Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Nobile:thesis,
  author =       "Marco Salvatore Nobile",
  title =        "Evolutionary Inference of Biological Systems
                 Accelerated on Graphics Processing Units",
  school =       "Dipartimento di Informatica, Sistemistica e
                 Comunicazione Universita degli Studi di
                 Milano-Bicocca",
  year =         "2014",
  address =      "Milan, Italy",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, particle swarm optimisation, PSO,
                 GPU, GPGPU, nvidia, CUDA, cupSODA, cuTauLeeping,
                 cuPEPSO, petri net, MemHPG, Schoegl",
  URL =          "https://boa.unimib.it/handle/10281/75434",
  URL =          "https://boa.unimib.it/retrieve/handle/10281/75434/111846/PhD_unimib_%20%09603317.pdf",
  size =         "318 pages",
  abstract =     "In silico analysis of biological systems represents a
                 valuable alternative and complementary approach to
                 experimental research. Computational methodologies,
                 indeed, allow to mimic some conditions of cellular
                 processes that might be difficult to dissect by
                 exploiting traditional laboratory techniques, therefore
                 potentially achieving a thorough comprehension of the
                 molecular mechanisms that rule the functioning of cells
                 and organisms. In spite of the benefits that it can
                 bring about in biology, the computational approach
                 still has two main limitations: first, there is often a
                 lack of adequate knowledge on the biological system of
                 interest, which prevents the creation of a proper
                 mathematical model able to produce faithful and
                 quantitative predictions; second, the analysis of the
                 model can require a massive number of simulations and
                 calculations, which are computationally burdensome. The
                 goal of the present thesis is to develop novel
                 computational methodologies to efficiently tackle these
                 two issues, at multiple scales of biological complexity
                 (from single molecular structures to networks of
                 biochemical reactions). The inference of the missing
                 data related to the three-dimensional structures of
                 proteins, the number and type of chemical species and
                 their mutual interactions, the kinetic parameters is
                 performed by means of novel methods based on
                 Evolutionary Computation and Swarm Intelligence
                 techniques. General purpose GPU computing has been
                 adopted to reduce the computational time, achieving a
                 relevant speedup with respect to the sequential
                 execution of the same algorithms. The results presented
                 in this thesis show that these novel evolutionary-based
                 and GPU-accelerated methodologies are indeed feasible
                 and advantageous from both the points of view of
                 inference quality and computational performances.",
  notes =        "Supervisor: Giancarlo Mauri",
}

Genetic Programming entries for Marco Nobile

Citations