Abstract
The evolution of high-dimensional phenotypes is investigated using a statistical physics model consisting of interacting spins, in which phenotypes, genotypes, and environments are represented by spin configurations, interaction matrices, and external fields, respectively. We found that phenotypic changes upon diverse environmental change and genetic variation are highly correlated across all spins, consistent with recent experimental observations of biological systems. The dimension reduction in phenotypic changes is shown to be a result of the evolution of the robustness to thermal noise, achieved at the replica symmetric phase.
Highlights
The evolution of high-dimensional phenotypes is investigated using a statistical physics model consisting of interacting spins, in which phenotypes, genotypes, and environments are represented by spin configurations, interaction matrices, and external fields, respectively
We found that phenotypic changes upon diverse environmental change and genetic variation are highly correlated across all spins, consistent with recent experimental observations of biological systems
The changes in the concentrations of mRNAs or proteins are found to be correlated [1,2,3] or proportional [4,5,6] across all components, against a variety of environmental stresses. This global proportionality suggests that phenotypic changes against environmental perturbations are constrained along a one- or low-dimensional manifold, a manifestation of a drastic dimension reduction from the high-dimensional composition space [7,8]
Summary
The changes in the (logarithmic) concentrations of mRNAs or proteins are found to be correlated [1,2,3] or proportional [4,5,6] across all components, against a variety of environmental stresses This global proportionality suggests that phenotypic changes against environmental perturbations are constrained along a one- or low-dimensional manifold, a manifestation of a drastic dimension reduction from the high-dimensional composition space [7,8]. Such dimension reduction would be rather universal in biological systems, as reported in studies of protein dynamics [9], ecological systems [10], and neural learning dynamics [11].
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