  A fitness landscape is a mapping from the space of genetic sequences, which
is modeled here as a binary hypercube of dimension $L$, to the real numbers. We
consider random models of fitness landscapes, where fitness values are assigned
according to some probabilistic rule, and study the statistical properties of
pathways to the global fitness maximum along which fitness increases
monotonically. Such paths are important for evolution because they are the only
ones that are accessible to an adapting population when mutations occur at a
low rate. The focus of this work is on the block model introduced by A.S.
Perelson and C.A. Macken [Proc. Natl. Acad. Sci. USA 92:9657 (1995)] where the
genome is decomposed into disjoint sets of loci (`modules') that contribute
independently to fitness, and fitness values within blocks are assigned at
random. We show that the number of accessible paths can be written as a product
of the path numbers within the blocks, which provides a detailed analytic
description of the path statistics. The block model can be viewed as a special
case of Kauffman's NK-model, and we compare the analytic results to simulations
of the NK-model with different genetic architectures. We find that the mean
number of accessible paths in the different versions of the model are quite
similar, but the distribution of the path number is qualitatively different in
the block model due to its multiplicative structure. A similar statement
applies to the number of local fitness maxima in the NK-models, which has been
studied extensively in previous works. The overall evolutionary accessibility
of the landscape, as quantified by the probability to find at least one
accessible path to the global maximum, is dramatically lowered by the modular
structure.
