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Italian Workshop on Artificial Life and Evolutionary Computation

WIVACE 2016: Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry pp 91–102Cite as

Automatic Design of Boolean Networks for Cell Differentiation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 708))

Abstract

Cell differentiation is the process that denotes a cell type change, typically from a less specialised type to a more specialised one. Recently, a cell differentiation model based on Boolean networks subject to noise has been proposed. This model reproduces the main abstract properties of cell differentiation, such as the attainment of different degrees of differentiation, deterministic and stochastic differentiation, reversibility, induced pluripotency and cell type change. The generic abstract properties of the model have been already shown to match those of the real biological phenomenon. A direct comparison with specific cell differentiation processes and the identification of genetic network features that are linked to specific differentiation traits requires the design of a suitable Boolean network such that its dynamics matches a set of target properties. To the best of our knowledge, the only current method for addressing this problem is a random generate and test procedure.

In this work we present an automatic design method for this purpose, based on metaheuristic algorithms. We devised two variants of the method and tested them against random search on typical abstract differentiation trees. Results, although preliminary, show that our technique is far more efficient than both random search and complete enumeration and it is able to find solutions to instances which were not solved by those techniques.

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Notes

  1. 1.

    In general, the so-called lineage tree may not be a proper tree structure, but rather a graph. However, without loss of generality, in this work we will focus on tree structures.

  2. 2.

    This hypothesis is supported by the observation that cells has a finite lifetime, which enables their dynamics to explore only a portion of the possible attractor transitions.

  3. 3.

    http://tree-edit-distance.dbresearch.uni-salzburg.at/.

  4. 4.

    See [5, 9] for details on this technique.

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Acknowledgements

The authors thank Alex Graudenzi and Chiara Damiani for helpful discussions and suggestions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Alma Mater Studiorum Università di Bologna, Cesena, Italy

    Michele Braccini & Andrea Roli

  2. Department of Physics, Informatics and Mathematics, Università di Modena e Reggio Emilia, Modena, Italy

    Marco Villani & Roberto Serra

  3. European Centre for Living Technology, Venice, Italy

    Marco Villani & Roberto Serra

Authors
  1. Michele Braccini
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  2. Andrea Roli
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  3. Marco Villani
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  4. Roberto Serra
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Corresponding author

Correspondence to Andrea Roli .

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Editors and Affiliations

  1. Chemistry and Biology, University of Salerno, Fisciano, Italy

    Federico Rossi

  2. Department of Pharmacy, University of Salerno, Fisciano, Italy

    Stefano Piotto

  3. Department of Industrial Engineering, University of Salerno, Fisciano, Italy

    Simona Concilio

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Braccini, M., Roli, A., Villani, M., Serra, R. (2017). Automatic Design of Boolean Networks for Cell Differentiation. In: Rossi, F., Piotto, S., Concilio, S. (eds) Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. WIVACE 2016. Communications in Computer and Information Science, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-57711-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-57711-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57710-4

  • Online ISBN: 978-3-319-57711-1

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