Equation 21 · Einstein's Random Walk and the Mathematics of Genetic Drift
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol phi
phi occurs above the fraction bar. The numerator is divided by the entire denominator below it.
Symbol t
t is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.
Symbol V
V is one of the signed contributions combined to compute the quantity on the left.
Symbol N
N occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.
=
The expressions on both sides represent the same quantity under the stated assumptions.
See an illustrated explanation →derivative
This notation tracks how one quantity changes with another. The indicated variable tells which change is being measured.
superscript
A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.
See an illustrated explanation →Denominator: partial t
The complete quantity below the fraction bar; it must be nonzero for this division.
Denominator: 2
The complete quantity below the fraction bar; it must be nonzero for this division.
Denominator: partial p^2
The complete quantity below the fraction bar; it must be nonzero for this division.
Denominator: 2N
The complete quantity below the fraction bar; it must be nonzero for this division.
How to interpret it
With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
Motoo Kimura, at Japan’s National Institute of Genetics, made the borrowing exact rather than approximate. His 1955 paper in the Proceedings of the National Academy of Sciences solved the full time-dependent diffusion equation derived from the Wright-Fisher model, recovering not just the eventual resting distribution of an allele’s frequency but its whole transient shape as it evolves generation by generation [ 6 ] . His 1962 paper in Genetics then posed the general fixation problem as a diffusion boundary-value equation and named the lineage precisely: the question of a mutant gene’s ultimate fate was “first treated quantitatively by Fisher (1922),” with “equivalent results” reached…
Read the full surrounding passage
Motoo Kimura, at Japan’s National Institute of Genetics, made the borrowing exact rather than approximate. His 1955 paper in the Proceedings of the National Academy of Sciences solved the full time-dependent diffusion equation derived from the Wright-Fisher model, recovering not just the eventual resting distribution of an allele’s frequency but its whole transient shape as it evolves generation by generation [ 6 ] . His 1962 paper in Genetics then posed the general fixation problem as a diffusion boundary-value equation and named the lineage precisely: the question of a mutant gene’s ultimate fate was “first treated quantitatively by Fisher (1922),” with “equivalent results” reached independently by Haldane in 1927 and Wright in 1931 [ 7 ] . Kimura wrote the fixation probability u(p,t) as the solution of what he explicitly called the Kolmogorov backward equation, subject to the boundary conditions u(0,t)=0 and u(1,t)=1 — an allele already lost stays lost, one already fixed stays fixed [ 7 ] . That boundary structure is doing real work: it is the diffusion equation’s way of encoding that zero and one are absorbing states of a finite population, not just convenient endpoints on a graph. Set the two founding equations beside each other and the claim of one shared mathematics stops being a figure of speech: . The same second-derivative operator acts on a diffusion term in both; only the variable and the coefficient it multiplies have changed. One honest difference is worth stating rather than smoothing over: Einstein’s D is a constant, fixed once by temperature and viscosity, while Kimura’s V(p) depends on the frequency itself, so the spread is fastest near p=0.5 and vanishes at the boundaries — an allele already fixed or already lost has nothing left to drift into [ 7 ] . The operator is identical; the coefficient it acts on has learned something about the biology.
Sources cited in the surrounding passage
- [6] A General Solution of the Wright-Fisher Model of Random Genetic Drift ↗
- [7] On the Probability of Fixation of Mutant Genes in a Population ↗
These citations give research context. Read each source to check which claims it supports.
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