Reprint

Biological Networks

Edited by
December 2018
174 pages
  • ISBN978-3-03897-433-8 (Paperback)
  • ISBN978-3-03897-434-5 (PDF)

This is a Reprint of the Special Issue Biological Networks that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Summary

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.

Format
  • Paperback
License and Copyright
© 2019 by the authors; CC BY license
Keywords
identifiability; controllability; reachability; observability; parameter estimation; nonlinear systems; differential geometry; multivariate statistics; Fisher discriminant analysis; probability density function; autism spectrum disorder; one carbon metabolism; transsulfuration; urine toxic metals; classification; machine learning; pathway crosstalk; Alzheimer’s disease; biomarker; disease prediction; optimal experimental design; D-optimality criterion; Fisher information matrix; sensitivity analysis; IL-6 signaling; parameter estimation; piecewise constant functions; biochemical systems theory; biofuel; lignin biosynthesis; optimization; plant metabolism; recalcitrance; design of experiments; multi-objective optimization; Fisher information matrix; curvature; biological processes; mathematical modeling; latent tuberculosis; immune system; cytokine signaling network; dynamic systems; collagen remodeling; mathematical modeling; biological networks; sensitivity analysis; programmed cell death; single cell dynamics; cell population; systems biology; parameter estimation; NFκB signaling pathway; lipopolysaccharide; flow cytometry; sensitivity analysis; Zymomonas mobilis; succinic acid; gene deletion; genome-scale metabolic model; systems biology; n/a

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