Recent developments in parameter estimation and structure identification of biochemical and genomic systems

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@Article{Chou200957,
  author =       "I-Chun Chou and Eberhard O. Voit",
  title =        "Recent developments in parameter estimation and
                 structure identification of biochemical and genomic
                 systems",
  journal =      "Mathematical Biosciences",
  volume =       "219",
  number =       "2",
  pages =        "57--83",
  year =         "2009",
  ISSN =         "0025-5564",
  DOI =          "doi:10.1016/j.mbs.2009.03.002",
  URL =          "http://www.sciencedirect.com/science/article/B6VHX-4VXDV4R-2/2/f7f1904f15cf7aa7404c664ae4658ce8",
  keywords =     "genetic algorithms, genetic programming, Parameter
                 estimation, Network identification, Inverse modelling,
                 Biochemical Systems Theory",
  abstract =     "The organisation, regulation and dynamical responses
                 of biological systems are in many cases too complex to
                 allow intuitive predictions and require the support of
                 mathematical modeling for quantitative assessments and
                 a reliable understanding of system functioning. All
                 steps of constructing mathematical models for
                 biological systems are challenging, but arguably the
                 most difficult task among them is the estimation of
                 model parameters and the identification of the
                 structure and regulation of the underlying biological
                 networks. Recent advancements in modern high-throughput
                 techniques have been allowing the generation of time
                 series data that characterise the dynamics of genomic,
                 proteomic, metabolic, and physiological responses and
                 enable us, at least in principle, to tackle estimation
                 and identification tasks using [`]top-down' or
                 [`]inverse' approaches. While the rewards of a
                 successful inverse estimation or identification are
                 great, the process of extracting structural and
                 regulatory information is technically difficult. The
                 challenges can generally be categorised into four
                 areas, namely, issues related to the data, the model,
                 the mathematical structure of the system, and the
                 optimisation and support algorithms. Many recent
                 articles have addressed inverse problems within the
                 modelling framework of Biochemical Systems Theory
                 (BST). BST was chosen for these tasks because of its
                 unique structural flexibility and the fact that the
                 structure and regulation of a biological system are
                 mapped essentially one-to-one onto the parameters of
                 the describing model. The proposed methods mainly
                 focused on various optimization algorithms, but also on
                 support techniques, including methods for circumventing
                 the time consuming numerical integration of systems of
                 differential equations, smoothing overly noisy data,
                 estimating slopes of time series, reducing the
                 complexity of the inference task, and constraining the
                 parameter search space. Other methods targeted issues
                 of data preprocessing, detection and amelioration of
                 model redundancy, and model-free or model-based
                 structure identification. The total number of proposed
                 methods and their applications has by now exceeded one
                 hundred, which makes it difficult for the newcomer, as
                 well as the expert, to gain a comprehensive overview of
                 available algorithmic options and limitations. To
                 facilitate the entry into the field of inverse modeling
                 within BST and related modeling areas, the article
                 presented here reviews the field and proposes an
                 operational [`]work-flow' that guides the user through
                 the estimation process, identifies possibly problematic
                 steps, and suggests corresponding solutions based on
                 the specific characteristics of the various available
                 algorithms. The article concludes with a discussion of
                 the present state of the art and with a description of
                 open questions.",
  notes =        "GP included in Survey",
}

Genetic Programming entries for I-Chun Chou Eberhard O Voit

Citations