International audience

We propose an original 2D agent-based model of biological "autopoiesis", the process by which a cell creates and continuously regenerates itself, considered one of the defining characteristics of life.In the space of our simulations, the positions of free molecules are continuous, and polymerized membrane components are regularly arranged (plus noise).While autopoiesis commonly refers to the self-driven maintenance of a system, we also follow Varela's historical study of emergence, and show that the same model can account for both selfperpetuation and self-formation-a step toward uniting the three main perspectives on life: origins, autopoiesis, and replication.Exploring different initial and environmental conditions, we observe that destructive reactions are important for the survival of our autopoietic system, and evaluate their impact on its lifespan.The tendency of cells to form spurious outgrowths is counteracted by moderate decay of the membrane.

This special issue pays tribute to 20 years of artificial life research in Europe. Since its inception around a coffee table in Paris in the summer of 1990, the European Conference on Artificial Life (ECAL) has grown and evolved to incorporate multiple research disciplines that aim to combine the natural and computational sciences. In addition to extending a long and rich tradition in theoretical biology present at that time in Europe toward the computational sciences and engineering, ECAL has also provided a platform for different innovative ideas that have produced a significant impact on other research domains or have resulted in novel research directions with their own workshops and conferences. This influence continues today, with the consideration of the different models and methods that are currently extensively used to answer biological questions in domains such as computational and systems biology. This evolution will benefit the field and will ensure that our models continue to push the envelope as they have been doing for the last two decades.Together with the reviewers and conference chairs of ECAL 2011 in Paris, we selected a small number of especially interesting contributions among the conference's 128 accepted submissions. Their authors were asked to extend their manuscripts for a wider audience and to provide more details than what was allowed by the conference format. The reader can find below a brief description of these articles, which range from artificial chemistry to evolutionary robotics and computational biology. We hope that this selection will provide an incentive for researchers from related fields to pay closer attention to the innovations created within the artificial life community and to motivate researchers already interested in this field to make further advances in the realism and influence of their models. In the following we summarize the topics discussed in this special issue.Xabier Barandiaran and Matthew Egbert study how norms become established from an organism-centered perspective. They give an in-depth analysis and evaluation of a minimal model capable of establishing norms dynamically. The model clearly opens a novel road toward answering questions associated to the notion of minimal agency, which is essential to a wide variety of artificial life models.The work of Navneet Bhalla, Peter Bentley, Peter Vize, and Christian Jacob focuses on the use of staging in the context of physical self-assembling systems made of magnetic shapes. They investigate whether replacing a full mix with gradual addition of morphological information manages to produce more stable constructs for systems composed of elements with heterogeneous properties. They show that this staging methodology can solve complex self-assembly problems by reducing the possibilities of errors in the shape-matching process.Tom Froese, Nathaniel Virgo, and Takashi Ikegami argue in the third article that time scales should play a central role in all models aiming to answer questions linked to the origin of life. Using a reaction-diffusion model, they assert that adaptive behavior realized at intermediate time scales by spatiotemporally localized self-producing systems may have been already present in the beginnings, and conclude with the feasibility of a movement-first approach to the origin of life.In the fourth contribution, Heiko Hamann, Thomas Schmickl, and Karl Crailsheim investigate the relevance of statistical mechanics, and more specifically of the fluctuation theorem, to understanding the collective behavior of simulated swarms. They are able to show that an inverted fluctuation theorem is present in this type of systems, which they claim may be generally applicable to different self-organizing systems studied in artificial life.Tsutomu Oohashi, Tadao Maekawa, Osamu Ueno, Norie Kawai, Emi Nishina, and Manabu Honda investigate a completely different question: Can organisms evolve to give up their immortality? Using an artificial-chemistry-based model called SIVA, they examine the evolution of programmed self-decomposition. Interestingly, they identify the conditions for mortal organisms emerging from immortal ones to outcompete them, which they associate with the emergence of an altruistic activity in terrestrial life.As mentioned earlier, artificial life modeling has a prominent role to play in computational and systems biology. The manuscript of Joshua Payne, Jason Moore, and Andreas Wagner is an example of this cross-fertilization. The authors investigate the space of signal-integration functions, which are defined as functions that map regulatory signals to gene expression states. Their aim is to understand the relationship between the robustness and evolvability of gene expression states when genetic perturbations modify these functions. They show, among other things, that the majority of signal-integration functions are resistant to perturbation.José Pereira, Porfírio Silva, Pedro Lima, and Alcherio Martinoli propose in their work a new framework to coordinate the actions of robot teams using concepts from the field of institutional economics. They first introduce the notion of executable Petri nets (EPNs), which can be run directly on a robot. These EPNs are then used to define institutions and individual robot behaviors, resulting in what they call an institutional agent controller. To validate this new approach, they design some swarm-related experiments in which robots need to coordinate to remain connected over a wireless network, showing that their novel institutional robotics framework can perform as well as some of the other approaches that aim to design swarm behavior, while offering a higher potential for modular control design and consideration of social behavior in robot teams.In the eighth contribution to this special issue, John Rieffel, Davis Knox, Schuyler Smith, and Barry Trimmer provide insight into four years of research into soft robotics. They particularly focus on the question of how to coevolve soft robot morphology and control. They argue that the evolution of soft robots corresponds to solving a problem of three tightly interdependent variables, namely material, morphology and control. In their study, they provide three approaches to address this problem, showing a way to achieve physically embodied soft robots.Within the 20 years of the European Conference of Artificial Life, numerous articles have addressed new evolutionary algorithms designed to solve particular problems or to overcome specific issues in the model originally proposed by John Holland. The ninth and last article, by Nicholas Tomko, Inman Harvey, Nathaniel Virgo, and Andrew Philippides, focuses here on the question of how to improve on prior work about niching and speciation. They suggest a new genetic algorithm (GA), called the group GA, which is based on principles of group or multilevel selection. They show that this new algorithm outperforms some of the earlier works in the same context, and that the group size need not be preset but can itself be evolved.

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.

MOTIVATION: Multi-view microscopy techniques such as Light-Sheet Fluorescence Microscopy (LSFM) are powerful tools for 3D + time studies of live embryos in developmental biology. The sample is imaged from several points of view, acquiring a set of 3D views that are then combined or fused in order to overcome their individual limitations. Views fusion is still an open problem despite recent contributions in the field. RESULTS: We developed a wavelet-based multi-view fusion method that, due to wavelet decomposition properties, is able to combine the complementary directional information from all available views into a single volume. Our method is demonstrated on LSFM acquisitions from live sea urchin and zebrafish embryos. The fusion results show improved overall contrast and details when compared with any of the acquired volumes. The proposed method does not need knowledge of the system's point spread function (PSF) and performs better than other existing PSF independent fusion methods. AVAILABILITY AND IMPLEMENTATION: The described method was implemented in Matlab (The Mathworks, Inc., USA) and a graphic user interface was developed in Java. The software, together with two sample datasets, is available at http://www.die.upm.es/im/software/SPIMFusionGUI.zip A public release, free of charge for non-commercial use, is planned after the publication of this article.

We propose to directly process 3D + t image sequences with mathematical morphology operators using a new classification of the 3D+t structuring elements. Several methods (filtering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebrafish embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebrafish early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it benefits from the intrinsic redundancy of the temporal dimension and minimizes the needs for human intervention in semi-automatic algorithms.

Investigating cell dynamics during early zebrafish embryogenesis requires specific image acquisition and analysis strategies. Multiharmonic microscopy, i.e., second- and third-harmonic generations, allows imaging cell divisions and cell membranes in unstained zebrafish embryos from 1- to 1000-cell stage. This paper presents the design and implementation of a dedicated image processing pipeline (tracking and segmentation) for the reconstruction of cell dynamics during these developmental stages. This methodology allows the reconstruction of the cell lineage tree including division timings, spatial coordinates, and cell shape until the 1000-cell stage with minute temporal accuracy and micrometer spatial resolution. Data analysis of the digital embryos provides an extensive quantitative description of early zebrafish embryogenesis.

We designed a strategy for extracting the shapes of cell membranes and nuclei from time lapse confocal images taken throughout early zebrafish embryogenesis using a partial-differential-equation-based segmentation. This segmentation step is a prerequisite for an accurate quantitative analysis of cell morphodynamics during embryogenesis and it is the basis for an integrated understanding of biological processes. The segmentation of embryonic cells requires live zebrafish embryos fluorescently labeled to highlight sub-cellular structures and designing specific algorithms by adapting classical methods to image features. Our strategy includes the following steps: the signal-to-noise ratio is first improved by an edge-preserving filtering, then the cell shape is reconstructed applying a fully automated algorithm based on a generalized version of the Subjective Surfaces technique. Finally we present a procedure for the algorithm validation either from the accuracy and the robustness perspective.

We propose an extension of the classical morphological filtering based on openings by line segment structuring elements. It consists in filtering a 3D+time image with the opening by all the possible rotations of a segment in the 4D space, given an initial segment and a rotation angle. The method has been applied to remove noise from confocal laser scanning microscopy images of live zebrafish embryos engineered to fluorescently label all their cell membranes.

July 01 2005 Reply to Ripps John Stewart, John Stewart CNRS Responsable Network of Excellence “Enactive Interfaces”Département Technologie et Sciences, de l'Homme, Centre Pierre Guillaumat, BP 60649, 60206 Compiègne Cedex, France Search for other works by this author on: This Site Google Scholar Paul Bourgine Paul Bourgine COSTECH Département Technologie et Sciences de l'HommeDépartement Technologie et Sciences, de l'Homme, Centre Pierre Guillaumat, BP 60649, 60206 Compiègne Cedex, France Search for other works by this author on: This Site Google Scholar Author and Article Information John Stewart CNRS Responsable Network of Excellence “Enactive Interfaces”Département Technologie et Sciences, de l'Homme, Centre Pierre Guillaumat, BP 60649, 60206 Compiègne Cedex, France Paul Bourgine COSTECH Département Technologie et Sciences de l'HommeDépartement Technologie et Sciences, de l'Homme, Centre Pierre Guillaumat, BP 60649, 60206 Compiègne Cedex, France Online Issn: 1530-9185 Print Issn: 1064-5462 © 2005 Massachusetts Institute of Technology2005 Artificial Life (2005) 11 (3): 365. https://doi.org/10.1162/artl.2005.11.3.365 Cite Icon Cite Permissions Share Icon Share MailTo Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation John Stewart, Paul Bourgine; Reply to Ripps. Artif Life 2005; 11 (3): 365. doi: https://doi.org/10.1162/artl.2005.11.3.365 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2005 Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.

This article revisits the concept of autopoiesis and examines its relation to cognition and life. We present a mathematical model of a 3D tesselation automaton, considered as a minimal example of autopoiesis. This leads us to a thesis T1: "An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopoietic domain." We propose a modified definition of autopoiesis: "An autopoietic system is a network of processes that produces the components that reproduce the network, and that also regulates the boundary conditions necessary for its ongoing existence as a network." We also propose a definition of cognition: "A system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint." It follows from these definitions that the concepts of autopoiesis and cognition, although deeply related in their connection with the regulation of the boundary conditions of the system, are not immediately identical: a system can be autopoietic without being cognitive, and cognitive without being autopoietic. Finally, we propose a thesis T2: "A system that is both autopoietic and cognitive is a living system."

This article focuses on an artificial life approach to some important problems in machine learning such as statistical discrimination, curve approximation, and pattern recognition. We describe a family of models, collectively referred to as semi-algebraic networks (SAN). These models are strongly inspired by two complementary lines of thought: the biological concept of autopoiesis and morphodynamical notions in mathematics. Mathematically defined as semi-algebraic sets, SANs involve geometric components that are submitted to two coupled processes: (a) the adjustment of the components (under the action of the learning examples), and (b) the regeneration of new components. Several examples of SANs are described, using different types of components. The geometric nature of SANs gives new possibilities for solving the bias/variance dilemma in discrimination or curve approximation problems. The question of building multilevel semi-algebraic networks is also addressed, as they are related to cognitive problems such as memory and morphological categorization. We describe an example of such multilevel models.

The most important question that autonomous systems have to answer is how to remain viable in various and changing environments. This question is thus the same as how emerges the semiotic capacity to guess viable solutions, that is abduction. The aim of this paper is to sketch a theory both of sensorimotor autonomy and of the emergence of abductive capacities in the cases of reactive, hedonistic and eductive systems. Such emergence has to be examined in the larger context of co-evolutionary process between autonomous systems.>

To conceive is to look for what does not exist and nonetheless fmd it" Plaute

The authors discuss the extraction of logical rules from a multilayer neural network when building a validity domain for the network is possible. This approach appears to be efficient when the expertise domain is restricted and when explicit rules are not easy to formulate. The validity domain is described as a set of mathematical and logical constraints. The constraints specifying the validity domain are then included in the constraint-propagation methods that can be used for extracting equivalent logical rules from the neural network. A corpus of jurisprudence illustrates these conditions very effectively. The logical rules that are extracted can be used either to explain the functioning of the neural network or as a first step for designing an expert system

A generalist-specialist paradigm for building neural networks is presented. The underlying principle is derived from an analogy with medicine. In most cases, a general practitioner or generalist is able to make a sure diagnosis: however, the generalist sometimes hesitates between several diagnoses and chooses to send his patient to see one or several specialists. This neural network architecture offers many advantages: improvement of the network performances, enhancement of the network interpretation, and selection of the critical frontiers between attraction valleys. The design of specialists is strongly dependent on the ambiguity threshold. A small threshold will lead to a rather simple network, whereas a larger value ensures a better robustness for the global network. This design methodology allows one to adjust the architecture's complexity to the overall problem's complexity. The authors present the definition of ambiguities matrices for the generalist networks, discuss methods for constructing specialists, and examine the functioning of the overall network