Scale-free networks in cell biology

By Réka Albert. Journal of Cell Science, 2005

1,273 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b7695732b89cfb43f322de0fdc1c23c8c68a83cb

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Citations in the corpus

Using graph theory to analyze biological networks (2011, background): “An interesting analysis of most of these properties in various PPI, metabolic or transcriptional networks of several organisms ( S. cerevisiae , H. pylori , C. elegans ) can be found in [100].”
Graph-based term weighting for information retrieval (2012, background): “…jEðGÞj denotes the cardinality of edges in G, and jVðGÞj denotes the cardinality of vertices in G. Extensive work on graph theory in the last decade (reviewed in Albert 2005; Albert and Barabási 2002; Boccaletti et al. 2006; Dorogovtsev and Mendes 2002; Newman 2003) has demonstrated that graphs…”
Topological analysis and interactive visualization of biological networks and protein structures (2012, background): “Commonly, large-scale interaction data are represented as networks and initially analyzed by graph-theoretic methods to characterize the topological network structure and its global and local interaction properties 12–18 .”
The Emergent Network Structure of the Multilateral Environmental Agreement System (2013, background): “Shortcuts provide alternative pathways and contribute to path redundancy that may enable the robust functioning of the system by relying less on individual pathways or mediators (Albert, 2005).”
Human disease classification in the postgenomic era: A complex systems approach to human pathobiology (2007, background): “Scale-free networks also minimize the consequences of most biochemical or genetic errors (Wagner and Fell, 2001), and accommodate perturbations to the network with minimal effects on critical functions (Pastor-Satorras and Vespignani, 2002), unless hubs are the targets of the perturbations (Albert, ”
The implications of human metabolic network topology for disease comorbidity (2008, methodology): “To address this question, we define the network distance between two diseases as the length (number of links) of the shortest reaction pathway connecting them within the MDN, a metric often used in network theory (10, 11, 24, 25).”
A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. (2006, background): “…properties of the ataxia network are compatible with the known topological characteristics of protein interaction networks (Barabasi and Oltvai, 2004; Albert, 2005), we investigated the degree distribution P(k) of the participating proteins and found that our disease network shows a scale-free…”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “This explains why scale-free networks are ve ry abundant in nature, being associated with the evolution of cellular processes and genes [191].”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, background): “Common global properties include modular organization [1,2], the presence of highly connected hub nodes, and approximate ‘scale free topology’ [3,4].”
Protein-to-protein interactions: Technologies, databases, and algorithms (2010, methodology): “Once an interaction network is modeled by using graphs, the study of biological properties can be done using graph-based algorithms [Aittokallio and Schwikowski 2006], and associating graph properties to the biological properties of the modeled PPI, is similar to what is done in other domains, such ”
Conserved and variable architecture of human white matter connectivity (2011, background): “These hubs likely support the majority of network traffic, and so their identity and characterization is important in describing the robustness and sensitivity of the network architecture (Albert and Barabási, 2002; Albert, 2005).”
Learning structured densities via infinite dimensional exponential families (2015, background): “For example, since many real-world networks are scale-free [1], several algorithms are designed specifically to learn structure of such networks”
Eigengene networks for studying the relationships between co-expression modules (2007, background): “From the point of view of systems biology ('from above'), functional modules bridge the gap between individual genes and emergent global properties [8-10].”