The perfect C. elagans project: an initial report
Artificial Life
Shapes in the shadow: evolutionary dynamics of morphogenesis
Artificial Life
Complex organization in multicellularity as a necessity in evolution
Artificial Life - Special issue on the Artificial Life VII: looking backward, looking forward
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Biologically inspired evolutionary development
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
There's more to a model than code: understanding and formalizing in silico modeling experience
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Artificial Life
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Directed evolution of an artificial cell lineage
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Application of logic synthesis to the understanding and cure of genetic diseases
Proceedings of the 49th Annual Design Automation Conference
Consideration of mobile DNA: new forms of artificial genetic regulatory networks
Natural Computing: an international journal
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Biological development is a remarkably complex process. A single cell, in an appropriate environment, contains sufficient information to generate a variety of differentiated cell types, whose spatial and temporal dynamics interact to form detailed morphological patterns. While several different physical and chemical processes play an important role in the development of an organism, the locus of control is the cell's gene regulatory network. We designed a dynamic recurrent gene network (DRGN) model and evaluated its ability to control the developmental trajectories of cells during embryogenesis. Three tasks were developed to evaluate the model, inspired by cell lineage specification in C. elegans, describing the variation in gene activity required for early cell diversification, combinatorial control of cell lineages, and cell lineage termination. Three corresponding sets of simulations compared performance on the tasks for different gene network sizes, demonstrating the ability of DRGNs to perform the tasks with minimal external input. The model and task definition represent a new means of linking the fundamental properties of genetic networks with the topology of the cell lineages whose development they control.