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SWARTHMORE PHYS 120 - NETWORK BIOLOGY- UNDERSTANDING THE CELL’S FUNCTIONAL ORGANIZATION

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Reductionism, which has dominated biological researchfor over a century, has provided a wealth of knowledgeabout individual cellular components and their func-tions. Despite its enormous success, it is increasinglyclear that a discrete biological function can only rarelybe attributed to an individual molecule. Instead, mostbiological characteristics arise from complex interac-tions between the cell’s numerous constituents, such asproteins, DNA, RNA and small molecules1–8.Therefore,a key challenge for biology in the twenty-first century is tounderstand the structure and the dynamics of the com-plex intercellular web of interactions that contribute tothe structure and function of a living cell.The development of high-throughput data-collectiontechniques, as epitomized by the widespread use ofmicroarrays, allows for the simultaneous interrogation of the status of a cell’s components at any given time.In turn, new technology platforms, such as PROTEIN CHIPSor semi-automated YEAST TWO-HYBRID SCREENS,help to deter-mine how and when these molecules interact with eachother. Various types of interaction webs, or networks,(including protein–protein interaction, metabolic, sig-nalling and transcription-regulatory networks) emergefrom the sum of these interactions. None of these net-works are independent, instead they form a ‘network ofnetworks’ that is responsible for the behaviour of thecell. A major challenge of contemporary biology is toembark on an integrated theoretical and experimental programme to map out, understand and model in quan-tifiable terms the topological and dynamic properties of thevarious networks that control the behaviour of the cell.Help along the way is provided by the rapidly develop-ing theory of complex networks that, in the past fewyears, has made advances towards uncovering the orga-nizing principles that govern the formation and evolutionof various complex technological and social networks9–12.This research is already making an impact on cell biology.It has led to the realization that the architectural featuresof molecular interaction networks within a cell are sharedto a large degree by other complex systems, such as theInternet, computer chips and society. This unexpecteduniversality indicates that similar laws may govern mostcomplex networks in nature, which allows the expertisefrom large and well-mapped non-biological systems to beused to characterize the intricate interwoven relationshipsthat govern cellular functions.In this review, we show that the quantifiable tools ofnetwork theory offer unforeseen possibilities to under-stand the cell’s internal organization and evolution,fundamentally altering our view of cell biology. Theemerging results are forcing the realization that, not-withstanding the importance of individual molecules,cellular function is a contextual attribute of strict and quantifiable patterns of interactions between themyriad of cellular constituents. Although uncoveringthe generic organizing principles of cellular networksNETWORK BIOLOGY:UNDERSTANDING THE CELL’SFUNCTIONAL ORGANIZATIONAlbert-László Barabási* & Zoltán N. Oltvai‡A key aim of postgenomic biomedical research is to systematically catalogue all molecules andtheir interactions within a living cell. There is a clear need to understand how these molecules andthe interactions between them determine the function of this enormously complex machinery, bothin isolation and when surrounded by other cells. Rapid advances in network biology indicate thatcellular networks are governed by universal laws and offer a new conceptual framework that couldpotentially revolutionize our view of biology and disease pathologies in the twenty-first century.PROTEIN CHIPSSimilar to cDNA microarrays,this evolving technologyinvolves arraying a genomic setof proteins on a solid surfacewithout denaturing them. Theproteins are arrayed at a highenough density for the detection of activity, binding to lipids and so on.NATURE REVIEWS | GENETICS VOLUME 5 | FEBRUARY 2004 | 101*Department of Physics,University of Notre Dame,Notre Dame, Indiana 46556,USA.‡Department of Pathology,Northwestern University,Chicago, Illinois 60611,USA.e-mails: [email protected];[email protected]:10.1038/nrg1272REVIEWSYEAST TWO-HYBRID SCREENA genetic approach for theidentification of potentialprotein–protein interactions.Protein X is fused to the site-specific DNA-bindingdomain of a transcription factor and protein Y to itstranscriptional-activationdomain — interaction betweenthe proteins reconstitutestranscription-factor activity andleads to expression of reportergenes with recognition sites forthe DNA-binding domain.102 | FEBRUARY 2004 | VOLUME 5 www.nature.com/reviews/geneticsREVIEWSBox 1 | Network measures Network biology offers a quantifiable description of the networksthat characterize various biological systems. Here we define themost basic network measures that allow us to compare andcharacterize different complex networks.Degree The most elementary characteristic of a node is its degree (orconnectivity), k,which tells us how many links the node has to othernodes. For example, in the undirected network shown in part a ofthe figure, node A has degree k = 5. In networks in which each linkhas a selected direction (see figure, part b) there is an incomingdegree, kin,which denotes the number of links that point to a node,and an outgoing degree, kout,which denotes the number of links thatstart from it. For example, node A in part b of the figure has kin= 4and kout= 1. An undirected network with N nodes and L links ischaracterized by an average degree <k> = 2L/N (where <> denotesthe average).Degree distributionThe degree distribution, P(k), gives the probability that a selectednode has exactly k links. P(k) is obtained by counting the number ofnodes N(k) with k = 1, 2… links and dividing by the totalnumber of nodes N. The degree distribution allows us to distinguishbetween different classes of networks. For example, a peaked degreedistribution, as seen in a random network (BOX 2),indicates that thesystem has a characteristic degree and that there are no highlyconnected nodes (which are also known as hubs). By contrast, apower-law degree distribution indicates that a few hubs holdtogether numerous small nodes (BOX 2).Scale-free networks and the degree exponent Most biological networks are scale-free, which means that their degree distribution approximates a power law, P(k) ~ k –γ,where γ is the


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SWARTHMORE PHYS 120 - NETWORK BIOLOGY- UNDERSTANDING THE CELL’S FUNCTIONAL ORGANIZATION

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