Scale-free networks in cell biology

Abstract

A cell's behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large number of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theoretical descriptions of the relationships between different cellular components. Recent theoretical advances allow us to describe cellular network structure with graph concepts and have revealed organizational features shared with numerous non-biological networks. We now have the opportunity to describe quantitatively a network of hundreds or thousands of interacting components. Moreover, the observed topologies of cellular networks give us clues about their evolution and how their organization influences their function and dynamic responses.

Boltzmann, Lotka and Volterra and spatial structural evolution: an integrated methodology for some dynamical systems

“…There are many examples in biology, for example using the methods of network analysis in cell biology and proteomics (Albert 2005). In these cases, the networks are very different and, because there will be high levels of interdependence between elements, the Boltzmann conditions are unlikely to apply.…”

Do scale-free regulatory networks allow more expression than random ones?

“…A gene network has two types of nodes, which correspond to transcription factors and the genes encoding them, and two types of directed links, which correspond to transcriptional regulation and translation (Lee et al, 2002). For simplicity, transcription factors are often combined with the genes encoding them (thus all nodes correspond to genes), and transcription and translation are condensed to one link (the assumption being if any of both processes happens, the other occurs too; see Albert, 2005). The nodes representing target genes that do not encode transcription factors become This is not the case for the dependence network of software packages, in which a steady state of installed packages is reached once no more packages can be installed without entering into conflict with the previously installed packages.…”

“…Since these transcription factors are themselves products of genes, the ultimate effect is that genes regulates each other's expression as a part of gene regulatory networks (Davidson, 2001;Guelzim et al, 2002;Lee et al, 2002;Albert, 2005). The patterns of regulatory interactions at genomic scale (in which genes can affect each other's expression) are becoming increasingly resolved (Davidson et al, 2002;Guelzim et al, 2002;Lee et al, 2002;Stuart et al, 2003;Luscombe et al, 2004).…”

The cognitive phenotype of Down syndrome: Insights from intracellular network analysis

“…These applications nicely fit the molecules to nodes and interactions to links simplification used to describe intracellular mammalian interactions networks. 66 Watts and Strogatz 67 used two previously defined global statistical properties of graphs to characterize networks: clustering coefficient and characteristic path length. It was found that biological interaction networks have higher clustering coefficients and similar characteristic path lengths expected if the networks would be randomly rewired.…”