We know Motoo Kimura beause of the neutral theory. (Or should I say that we know the neutral theory because of Kimura?) But before his classic paper in 1968, Kimura was already a prominent figure in evolutionary genetics, mainly because of his productive use of the diffusion method to study the change of gene frequencies in populations. Why is diffusion so important? And who was actually the first to apply it to population genetics?

### Diffusion and probability

Suppose that we have a large population, large enough that you can assume that it’s infinite in size. By using the so-called deterministic models one can easily compute the effects of evolutionary forces in the gene composition of such a population. Now suppose that the population is rather small. In that case, small fluctuations due to sampling will obviously influence the evolution of genes. This is known as genetic drift, and its study requires extensive computations. If you want to know how genetic drift affects to a population with mutation, selection, epistasis and/or linkage of multiple alleles, the computations become impractical or even impossible. But if you assume that the change in a gene frequency is very small during a short fraction of time, you can treat your gene frequencies as if they were particles diffusing in a continuum of probability states. This is (in a very simplistic way) the principle underlying the diffusion method.

### Fisher’s diffusion approach

Ronald Fisher first thought that the probability of a given gene frequency could be modelled in a continuous space (as I indicated above). He borrowed the heat diffusion equation from thermodynamics and adapted it to genetics. This is, as far as we know, the first use of diffusion in population genetics. However, a diffusion process is an approximation, and Fisher’s approximation wasn’t accurate enough. Indeed, Sewall Wright noticed some discrepancies with Fisher diffusion approach. After this, Fisher found an error and was just in time to correct the equation right before the publication of his 1930 book. Fisher thanked Wright and admitted:

“I have now fully convinced myself that your [Wright’s] solution is the right one” Letter from Fisher to Wright, quoted in Provine, 1986

However, Fisher’s ego was already quite damaged, and he soon stopped communicating with Wright. In later years, Fisher developed a bit further his diffusion method, but mostly for his own amusement, and never brought it to the fore of population genetics. (One may speculate that he was still affected by his early mistake.)

### Proper diffusion: Kolmogorov and Feller

In 1931, the prolific mathematician Andrey Kolmogorov published his celebrated probability diffusion equations (although under a different name). A few years later, William Feller fully explored the potentials of Kolmogorov’s diffusion and coined the terms forward and backwards equations to refer to the two most popular forms of these equations. As Feller noticed, Kolmogorov added an additional term to the standard heat diffusion equation. That term, precisely, is the part that Fisher missed in his first approximation and that he added later on.

Kolmogorov quickly realized of the potential of his own equations to described evolutionary dynamics and published a paper about it (see comment in Feller 1951). Kolmogorov sent a reprint of his paper to Sewall Wright, who rapidly published a paper in PNAS using Kolmogorov forward equation to calculate the stationary distribution of gene frequencies. However, Wright himself preferred his integration method and his paper received little attention. Feller and Malécot showed later that Fisher’s diffusion, Wright’s integrals and the classical branching models all converge to the Kolmogorov forward equation. That is, they are mathematically equivalent. The path was ready for someone to fully exploit the potential of Kolmogorov’s equations in genetics.

### Kimura enters the game

Motoo Kimura first read about diffusion processes in Wright’s 1945 paper and quickly started to develop these equations for his own purposes. Kimura’s first diffusion paper was indeed communicated to the National Academy of Sciences by Sewall Wright himself. But if Kimura surprised the other theoreticians was because of his use of Kolmogorov’s backward equation to calculate the probability and time of fixation of new genes in a population. Kimura provided a new horizon to explore the evolution of finite-size populations in a time in which computers were not powerful enough. However, he was aware of his limits as a mathematician, and invited others to join his particular crusade:

“I cannot escape from this limitation […] but I hope it will stimulate mathematicians to work in this fascinating field”. Kimura 1964

The years that followed were dominated by the diffusion method, and ‘proper’ mathematicians joined the ‘diffusion crew’ (Karlin, Ewens and Watterson, to name but a few). The diffusion method gained in rigour and precision. Today the diffusion method has lost some interest in favour of computational simulation. However, they are still at the core (and the heart) of theoretical population genetics.

### So, who was first?

So far we could concluded that Fisher was the first using diffusion to approximate stochastic processes, not only in genetics but in probability theory. However, as also noticed by Feller, the original Fisher’s diffusion equation was first used in a probabilistic context by Albert Einstein in his classic paper about Brownian motion of particles, almost 20 years before Fisher’s account! It may be that probability as a diffusion process was a popular topic among mathematicians in the early 20th Century, and that Fisher was smart enough to adapt it to genetics before anyone else. Whether Fisher knew or not about Einstein’s approach, I have no idea.

For an historical discussion on the use of diffusion equations in genetics I recommend Kimura’s review on the topic and Felsenstein free textbook on Theoretical Population Genetics. For a technical account it is often recommended Warren Ewens classic text, but I’ve found more useful the recent manual by Otto and Day on mathematical models. The later is, in my opinion, the best textbook in mathematical biology I’ve read so far.

So, what is the take home message? Who we have to thank for the diffusion method in population genetics? It’s hard to summarize the contributions of the different people involved. But if I have to write a single sentence I would conclude: Fisher was the first, Kimura did the best!