Investigating Artificial Cells' Spatial Proliferation with a Gene Regulatory Network

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@Article{DEMBELE:2017:PCS,

• author = "Jean Marie Dembele and Sylvain Cussat-Blanc and Jean Disset and Yves Duthen",
• title = "Investigating Artificial Cells' Spatial Proliferation with a Gene Regulatory Network",
• journal = "Procedia Computer Science",
• year = "2017",
• volume = "114",
• pages = "208--215",
• note = "Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS October 30 - November 1, 2017, Chicago, Illinois, USA",
• keywords = "genetic algorithms, genetic programming, Artificial ontogeny, Dynamical Systems, Particle Systems, Evolutionary algorithm, Adaptive systems",
• ISSN = "1877-0509",
• URL = "http://www.sciencedirect.com/science/article/pii/S1877050917318665",
• DOI = "doi:10.1016/j.procs.2017.09.062",
• size = "8 page",
• abstract = "This paper discusses the combination of a Gene Regulatory Network (GRN) with a Genetic Algorithm (GA) in the context of spatial proliferation of artificial and dynamical cells. It gives the first steps in constructing and investigating simple ways of self-adaptation to furnish lifelike behaving cells. We are thus interested in growing an adaptive cells population in respect to environmental conditions. From a single cell, evolving on some nutriment field, we obtain relatively complex shapes, and functions, acquired with a GA. In a previous work, the artificial cells have been implemented with physical primitives for motion (in order to move correctly in space by convection and diffusion dynamics). The main goal of this current work is therefore to implement, for these physically moving cells, an embedded mechanism providing them with decisions capacities when it comes to choose the suitable biological routines (mitosis, apoptosis, migrationa) depending on nutriment conjuncture. To that end, we use a protein-based GRN, easily evolvable to achieve adequate behavior in response to environment inputs. In order to build such a GRN, we start from random GRNs, train them using a GA with a generic nutriment field and different fitness functions, and finally we run the obtained evolved GRN in different nutriment fields to test the robustness of our self-adaption structure",
• notes = "Universite Gaston Berger de Saint-Louis, UFR SAT, BP 234, Saint-Louis, Senega",

}

Genetic Programming entries for Jean Marie Dembele Sylvain Cussat-Blanc Jean Disset Yves Duthen

Citations