00853nam0-2200277 --450 991029595970332120181217103527.0978-88-541-9090-020181217d2016----kmuy0itay5050 baitaengIT 001yyC'era una volta la mafiala storia mai raccontata del primo boss della mafia americanaMike DashRomaNewton Compton2016331 p.ill.24 cm<<I >>volti della storia363MafiaStati Uniti d'AmericaStoria364.106097323itaDash,Mike761080ITUNINAREICATUNIMARCBK9910295959703321COLLEZ. 2427 (363)2399/2018FSPBCFSPBCC'era una volta la mafia1540723UNINA02891nam 22005653a 450 991034685120332120250203235427.09783038974345303897434X10.3390/books978-3-03897-434-5(CKB)4920000000095155(oapen)https://directory.doabooks.org/handle/20.500.12854/42229(ScCtBLL)c7695129-37ca-42ca-bd90-6a4c6e16fe0d(OCoLC)1163814011(EXLCZ)99492000000009515520250203i20182019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierBiological NetworksRudiyanto Gunawan, Neda BagheriBasel, Switzerland :MDPI,2018.1 electronic resource (174 p.)9783038974338 3038974331 Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales-from ecosystems to individual cells and from years to milliseconds. For these networks, the concept "the whole is greater than the sum of its parts" applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution-even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on "Biological Networks" showcases advances in the development and application of in silico network modeling and analysis of biological systems.Pathway crosstalkAlzheimer’s diseaseBioenergy cropsModel identificationMetabolic networksHost–pathogen interactionsSingle cellParameter sensitivityTuberculosisMultivariate statistical analysisSystems biologyBiological networksMathematical modelingLignin biosynthesisDesign of experimentsGunawan Rudiyanto1787457Bagheri NedaScCtBLLScCtBLLBOOK9910346851203321Biological Networks4320912UNINA