Computational biology & Bioinformatics: solution for scientists in low-income countries? 1

Since that I am a psychiatrist, my focus will be on neuroscience and mental illness. Many times me and my colleagues came across a patient suffering from a clinical picture that arisen among us several questions regarding brain circuits and signaling. Then when we think about how can we design some form of study to try to clarify things, we are faced by the problem of the absence of research funding.

Other times, we read about a new study and we become so excited about it. We try to imagine how fascinating it would be to be looking at something that no one before looked at; to enjoy being the first people to look at it. Yet, come again the problem of no funding, and we are back to earth again.

I can imagine also other colleagues in other scientific disciplines facing the same problem, the problem of minimal funding.

Then come the other problem. In low-income countries, we suffer from inadequacy of team work. So, everyone of us becomes alone, and since that he/she cannot solve the problem all alone, we stop. And in the end, the person pay from his/her own money. And since that research is expensive, usually the story ends with the researcher doing the study according to lower standards than he/she wished.
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Computational biology techniques, or the so called "dry biology", need less money (according to my point of view which I hope is shared by many people). You don't need biochemical kits, DNA sequencing facilities, or places to keep the animals. But you need an adequate understanding of your problem at the molecular level, you need mathematical knowledge to some extent, so that you can understand different algorithms designed for a particular problem, and to design a mathematical model if needed. You also need some basic knowledge of computer sciences (and preferably one or more programming languages).

Yes, this looks hard, and it is hard, but it needs less money. As learning sources are out there, mostly for free, learning becomes possible. As the access to different biological databases and different bioinformatics servers is available, some for free, and some others for a sum of money (that can pay well back), the tools to shape our questions and answer them become within the reach of hand.

Also, the use of computational biology techniques in tackling the mysteries of mental illness would require the collaboration between psychiatrists, neurologists, neuroscientists, in addition to mathematicians, and computer scientists. This can start to create a highly creative scientific atmosphere within our country.

Next comes the point of where to get the research question. In psychiatry, we spend a lot of time with our patients. We listen to them talking about their fears, what make them uncomfortable, what disturbs them, the side effects they are suffering with our medications, the lack of support that they feel, or hopefully, the support that they are finding from their families. Listening to them describing their illness and their childhood helps us to make what we usually call the psychodynamic formulation (or actually, the psychodynamic hypothesis of why this patient is suffering that illness in that particular way).

Using the advancements in molecular neuroscience, neuroimaging, and developing animal models of illness, we can start to generate hypotheses regarding the cause (etiology) and mechanism (pathogenesis) of the illness. Usually this is the path taken to reach to a research question. What I think that we, as psychiatrists with low funds for research can do, is to see how computational biology techniques can help the "wet biology" research taking place in other areas of the globe where there is better funding opportunities. This will make our countries share in the global advancement of the understanding of the mechanism of mental illness.

"Dry biology" techniques are complementary to the wet biology. The hypothesis can be generated from findings resulting from wet biology, and then checked and elaborated using the dry techniques (eg, the effect of a mutation on the binding affinity between two proteins). Things can also go the other way round. Hypothesis can be generated from results obtained from computational biology work, then tested using wet biology techniques (eg, identifying new molecular targets for medications).

An article that I think is worth reading for scientists in low income countries is "Ten Simple Rules for Aspiring Scientists in a Low-Income Country" by Edgardo Moreno and José-María Gutiérrez, in PLoS Computational Biology.

This is a rough scheme for how I see computational biology can help psychiatrists understand more about mental illness.

To be continued..... :)