing complexity is better than not being aware of it.

ps: I hope though, in the case of non mental disorders, that finer and cheaper technology allow for preemptive discovery, continuous monitoring and non invasive therapies.

Computing will help. But we also need new fundamental technologies that get us information that old methods from molecular biology miss. DNA sequencing isn't everything--we need to be able to look at ALL of the biomolecules within an organism and their dynamics.

I know. I was talking to someone in the genetic field and he agreed that the legacy view on genetics is out of touch. So far mainstream media still report this kind of sequencing wows but I hope research is aiming at a deeper yet leaner model.

The mathematician John Baez is exploring the mathematics of complex systems with his students. Here's a place to start reading[1] but a caveat: it would take a lot of patience and link-following to grok this research from scratch.

[1] https://golem.ph.utexas.edu/category/2015/04/categories_in_c...

Much appreciated.

My suspicion is Machine Learning (IMHO)

You can feed a lot of data then try to derive (maybe human readable) results

The problem as always with machine learning methods is figuring out how to construct your machine correctly and what data to feed it. Mostly this is about good data collection, not about what machine you use. I am somewhat agnostic on whether you train using a linear model, SVM, neural network, etc. - all of these are likely to be analytically useful, provided I can give them an interesting and well-contsructed input set. But if the data I'm feeding into such a system is too noisy, doesn't sample deeply enough to catalog the extent of variation, or we don't have good descriptors for correctly labeling the dataset, it's useless to try to train a machine, we'll just have GIGO.

It is physics to a degree. A doctor is called a physician, e.g. All Biology should be within a frame of physics. The methods might differ, but mathematicians are also concerned to not pare it down to the maths we can handle.

What would you say is the optimal path for research in psychiatry/psychology?

Probably not the best person to ask. I have a limited understanding of psychiatry but still tend to take a dim view of it. I think the brain is substantially more complicated than psychiatric theory suggests, and so I think that the modern science is far away from useful and accurate models. fMRI, for example, is a very gross way of examining brain function and seems to me rather like trying to read source code by examining the number of bits of entropy in a file.

I think that psychology is more useful for understanding the human mind, but even here I wish they would eschew the ersatz methods of science (stats, p-values, controlled studies) and go back to more qualitative descriptions of the psyche; we're not even close to successfully bridging between psychology and biology.

But this is all a lay understanding of the field and probably no better an insight than yours.

It might be nice if they actually started doing some research. Psychology research takes all of the issues that harder sciences have (e.g. most experiments are never reproduced), and then combines that with extremely low-power experiments, and then mixes in the fact that most research must be on humans, which means your experiment won't pass the ethical review board.

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