An Introduction to the Bio-logic of Artificial Creatures

Duthen, Yves; Luga, Hervé; Lassabe, Nicolas; Cussat‐Blanc, Sylvain; Breton, Thomas; Pascalie, Jonathan

doi:10.1007/978-3-642-15690-8_1

Cited by 4 publications

(4 citation statements)

References 45 publications

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“…In contrast to normal cells, which work for the good of a macroscopic host (the combined 'self'), cancer cells have reduced their computational boundary to their own surface: to each cancer cell, the whole body is just 'the environment', at the expense of which all living things reproduce and survive. Cancer may be not be an increase of selfishness, but rather an unchanged self-interest coupled to a shift (reduction) of the internal self-model [65,[443][444][445]. This view challenges us to explore models that rely not on selfishness-based evolutionary dynamics but rather on the computational aspects of what cells and cell collectives measure and how they form internal models of self and environment [445][446][447].…”

Section: Global Physiological Dynamics Underlie Cancer: Information mentioning

confidence: 99%

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Moore

Walker

Levin

2017

Converg. Sci. Phys. Oncol.

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TOPICAL REVIEWCancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem IntroductionCancer is well-recognized not only as an extremely pernicious medical problem, but also as a group of phenomena deeply connected to the fundamental questions of evolution, multicellularity, pattern (disregulation, and the interplay between the genome and the environment [1,2]. Two fundamental paradigms currently divide the field. One is that cancer results from disorders of genetics: cancer cells are fundamentally broken due to genomic mutation or instability, and their clonal progeny form tumors and metastases. This view is sometimes called the SMT, somatic mutation theory [3][4][5]. A competing perspective is that of cancer as a circuit disease-a disorder of the complex biophysical, transcriptional, and epigenetic dynamics that regulate cellular state and the ability of cells to cooperate in vivo [6][7][8]. Under this view (sometimes called the tissue organization field theory or TOFT, [9,10]), cancer is akin to a traffic jam [11]-the problem is not a permanent discrete alteration within a founder cell and all of its clonal descendants, but an undesirable stable attractor in the complex network of controls that normally guides anatomical homeostasis [12].The relative merits of the two models have been discussed extensively [10,[13][14][15][16][17] AbstractThe current paradigm views cancer as arising clonally from a degradation of genetic information in single cells. A complementary perspective, originating at the dawn of modern developmental biology, is that cancer is the result of a system disorder of algorithms that normally orchestrate individual cell activities toward specific anatomical structures and away from tumorigenesis. A view of cancer as a disease of geometry focuses on the pathways that allow cells to cooperate to build and maintain large-scale anatomical patterning. Cancer may result when cells stop maintaining higher-order structures and reduce the boundary of their computational selves to a single-cell level, reverting to a unicellular lifestyle in which the rest of the organism is merely part of the environment at the expense of which all living things survive. While this view has been widely discussed, little progress has been made in providing a quantitative, mechanistic framework within which this perspective's specific and unique implications for treatment strategies can be tested and biomedically exploited. Here, we highlight two recent areas of progress which may facilitate much-needed progress on the cancer problem. First, we review the roles that endogenous bioelectrical networks, operating across many tissues in vivo, play as a medium of information processing in tumor suppression, progression, and reprogramming. Second, we provide a primer to the development of computational theory and tools for quantifying the information and causal control structures in cancer and other complex biological systems. Rigorous mathematical formalisms now exist to...

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Section: Global Physiological Dynamics Underlie Cancer: Information mentioning

confidence: 99%

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Moore

Walker

Levin

2017

Converg. Sci. Phys. Oncol.

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“…Our model, an example of digital embryogeny, 166 , 167 , 168 , 169 , 170 was designed not to make claims about specific gene products, but rather to show a general design principle sufficient to explain the observed tissue-wide, multimodal collective behavior. Such models will be made even more bio-realistic in the future by training the model from scratch with real-time dynamic physiomic and transcriptomic datasets that will come online in subsequent years.…”

Section: Discussionmentioning

confidence: 99%

Information integration during bioelectric regulation of morphogenesis of the embryonic frog brain

Manicka,

Pai,

Levin

2023

iScience

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“…This work is based on a research done by Duthen et al [10], which aimed to search a sequence of movements that can rearrange a given modular configuration into a new one that suits a different task. The target configuration is not explicitly specified, it is defined only by its desired function.…”

Section: B Robot's Configuration and Reconfigurationmentioning

confidence: 99%

“…2). We used the simulator developed in [10], and implemented our model of ANN controllers and the co-evoltionary approach on it and tested it using different configurations.…”

Section: B Robot's Configuration and Reconfigurationmentioning

confidence: 99%

Cooperative co-evolution of configuration and control for modular robots

Guettas

Cherif

Breton

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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The general approach in modular robots is to hand design the morphology, and then optimizes the controller of the structure for a given task. Evolutionary robotics has proposed evolution as a bio-inspired approach to overcome the limitations of human intuition in designing robots; theoretically, the resulting structures will be better adapted. In this work, we propose an approach based on cooperative co-evolutionary genetic algorithms to design configurations and controllers for homogenous robots implicitly to support self-reconfiguration, The algorithm introduces some elements to make finding solutions easier and faster by co-evolving two populations; a population of motions sequence to search a sequence of movements that can rearrange a given modular configuration into a new one that suits a different task defined by its desired function and a population of homogenous fixed topology ANNs for the controllers to perform locomotion as a behavior evolved using genetic algorithm based on standard deviation norm. The modular robots are evaluated in a simulation environment implemented with NVidia physics engine; PhysX. The experiments carried out in this work show that co-evolving both the configuration and the controllers positively contributes to the robot's performance and optimizes its locomotion behavior.

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An Introduction to the Bio-logic of Artificial Creatures

Cited by 4 publications

References 45 publications

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Information integration during bioelectric regulation of morphogenesis of the embryonic frog brain

Cooperative co-evolution of configuration and control for modular robots

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