Graph grammars with string-regulated rewriting

Multicellular organisms undergo a complex developmental process, orchestrated by the genetic information in their cells, in order to form a newborn individual from a fertilized egg. This complex process, not completely understood yet, is believed to have a key role in generating the impressive biotic diversity of organisms found on earth. Inspired by mechanisms of Eukaryotic genetic expression, we propose and analyse graph grammars with string-regulated rewriting. In these grammatical systems a genome sequence is represented by a regulatory string, a graph corresponds to an organism, and a set of graph grammar rules represents different forms of implementing cell division. Accordingly, a graph derivation by the graph grammar resembles the developmental process of an organism. We give examples of the concept and compare its generative power to the power of the traditional context-free graph grammars. We demonstrate that the power of expression increases when genetic regulation is included in the model, as compared to non-regulated grammars. Additionally, we propose a hierarchy of string-regulated graph grammars, arranged by expressive power. These results highlight the key role that the transmission of regulatory information during development has in the emergence of biological diversity.D.L. was supported in part by a research stay fellowship at Otto-von-Guericke-Universität Magdeburg from the Spanish Ministerio de Educación

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Towards More Relevant Evolutionary Models: Integrating an Artificial Genome With a Developmental Phenotype

The relationship between the genotype and phenotype of organisms plays a key role in the evolutionary process. While Evolutionary Computation (EC) models have traditionally taken biological inspiration in the design of many key model components (e.g., genetic mutation and crossover, populations under natural selection, etc.), there is a need for more biological input in specifying how a genotype forms a phenotype. There are two powerful theoretical abstractions used in biology for explaining the evolutionary basis of phenotypic development. The first is that there is a sequence of hereditary information (the genotype) passed from one generation to the next. The second is that genes extracted from this sequence interact to form networks of regulation that, when coupled with environmental factors, control the development of an organism (the phenotype). An abstract model of gene regulation exists in the form of the Artificial Genome. This model provides a principled approach to extracting regulatory networks of genes from sequence-level information. L-systems provide a mature framework for modelling developmental phenotypes interacting within environments. This paper takes a step towards integrating these two models, providing a biologically-inspired modelling framework that bridges the chasm between processes occurring in evolutionary timescales, and those occurring within individual lifetimes

Membrane Computing Schema: A New Approach to Computation Using String Insertions

In this paper, we introduce the notion of a membrane computing schema for string objects. We propose a computing schema for a membrane network (i.e., tissue-like membrane system) where each membrane performs unique type of operations at a time and sends the result to others connected through the channel. The distinguished features of the computing models obtained from the schema are: 1. only context-free insertion operations are used for string generation, 2. some membranes assume filtering functions for structured objects (molecules), 3. generating model and accepting model are obtained in the same schema, and both are computationally universal, 4. several known rewriting systems with universal computability can be reformulated by the membrane computing schema in a uniform manner. The first feature provides the model with a simple uniform structure which facilitates a biological implementation of the model, while the second feature suggests further feasibility of the model in terms of DNA complementarity. Through the third and fourth features, one may have a unified view of a variety of existing rewriting systems with Turing computability in the framework of membrane computing paradigm.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58