A survey on OR and mathematical methods applied on gene-environment networks

Weber G., Kropat E., ÖZTÜRK B., Gorgulu Z.

CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH, vol.17, no.3, pp.315-341, 2009 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 3
  • Publication Date: 2009
  • Doi Number: 10.1007/s10100-009-0092-4
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.315-341
  • Keywords: Gene-environment networks, Operational research, Financial mathematics, Environmental protection, Generalized semi-infinite programming, Dynamical systems, Computational statistics, Uncertainty, Identification, Structural stability, Computational biology, GENERALIZED SEMIINFINITE OPTIMIZATION, EXPRESSION, STABILITY


In this paper, we survey the recent advances and mathematical foundations of gene-environment networks. We explain their interdisciplinary implications with special regard to human and life sciences as well as financial sciences. Special attention is paid to applications in Operational Research and environmental protection. Originally developed in the context of modeling and prediction of gene-expression patterns, gene-environment networks have proved to provide a conceptual framework for the modeling of dynamical systems with respect to errors and uncertainty as well as the influence of certain environmental items. Given the noise-prone measurement data we extract nonlinear differential equations to describe and analyze the interactions and regulating effects between the data items of interest and the environmental items. In particular, these equations reflect data uncertainty by the use of interval arithmetics and comprise unknown parameters resulting in a wide variety of the model. For an identification of these parameters Chebychev approximation and generalized semi-infinite optimization are applied. In addition, the time-discrete counterparts of the nonlinear equations are introduced and their parametrical stability is investigated by a combinatorial algorithm which detects the region of parameter stability. Finally, we analyze the topological landscape of the gene-environment networks in terms of structural stability. We conclude with an application of our analysis and introduce the eco-finance networks.