The origin of multicellular organisms and the mechanism of development in cell societies are studied by choosing a model with intracellular biochemical dynamics allowing for oscillations, cell--cell interaction through diffusive chemicals on a two-dimensional grid, and state-dependent cell adhesion. Cells differentiate due to a dynamical instability, as described by our "isologous diversi#cation" theory. A #xed spatial pattern of differentiated cells emerges, where spatial information is sustained by cell--cell interactions. This pattern is robust against perturbations. With an adequate cell adhesion force, active cells are released that form the seed of a new generation of multicellular organisms, accompanied by death of the original multicellular unit as a halting state. It is shown that the emergence of multicellular organisms with differentiation, regulation, and life cycle is not an accidental event, but a natural consequence in a system of replicating cells with growth.

A mechanism of genetic diversification and reproductive isolation is presented based on the interaction-induced diversification of phenotypes. First, phenotypes of individuals with identical genotypes split into a few groups, according to instability in the developmental dynamics associated with the interaction among individuals. Later, through competition for reproduction and mutational change of genes, the phenotypic dierences are fixed to genes, until the groups (`species') are completely separated in terms of genes as well as phenotypes. In addition, we demonstrate that the proposed theory for speciation works also under sexual recombination and provides a basis for the evolution of mating preference. The relevance of the results to natural evolution are discussed, including incomplete penetrance in mutants and the change in flexibility in genotype--phenotype correspondence. Possible experiments are proposed to verify the theory presented.

Th origin and robustness ofmorphxpMfl`EI are studied by dynamical system modeling of a cell society, inwhflW cells possessing internal chernal reaction dynamics interactwith each oth thac th mutual interactionwith diffusive chfusive in a twodimensional medium. It is foundthn stem-type cells differentiate into various cell types (whsp a cell `type' is defined by a type of intra-cellular dynamics) due to a dynamic instability caused by cell--cell interactions in a manner described by th isologous diversificationthersi Th differentiations are spatially regulated by th concentration ofchflI`I'p in th medium,whiu th chium, concentrations are locally influenced by th intra-cell dynamics.Thamic tha reciprocalrelationshflx chlatio concentrations come toexhfiflfi spatial variation as differentiated cell types begin to emerge, and as a resultth regulation exercised by th chxfl'Ep concentrations become spatiallyinhallyp'fiWfl` Thp reinforcesth process of differentiation,thffer whff spatial patterns of differentiated cells appear.Withr thh reciprocalrelationshfi' th concentration gradients are read and interpreted byth cell as positional information. A spatial order of cells realized inthfi process represents a stable state of th system governed bythL reciprocalrelationsh`x and thp th developmental process thcess whce thc state is realized is robustwith respect to perturbations.Th dependence of th morphzpM'Lzfl onhpfiI" and th community effect in cell differentiation are also discussed.

By introducing a mean-field version of the tile automaton model introduced in earlier works, growth of molecules through chemical reaction networks is studied with explicit consideration for molecule shape as a "tile". Tiles are picked up randomly to collide, and with a certain rule they react to form new tiles. A non-trivial growth pattern, called joint growth is found, with which tiles grow by combining tiles successively. This joint growth leads to a power-law distribution of tile sizes, by forming a positive feedback process for reproduction of tiles through cooperative relationship among large tiles. This effective growth is achieved by spontaneous differentiation of time scales: quick process for an autocatalytic network and a slower process with joint growth. We also discuss the relevance of the present results to the origin of life as a loose set of reproducting chemicals. 2003 Elsevier Science B.V. All rights reserved.

Artificial Embryogeny (AE) can be described as the use of a dynamical system as a mid-step in a design process; Through emulating Biological Embryogenesis, we hope to reach levels of complexity and robustness currently impossible. AE is a new field, and suffers from a lack of standards and meaningful means of evaluation. In this document, we review existing work, discussing motivations and merits of existing approaches. Throughout, we argue that a viewpoint which does not regard environment as a primary source of information risks taking a naive view of evolution. We argue that ``complexity'' is vaguely and inconsistently defined, and propose several novel measures; Perhaps the simplest model of AE, the Terminating Cellular Automaton, is introduced, and used to compute and contrast our measures. Next, the Deva family of AE algorithms is introduced, a modular Cellular Automaton-like group. A domain of application from Civil Engineering is chosen as an interpretation of the grown organisms. It is initially shown that it is possible to use a Deva algorithm to evolve Plane Trusses successfully, this interpretation providing a discipline-independent measure of success. A series of empirical experiments is undertaken, showing the relative efficacy and effects of several model-level strategies in the context of the evolution of structural design. Finally, we explore the role of environment as a constraint on development of structural form. We demonstrate a strong resistance to environmental change by successfully re-growing the organisms in new environments, showing that some Deva organisms are adding information from the environment to their overall morphology; This provides an arti?cial analogue to the re-use of genes which characterizes biological development.

Existence of biological uncertainty principle implies that we can never find ’THE ’ measure for biological complexity.