Fundamental patterns underlying gene expression profiles: Simplicity from complexity

Analysis of previously published sets of DNA microarray gene expression data by singular value decomposition has uncovered underlying patterns or “characteristic modes” in their temporal profiles. These patterns contribute unequally to the structure of the expression profiles. Moreover, the essential features of a given set of expression profiles are captured using just a small number of characteristic modes. This leads to the striking conclusion that the transcriptional response of a genome is orchestrated in a few fundamental patterns of gene expression change. These patterns are both simple and robust, dominating the alterations in expression of genes throughout the genome. Moreover, the characteristic modes of gene expression change in response to environmental perturbations are similar in such distant organisms as yeast and human cells. This analysis reveals simple regularities in the seemingly complex transcriptional transitions of diverse cells to new states, and these provide insights into the operation of the underlying genetic networks.

Long-term change in the organization of inventive activity

Relying on a quantitative analysis of the patenting and assignment behavior of inventors, we highlight the evolution of institutions that encouraged trade in technology and a growing division of labor between those who invented new technologies and those who exploited them commercially over the nineteenth and early-twentieth centuries. At the heart of this change in the organization of inventive activity was a set of familiar developments which had significant consequences for the supply and demand of inventions. On the supply side, the growing complexity and capital intensity of technology raised the amount of human and physical capital required for effective invention, making it increasingly desirable for individuals involved in this activity to specialize. On the demand side, the growing competitiveness of product markets induced firms to purchase or otherwise obtain the rights to technologies developed by others. These increasing incentives to differentiate the task of invention from that of commercializing new technologies depended for their realization upon the development of markets and other types of organizational supports for trade in technology. The evidence suggests that the necessary institutions evolved first in those regions of the country where early patenting activity had already been concentrated. A self-reinforcing process whereby high rates of inventive activity encouraged the evolution of a market for technology, which in turn encouraged greater specialization and productivity at invention as individuals found it increasingly feasible to sell and license their discoveries, appears to have been operating. This market trade in technological information was an important contributor to the achievement of a high level of specialization at invention well before the rise of large-scale research laboratories in the twentieth century.

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."