A review of morphogenetic engineering

Doursat, René; Sayama, Hiroki; Michel, Olivier

doi:10.1007/s11047-013-9398-1

Cited by 108 publications

(89 citation statements)

References 98 publications

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“…This picture was proposed by Jamie Davies, who introduced the concept of synthetic morphology as a hybrid discipline aimed towards the programming of tissues and artificial multicellular assemblies [48]. A closely related view, named morphogenetic engineering, a synthetic biology path, was suggested as a form of shape engineering exploiting both programmed circuits and self-organization [49]. Extra computational complexity can be achieved by introducing synthetic circuits as part of complex multicellular structures.…”

Section: Discussionmentioning

confidence: 99%

Synthetic associative learning in engineered multicellular consortia

Macía

Vidiella

Solé

2017

J. R. Soc. Interface.

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Associative learning (AL) is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was recognized early as a general trait of complex multicellular organisms but is also found in 'simpler' ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell, and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display AL responses to two classes of stimuli and displaying long-and short-term memory (i.e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.

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Section: Discussionmentioning

confidence: 99%

Synthetic associative learning in engineered multicellular consortia

Macía

Vidiella

Solé

2017

J. R. Soc. Interface.

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“…Considering the embodied nature of these aggregates is not only required as an additional feature but also it can actually be crucial to understanding the transition itself. It is worth noting that the use of physical models of multicellularity reveals that even under very simplistic assumptions, complex forms easily emerge [107][108][109][110][111][112].…”

Section: Synthetic Multicellularity and Organismalitymentioning

confidence: 99%

Synthetic transitions: towards a new synthesis

Solé

2016

Phil. Trans. R. Soc. B

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One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

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“…It typically uses indirect encoding between the genotype and phenotype [109], usually through the expression of rules. Rules can be anything from simple grammar rules [40,58] to complex gene regulatory systems, designed to closely mimic biological development [64,28]. AD alows the possibility of shrinking the genome size (genetic representation) of an organism drastically compared to the total number of cells of the developed organism.…”

Section: Artificial Developmentmentioning

confidence: 99%

Studying network morphology in common developmental genomes

Antonakopoulos

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Developmental biology seeks to understand how organisms are constructed. Development is a set of very complex processes involving a coded program as the organisms' genome. This genome describes how to build the organism, but not how the organism will look like. The zygote (the initial single cell), will eventually develop into a trillion cell organism. This extraordinary phenomenon has been an inspiration to the world of Computer Science and Artificial Life and has led to the creation of Artificial Embryogeny (AE). Artificial Embryogeny is a sub-discipline of evolutionary computation (EC) in which a phenotype undergoes a developmental phase. The number of AE systems currently being developed investigate mainly how principal biological processes and mechanisms can be exploited in the artificial world.One approach that utilize the phase of biological development in artificial systems is called Artificial Development (AD) where the genotype (genetic representation) contain a similar set of instructions -as in the biological organisms case -called generative program or developmental encoding. Therefore, the process of development comprise to actually execute those instructions and deal with the highly parallel interactions between them and the structure they create.On the other hand, nature uses the same fundamental machinery and almost the same genetic information to create vastly different creatures. A study reveals that about 99% of mouse genomes have direct counterparts in humans with cats having 90% of their homologous genes identical to humans. How is it possible for nature to use a vast majority of the same genetic representation in the DNA but still be able to develop such distant species? It was found that a common regulator gene can control the formation of many of the internal organs in both nematodes and vertebrates. Therefore, the very same gene can initiate the process of formation and define its outcome, for example, an intestine or a muscle cell.This thesis investigates how to design an Artificial Embryogeny by using the same genetic information to develop a class of computational architectures or different computational architectures. The result of this investigation has given rise to the Common Developmental Genomes (CDG). The computational architectures targeted, have a common characteristic of being sparsely-connected networks, with each node acting as a simple computational unit. Such computational architectures are cellular automata and boolean networks, artificial neural networks and cellular neural networks.The approach followed includes the following steps: a. investigate which architectures are suitable for development with such a model, b. describe a common developmental approach that can handle the targeted architectures, c. define how genetic information can be exploited by the developmental process, so as to develop these architectures and d. identify a suitable genome representation to ensure that different structures can be developed and achieved. The target architectures chos...

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A review of morphogenetic engineering

Cited by 108 publications

References 98 publications

Synthetic associative learning in engineered multicellular consortia

Synthetic associative learning in engineered multicellular consortia

Synthetic transitions: towards a new synthesis

Studying network morphology in common developmental genomes

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