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1413 results in Ebooks in network science

Page 34 of 71

Systems Biology of Cancer
  • Edited by Sam Thiagalingam
  • Published online:
    05 April 2015
    Print publication:
    09 April 2015
  • With over two hundred types of cancer diagnosed to date, researchers the world over have been forced to rapidly update their understanding of the biology of cancer. In fact, only the study of the basic cellular processes, and how these are altered in cancer cells, can ultimately provide a background for rational therapies. Bringing together the state-of-the-art contributions of international experts, Systems Biology of Cancer proposes an ultimate research goal for the whole scientific community: exploiting systems biology to generate in-depth knowledge based on blueprints that are unique to each type of cancer. Readers are provided with a realistic view of what is known and what is yet to be uncovered on the aberrations in the fundamental biological processes, deregulation of major signaling networks, alterations in major cancers and the strategies for using the scientific knowledge for effective diagnosis, prognosis and drug discovery to improve public health.

Systems Biology
  • Constraint-based Reconstruction and Analysis
  • Bernhard Ø. Palsson
  • Published online:
    05 February 2015
    Print publication:
    26 January 2015
  • Recent technological advances have enabled comprehensive determination of the molecular composition of living cells. The chemical interactions between many of these molecules are known, giving rise to genome-scale reconstructed biochemical reaction networks underlying cellular functions. Mathematical descriptions of the totality of these chemical interactions lead to genome-scale models that allow the computation of physiological functions. Reflecting these recent developments, this textbook explains how such quantitative and computable genotype-phenotype relationships are built using a genome-wide basis of information about the gene portfolio of a target organism. It describes how biological knowledge is assembled to reconstruct biochemical reaction networks, the formulation of computational models of biological functions, and how these models can be used to address key biological questions and enable predictive biology. Developed through extensive classroom use, the book is designed to provide students with a solid conceptual framework and an invaluable set of modeling tools and computational approaches.

  • 9 - The Stoichiometric Matrix

  • from II - Mathematical Properties of Reconstructed Networks
  • Bernhard Ø. Palsson, University of California, San Diego
  • Book:
    Systems Biology
    Published online:
    05 February 2015
    Print publication:
    26 January 2015, pp 151-171

  • Summary

    Mathematics is the door and key to the sciences

    – Roger Bacon

    The reactions that comprise a biological network can be represented by chemical equations. The stoichiometric matrix is formed from these chemical equations. It has several important attributes. In this chapter we focus on four principal views of the stoichiometric matrix and its content: (i) it is a data matrix, (ii) it is a connectivity matrix, (iii) it is a mathematical mapping operation, and (iv) it is a central part of in silico models used to compute steady and dynamic network states. These features are summarized in Figure 9.1.

    The Many Attributes of S

    The stoichiometric matrix is formed by the stoichiometric coefficients of the reactions that constitute a reaction network. It is organized such that every column corresponds to a reaction and every row corresponds to a compound. The entries in the matrix are stoichiometric coefficients that are integers. Each column that describes a reaction is constrained by the rules of chemistry, such as elemental balancing. Every row thus describes all the reactions in which the corresponding compound participates, and therefore how the reactions are interconnected. This deceptively simple matrix has many noteworthy attributes that are summarized in Table 9.1.

    Informatic attributes. The stoichiometric matrix is a data matrix. The data that go into building a genome-scale stoichiometric matrix come primarily from the annotated genomic sequence and detailed assessment of the literature (bibliomic data) that is available about the target organism. Often, inferences from phylogenetics are used as well. All this information is the basis for the reconstruction process described in Part I.

    Physical/chemical attributes. The stoichiometric coefficients represent counts of molecules that are involved in a chemical reaction. Chemical reactions come with conservation relationships of elements, charge, and other properties. These properties must be represented accurately. The cellular location of a reaction is included through the assignment of a metabolite to a cellular compartment.

    Genetic/genomic attributes. A genome-scale network reconstruction effectively represents a two-dimensional annotation of a genome [313].

Page 34 of 71