Abstract
Living cells are controlled by networks of interacting genes, proteins and biochemicals. Cells use the emergent collective dynamics of these networks to probe their surroundings, perform computations and generate appropriate responses. Here, we consider genetic networks, interacting sets of genes that regulate one another’s expression. It is possible to infer the interaction topology of genetic networks from high-throughput experimental measurements. However, such experiments rarely provide information on the detailed nature of each interaction. We show that topological approaches provide powerful means of dealing with the missing biochemical data. We first discuss the biochemical basis of gene regulation, and describe how genes can be connected into networks. We then show that, given weak constraints on the underlying biochemistry, topology alone determines the emergent properties of certain simple networks. Finally, we apply these approaches to the realistic example of quorum-sensing networks: chemical communication systems that coordinate the responses of bacterial populations.
Highlights
Genes are physically embodied as a string of nucleotide bases (ATGGCCCTG. . . ) on a self-replicating DNA molecule, contained within the cytoplasm of a prokaryote or the nucleus of a eukaryote
There are three types of changes that can be used to modulate the response of genetic networks, operating on completely distinct time scales
When studying natural genetic networks, the approach to take depends on the extent of available data
Summary
Genes are physically embodied as a string of nucleotide bases (ATGGCCCTG. . . ) on a self-replicating DNA molecule, contained within the cytoplasm of a prokaryote or the nucleus of a eukaryote. A deeper analysis suggests that gene number is not the correct measure of complexity: the properties of a cell are specified by the proteins contained within it; the range of possible cell types is determined by the range of possible combinations of expressed genes, and grows exponentially with gene number. Saccharomyces cerevisiae, there is a growing body of detailed information regarding transcriptional and regulatory interactions [9,10,11,12] When these data are combined, what emerges is a picture of highly structured networks with rich topologies [13], containing recurring motifs or patterns [14, 15], very different from randomly connected sets of genes. Here is what one might call the central idea of network biology: that the complex behaviour of living cells must be understood as emerging not just from the properties of individual genes, but from the manner in which they are connected
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Schedule a callMore From: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
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