Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism

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  author =       "Jason Robert White",
  title =        "Integration of Reaction Kinetics Theory and Gene
                 Expression Programming to Infer Reaction Mechanism",
  school =       "Chemical Engineering, University of Connecticut",
  year =         "2014",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, network inference, reaction
                 kinetics, viral dynamics, human immunodeficiency virus,
                 systems biology, AIDS",
  URL =          "",
  URL =          "",
  size =         "199 pages",
  abstract =     "Mechanistic mathematical models of biological systems
                 have been used to describe biological phenomena,
                 including human disease, in the hope that one day these
                 models may be used to better understand diseases, as
                 well as to develop and optimize therapeutic strategies.
                 Evolutionary algorithms, such as genetic programming,
                 may be used to symbolically regress mathematical models
                 describing chemical and biochemical species for which
                 kinetic data are available. However, current
                 evolutionary algorithms are restricted to the
                 formulation of simple or approximate models due to the
                 computational cost of evolving mechanistic models for
                 more complex systems.

                 It was hypothesized that chemical reaction kinetic
                 theory could be used to sufficiently reduce the model
                 search space for an evolutionary algorithm such that it
                 would be possible to infer mechanistic mathematical
                 models of complex biological interactions. An
                 evolutionary algorithm capable of formulating mass
                 action kinetic models of biological systems from time
                 series data sets was developed for a system of
                 n-species using heuristics from chemical reaction
                 kinetic theory and a gene expression programming (GEP)
                 based approach.

                 The resulting algorithm was then successfully validated
                 on a general model of viral dynamics that accounted for
                 six pathways relating the change in viral template,
                 viral genome, and viral structural protein
                 concentrations over time.

                 The algorithm was applied to generate cohort-specific
                 models of HIV dynamics from a clinical data set. HIV-1
                 infection models were defined as sets of two ordinary
                 differential equations describing the change in CD4+
                 T-cell and HIV-1 concentrations over time. The evolved
                 models were used to generate hypotheses regarding
                 treatment effectiveness and the potential for viral
                 rebound in three cohorts of HIV-1 positive individuals
                 receiving different Highly Active Antiretroviral
                 Therapy (HAART) regimens. It was hypothesized by the
                 algorithm that HAART was effective in stopping HIV-1
                 propagation in two of the three cohorts studied. In the
                 other cohort, it was hypothesized that HIV-1 continued
                 to propagate and that there was the potential for viral

                 The result of this work was the development of an
                 algorithm that can be used for the generation of
                 complex mechanistic biological models based upon
                 kinetic data with potential uses in fields ranging from
                 biomedical to biotechnological.",
  notes =        "Supervisor Ranjan Srivastava",

Genetic Programming entries for Jason R White