Driving Forces of Evolution

What is the driving force of evolution??
What makes one species evolve into another??
Why does evolution occur??
Is it natural selection? or is it perhaps mutation?

The points below are what I understood after a bit of reading and thinking.

Natural Selection drives evolution. Since irrespective of the repertoire of different genes present in the gene pool, if one gene is not chosen above another gene (based on some criteria) then the relative frequencies of genes in gene pool will remain the same and consequently there wouldn't be any evolution.
So clearly some gene has to get selected over another, this selection criteria is called Natural Selection.

However for Natural Selection to work there must be sufficient diversity in the gene pool itself so that some or the other gene is good enough to pass the test proposed by selection. Thus rate of mutation also is a factor at driving evolution. Its observed that viruses mutate at a higher rate and (thus?) evolve at a higher rate too.

However, mutations can either make a gene better at passing the present selection test, or make it worse at it. If they make the gene worse then it will fail the test and disappear into oblivion. But generally mutations don't cause any significant change in the capability of a gene to pass a selection test. These 'neutral' mutations can accumulate to a large number over the span of time. Random drifts in such neutral mutations can result in some gene being replaced by another as result of random event. Might seem a little far fetched, but in the timescales that evolution works under its definitely plausible. And so random drifts in neutral mutations may drive evolution too.

Is there any bias for or against some particular mutation? Is it more likely for an 'A' to convert into 'G' or 'C' rather than a 'T'. Or for that matter any other combination of 'ATGC' in the above sentence. I don't know if such bias is proven, but this bias has been implicated as the reason for the observed GC content variations among closely related prokaryotic species.

Of course all these mechanisms are not mutually exclusive w.r.t gene or time. For different genes different factors would be responsible for evolution. But these forces probably are the reason why one species evolves into another with passage of time.

The Computational Biologist Arrives.

Biology started off as a descriptive science. There was a lot of observation involved and description of observed data. That was the day of the Naturalist. He/She went afield with telescopes in hand or a magnifying lens perhaps and waited hours patiently for the glimpse of that rare species. With the classification of life into kingdom animalia and plantae, evolved the Botanist, concerned chiefly with plants, and the Zoologist, concerned chiefly with animals. Then one day Antonie van Leeuwenhoek directed his microscope to a spoiled food item and a whole new world of the microorganisms was discovered. As understanding about the variety of flora and fauna increased interest grew in the behavior of these organisms. This was still a descriptive science.

Observation of behavior lead to observation of other phenomena and Mendel was the first to explain the concept of heredity. I consider the time between the early 1900s to mid 1900s the golden age for biology. Not only was the structure of DNA, which by then was proven to be the information carrier, elucidated but also the mechanism of coding this information found out. This period saw the birth of the experimental biologist. No longer did the biologist have to roam about in the fields, he explained fundamental biological phenomena in the laboratory. However unlike the experimental physicist who first builds a hypothesis, and then designs experiments to verify it, the experimental biologist has no hypothesis. Instead the experimental biologist asks objective questions about a particular phenomena and then designs experiments which will give him the answers. He uses these answers to then explain the phenomena. However the tricks and tools of his trade were not refined and were unwieldy.

Then came the revolution called molecular biology. This science is predominantly tool and technology based. The molecular biologist again works differently than the experimental biologist. The molecular biologist generates data which he then analyses to come to his answers. As the technology of molecular biology matured, the molecular biologist soon started drowning in the flood of data, in stepped the Bioinformatician to the rescue.

The main job of the bioinformatician was to arrange the data in meaningful structures which could be made sense of by other biologists. Bioinformaticians soon became indispensable, especially with the advent of the Internet and its associated services. With the Human Genome Project and the subsequent easy availability of data in public databases, understanding of biology progressed by leaps and bounds.

However even with the advances in molecular biology techniques many questions remain unanswered. A new school of thought has emerged which argues that further understanding of biology will only take place if the whole organism is studied as a single system rather than understanding different phenomena independently. The Systems Biologist tries to take a broader view of the problem at hand. Unfortunately with the level of detail known today, multiple phenomena can be analysed only by the modern day computers. The number of variables involved is so high that the human brain cant analyse it.

This is when the computational biologist arrives. He is an expert in handling computers. The Computational biologist not only needs to understand the softwares being run on his machine but also the hardware that his machine is made up of. The computational biologist, infact, uses his machine as an extension of his own mental capacity to solve a problem. He is an expert in programming and biology at the same time. Computational Biology is the future of Biology which will solve problems of a global nature, where the whole organism is involved. This revolution might not come about overnight. The level of complexity known today is so much that even superfast computers take a lot of time to simulate a protein-protein interaction for a small time interval, as much as 1 day of computation to calculate a time interval of 1 pico sec. None the less trying to solve these problems experimentally is, in some cases impossible, and in other cases, not financially feasible, especially now that funding is so hard to come by. The Computational Biologist has definitely arrived on the scene and with advancement in computer hardware and software, tough biological problems will be tackled only by the Computational Biologist.

The inspiration for this article comes from:
http://www.geocities.com/letapk/physics3.html