Based on data on gene expression profiles from microarrays for E. Coli and tumor tissues, I show that the evolution for thousands of generations leads to a set of strongly over- and under-expressed genes, as compared to the initial expression profile. This fact is manifested as heavy tails in the gene expression distribution functions . The tails are heavier -- i.e. the fraction of genes and the expression levels vary much more -- in tumors than in bacteria. Different tissue tumors show remarkably similar tails, indicating a kind of universality in cancer phenomena .
In many situations, the gene expression signature is a unique marker of the biological state. We study the modification of the gene expression distribution function when the biological state of a system experiences a change. This change may be the result of a selective pressure, as in the Long Term Evolution Experiment with E. Coli populations, or the progression to Alzheimer disease in aged brains, or the progression from a normal tissue to the cancer state. The first two cases seem to belong to a class of transitions, where the initial and final states are relatively close to each other, and the distribution function for the differential expressions is short ranged, with a tail of only a few dozens of strongly varying genes. In the latter case, cancer, the initial and final states are far apart and separated by a low-fitness barrier. The distribution function shows a very heavy tail, with thousands of silenced and over-expressed genes. We characterize the biological states by means of their principal component representations, and the expression distribution functions by their maximal and minimal differential expression values and the exponents of the Pareto laws describing the tails .
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Based on data on gene expression profiles from microarrays for
In many situations, the gene expression signature is a unique marker of the biological state. We study the modification of the gene expression distribution function when the biological state of a system experiences a change. This change may be the result of a selective pressure, as in the Long Term Evolution Experiment with
R
and tumor tissues, I show that the evolution for thousands of generations leads to a set of strongly over- and under-expressed genes, as compared to the initial expression profile. This fact is manifested as heavy tails in the gene
populations, or the progression to Alzheimer disease in aged brains, or the progression from a normal tissue to the cancer state. The first two cases seem to belong to a class of transitions, where the initial and final states are relatively close to each other, and the distribution function for the differential expressions is short ranged, with a tail of only a few dozens of strongly varying genes. In the latter case, cancer, the initial and final states are far apart and separated by a low-fitness barrier. The distribution function shows a very heavy tail, with thousands of silenced and over-expressed genes. We characterize the biological states by means of their principal component representations, and the
R
. The tails are heavier -- i.e. the fraction of genes and the expression levels vary much more -- in tumors than in bacteria. Different tissue tumors show remarkably similar tails, indicating a kind of universality in cancer phenomena
by their maximal and minimal differential expression values and the exponents of the Pareto laws describing the tails