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Gumuskaya G, Srivastava P, Cooper BG, Lesser H, Semegran B, Garnier S, Levin M. Motile Living Biobots Self-Construct from Adult Human Somatic Progenitor Seed Cells. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2303575. [PMID: 38032125 PMCID: PMC10811512 DOI: 10.1002/advs.202303575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/31/2023] [Indexed: 12/01/2023]
Abstract
Fundamental knowledge gaps exist about the plasticity of cells from adult soma and the potential diversity of body shape and behavior in living constructs derived from genetically wild-type cells. Here anthrobots are introduced, a spheroid-shaped multicellular biological robot (biobot) platform with diameters ranging from 30 to 500 microns and cilia-powered locomotive abilities. Each Anthrobot begins as a single cell, derived from the adult human lung, and self-constructs into a multicellular motile biobot after being cultured in extra cellular matrix for 2 weeks and transferred into a minimally viscous habitat. Anthrobots exhibit diverse behaviors with motility patterns ranging from tight loops to straight lines and speeds ranging from 5-50 microns s-1 . The anatomical investigations reveal that this behavioral diversity is significantly correlated with their morphological diversity. Anthrobots can assume morphologies with fully polarized or wholly ciliated bodies and spherical or ellipsoidal shapes, each related to a distinct movement type. Anthrobots are found to be capable of traversing, and inducing rapid repair of scratches in, cultured human neural cell sheets in vitro. By controlling microenvironmental cues in bulk, novel structures, with new and unexpected behavior and biomedically-relevant capabilities, can be discovered in morphogenetic processes without direct genetic editing or manual sculpting.
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Affiliation(s)
- Gizem Gumuskaya
- Allen Discovery Center at Tufts Universityand Department of BiologyTufts UniversityMedfordMA02155USA
- Wyss Institute for Biologically Inspired EngineeringHarvard UniversityBostonMA02115USA
| | - Pranjal Srivastava
- Allen Discovery Center at Tufts Universityand Department of BiologyTufts UniversityMedfordMA02155USA
| | - Ben G. Cooper
- Allen Discovery Center at Tufts Universityand Department of BiologyTufts UniversityMedfordMA02155USA
| | - Hannah Lesser
- Allen Discovery Center at Tufts Universityand Department of BiologyTufts UniversityMedfordMA02155USA
| | - Ben Semegran
- Allen Discovery Center at Tufts Universityand Department of BiologyTufts UniversityMedfordMA02155USA
| | - Simon Garnier
- Federated Department of Biological SciencesNew Jersey Institute of TechnologyNewarkNJ07102USA
| | - Michael Levin
- Allen Discovery Center at Tufts Universityand Department of BiologyTufts UniversityMedfordMA02155USA
- Wyss Institute for Biologically Inspired EngineeringHarvard UniversityBostonMA02115USA
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Grodstein J, McMillen P, Levin M. Closing the loop on morphogenesis: a mathematical model of morphogenesis by closed-loop reaction-diffusion. Front Cell Dev Biol 2023; 11:1087650. [PMID: 37645245 PMCID: PMC10461482 DOI: 10.3389/fcell.2023.1087650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 07/31/2023] [Indexed: 08/31/2023] Open
Abstract
Morphogenesis, the establishment and repair of emergent complex anatomy by groups of cells, is a fascinating and biomedically-relevant problem. One of its most fascinating aspects is that a developing embryo can reliably recover from disturbances, such as splitting into twins. While this reliability implies some type of goal-seeking error minimization over a morphogenic field, there are many gaps with respect to detailed, constructive models of such a process. A common way to achieve reliability is negative feedback, which requires characterizing the existing body shape to create an error signal-but measuring properties of a shape may not be simple. We show how cells communicating in a wave-like pattern could analyze properties of the current body shape. We then describe a closed-loop negative-feedback system for creating reaction-diffusion (RD) patterns with high reliability. Specifically, we use a wave to count the number of peaks in a RD pattern, letting us use a negative-feedback controller to create a pattern with N repetitions, where N can be altered over a wide range. Furthermore, the individual repetitions of the RD pattern can be easily stretched or shrunk under genetic control to create, e.g., some morphological features larger than others. This work contributes to the exciting effort of understanding design principles of morphological computation, which can be used to understand evolved developmental mechanisms, manipulate them in regenerative-medicine settings, or engineer novel synthetic morphology constructs with desired robust behavior.
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Affiliation(s)
- Joel Grodstein
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, United States
| | - Patrick McMillen
- Allen Discovery Center at Tufts University, Medford, MA, United States
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, United States
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, United States
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Blackiston D, Kriegman S, Bongard J, Levin M. Biological Robots: Perspectives on an Emerging Interdisciplinary Field. Soft Robot 2023; 10:674-686. [PMID: 37083430 PMCID: PMC10442684 DOI: 10.1089/soro.2022.0142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023] Open
Abstract
Advances in science and engineering often reveal the limitations of classical approaches initially used to understand, predict, and control phenomena. With progress, conceptual categories must often be re-evaluated to better track recently discovered invariants across disciplines. It is essential to refine frameworks and resolve conflicting boundaries between disciplines such that they better facilitate, not restrict, experimental approaches and capabilities. In this essay, we address specific questions and critiques which have arisen in response to our research program, which lies at the intersection of developmental biology, computer science, and robotics. In the context of biological machines and robots, we explore changes across concepts and previously distinct fields that are driven by recent advances in materials, information, and life sciences. Herein, each author provides their own perspective on the subject, framed by their own disciplinary training. We argue that as with computation, certain aspects of developmental biology and robotics are not tied to specific materials; rather, the consilience of these fields can help to shed light on issues of multiscale control, self-assembly, and relationships between form and function. We hope new fields can emerge as boundaries arising from technological limitations are overcome, furthering practical applications from regenerative medicine to useful synthetic living machines.
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Affiliation(s)
- Douglas Blackiston
- Department of Biology, Allen Discovery Center at Tufts University, Medford, Massachusetts, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA
- Institute for Computationally Designed Organisms, Massachusetts and Vermont, USA
| | - Sam Kriegman
- Institute for Computationally Designed Organisms, Massachusetts and Vermont, USA
- Center for Robotics and Biosystems, Northwestern University, Evanston, Illinois, USA
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois, USA
| | - Josh Bongard
- Institute for Computationally Designed Organisms, Massachusetts and Vermont, USA
- Department of Computer Science, University of Vermont, Burlington, Vermont, USA
| | - Michael Levin
- Department of Biology, Allen Discovery Center at Tufts University, Medford, Massachusetts, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA
- Institute for Computationally Designed Organisms, Massachusetts and Vermont, USA
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Bongard J, Levin M. There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines. Biomimetics (Basel) 2023; 8:biomimetics8010110. [PMID: 36975340 PMCID: PMC10046700 DOI: 10.3390/biomimetics8010110] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/24/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
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Affiliation(s)
- Joshua Bongard
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, 200 Boston Ave., Suite 4600, Medford, MA 02155, USA
- Correspondence: ; Tel.: +(617)-627-6161
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Samarasinghe S, Minh-Thai TN. A Comprehensive conceptual and computational dynamics framework for autonomous regeneration of form and function in biological organisms. PNAS NEXUS 2023; 2:pgac308. [PMID: 36845351 PMCID: PMC9944231 DOI: 10.1093/pnasnexus/pgac308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
In biology, regeneration is a mysterious phenomenon that has inspired self-repairing systems, robots, and biobots. It is a collective computational process whereby cells communicate to achieve an anatomical set point and restore original function in regenerated tissue or the whole organism. Despite decades of research, the mechanisms involved in this process are still poorly understood. Likewise, the current algorithms are insufficient to overcome this knowledge barrier and enable advances in regenerative medicine, synthetic biology, and living machines/biobots. We propose a comprehensive conceptual framework for the engine of regeneration with hypotheses for the mechanisms and algorithms of stem cell-mediated regeneration that enables a system like the planarian flatworm to fully restore anatomical (form) and bioelectric (function) homeostasis from any small- or large-scale damage. The framework extends the available regeneration knowledge with novel hypotheses to propose collective intelligent self-repair machines with multi-level feedback neural control systems driven by somatic and stem cells. We computationally implemented the framework to demonstrate the robust recovery of both form and function (anatomical and bioelectric homeostasis) in an in silico worm that, in a simple way, resembles the planarian. In the absence of complete regeneration knowledge, the framework contributes to understanding and generating hypotheses for stem cell mediated form and function regeneration, which may help advance regenerative medicine and synthetic biology. Further, as our framework is a bio-inspired and bio-computing self-repair machine, it may be useful for building self-repair robots/biobots and artificial self-repair systems.
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Affiliation(s)
| | - Tran Nguyen Minh-Thai
- Precision Agriculture Team, Lincoln Agritech Limited, PO Box 69133, Lincoln, New Zealand
- Department of Information Systems, College of Information and Communication Technology, Can Tho University, 3/2 Street, Ninh Kieu District, Can Tho, Vietnam
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Clawson WP, Levin M. Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms. Biol J Linn Soc Lond 2022. [DOI: 10.1093/biolinnean/blac073] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Abstract
The rich variety of biological forms and behaviours results from one evolutionary history on Earth, via frozen accidents and selection in specific environments. This ubiquitous baggage in natural, familiar model species obscures the plasticity and swarm intelligence of cellular collectives. Significant gaps exist in our understanding of the origin of anatomical novelty, of the relationship between genome and form, and of strategies for control of large-scale structure and function in regenerative medicine and bioengineering. Analysis of living forms that have never existed before is necessary to reveal deep design principles of life as it can be. We briefly review existing examples of chimaeras, cyborgs, hybrots and other beings along the spectrum containing evolved and designed systems. To drive experimental progress in multicellular synthetic morphology, we propose teleonomic (goal-seeking, problem-solving) behaviour in diverse problem spaces as a powerful invariant across possible beings regardless of composition or origin. Cybernetic perspectives on chimaeric morphogenesis erase artificial distinctions established by past limitations of technology and imagination. We suggest that a multi-scale competency architecture facilitates evolution of robust problem-solving, living machines. Creation and analysis of novel living forms will be an essential testbed for the emerging field of diverse intelligence, with numerous implications across regenerative medicine, robotics and ethics.
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Affiliation(s)
| | - Michael Levin
- Allen Discovery Center at Tufts University , Medford, MA , USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University , Boston, MA , USA
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Minh-Thai TN, Samarasinghe S, Levin M. A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration Systems. ARTIFICIAL LIFE 2021; 27:80-104. [PMID: 34473826 DOI: 10.1162/artl_a_00343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Many biological organisms regenerate structure and function after damage. Despite the long history of research on molecular mechanisms, many questions remain about algorithms by which cells can cooperate towards the same invariant morphogenetic outcomes. Therefore, conceptual frameworks are needed not only for motivating hypotheses for advancing the understanding of regeneration processes in living organisms, but also for regenerative medicine and synthetic biology. Inspired by planarian regeneration, this study offers a novel generic conceptual framework that hypothesizes mechanisms and algorithms by which cell collectives may internally represent an anatomical target morphology towards which they build after damage. Further, the framework contributes a novel nature-inspired computing method for self-repair in engineering and robotics. Our framework, based on past in vivo and in silico studies on planaria, hypothesizes efficient novel mechanisms and algorithms to achieve complete and accurate regeneration of a simple in silico flatwormlike organism from any damage, much like the body-wide immortality of planaria, with minimal information and algorithmic complexity. This framework that extends our previous circular tissue repair model integrates two levels of organization: tissue and organism. In Level 1, three individual in silico tissues (head, body, and tail-each with a large number of tissue cells and a single stem cell at the centre) repair themselves through efficient local communications. Here, the contribution extends our circular tissue model to other shapes and invests them with tissue-wide immortality through an information field holding the minimum body plan. In Level 2, individual tissues combine to form a simple organism. Specifically, the three stem cells form a network that coordinates organism-wide regeneration with the help of Level 1. Here we contribute novel concepts for collective decision-making by stem cells for stem cell regeneration and large-scale recovery. Both levels (tissue cells and stem cells) represent networks that perform simple neural computations and form a feedback control system. With simple and limited cellular computations, our framework minimises computation and algorithmic complexity to achieve complete recovery. We report results from computer simulations of the framework to demonstrate its robustness in recovering the organism after any injury. This comprehensive hypothetical framework that significantly extends the existing biological regeneration models offers a new way to conceptualise the information-processing aspects of regeneration, which may also help design living and non-living self-repairing agents.
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Affiliation(s)
- Tran Nguyen Minh-Thai
- Lincoln University, Complex Systems, Big Data and Informatics Initiative (CSBII)
- Can Tho University, College of Information and Communication Technology
| | - Sandhya Samarasinghe
- Lincoln University, Complex Systems, Big Data and Informatics Initiative (CSBII).
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8
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Murugan NJ, Kaltman DH, Jin PH, Chien M, Martinez R, Nguyen CQ, Kane A, Novak R, Ingber DE, Levin M. Mechanosensation Mediates Long-Range Spatial Decision-Making in an Aneural Organism. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2008161. [PMID: 34263487 DOI: 10.1002/adma.202008161] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 06/14/2021] [Indexed: 05/25/2023]
Abstract
The unicellular protist Physarum polycephalum is an important emerging model for understanding how aneural organisms process information toward adaptive behavior. Here, it is revealed that Physarum can use mechanosensation to reliably make decisions about distant objects in its environment, preferentially growing in the direction of heavier, substrate-deforming, but chemically inert masses. This long-range sensing is abolished by gentle rhythmic mechanical disruption, changing substrate stiffness, or the addition of an inhibitor of mechanosensitive transient receptor potential channels. Additionally, it is demonstrated that Physarum does not respond to the absolute magnitude of strain. Computational modeling reveales that Physarum may perform this calculation by sensing the fraction of its perimeter that is distorted above a threshold substrate strain-a fundamentally novel method of mechanosensation. Using its body as both a distributed sensor array and computational substrate, this aneural organism leverages its unique morphology to make long-range decisions. Together, these data identify a surprising behavioral preference relying on biomechanical features and quantitatively characterize how the Physarum exploits physics to adaptively regulate its growth and shape.
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Affiliation(s)
- Nirosha J Murugan
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
| | - Daniel H Kaltman
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
| | - Paul H Jin
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
| | - Melanie Chien
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
| | - Ramses Martinez
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA, 02115, USA
| | - Cuong Q Nguyen
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
| | - Anna Kane
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA, 02115, USA
| | - Richard Novak
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA, 02115, USA
| | - Donald E Ingber
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA, 02115, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02115, USA
- Vascular Biology Program and Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, 200 College Avenue, Medford, MA, 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA, 02115, USA
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9
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Abstract
Increased control of biological growth and form is an essential gateway to transformative medical advances. Repairing of birth defects, restoring lost or damaged organs, normalizing tumors, all depend on understanding how cells cooperate to make specific, functional large-scale structures. Despite advances in molecular genetics, significant gaps remain in our understanding of the meso-scale rules of morphogenesis. An engineering approach to this problem is the creation of novel synthetic living forms, greatly extending available model systems beyond evolved plant and animal lineages. Here, we review recent advances in the emerging field of synthetic morphogenesis, the bioengineering of novel multicellular living bodies. Emphasizing emergent self-organization, tissue-level guided self-assembly, and active functionality, this work is the essential next generation of synthetic biology. Aside from useful living machines for specific functions, the rational design and analysis of new, coherent anatomies will greatly increase our understanding of foundational questions in evolutionary developmental and cell biology.
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Affiliation(s)
- Mo R. Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, A809B Scaife Hall, 3550 Terrace Street, Pittsburgh, PA 15261, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
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10
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Shah D, Yang B, Kriegman S, Levin M, Bongard J, Kramer-Bottiglio R. Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2002882. [PMID: 32954582 DOI: 10.1002/adma.202002882] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/01/2020] [Accepted: 06/08/2020] [Indexed: 06/11/2023]
Abstract
One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for artificial systems that must robustly operate in the natural world. Recently, researchers have begun to leverage insights from regenerating and metamorphosing organisms, designing robots capable of editing their own structure to more efficiently perform tasks under changing demands and creating new algorithms to control these changing anatomies. Here, an overview of the literature related to robots that change shape to enhance and expand their functionality is presented. Related grand challenges, including shape sensing, finding, and changing, which rely on innovations in multifunctional materials, distributed actuation and sensing, and somatic control to enable next-generation shape changing robots are also discussed.
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Affiliation(s)
- Dylan Shah
- School of Engineering & Applied Science, Yale University, 9 Hillhouse Avenue, New Haven, CT, 06511, USA
| | - Bilige Yang
- School of Engineering & Applied Science, Yale University, 9 Hillhouse Avenue, New Haven, CT, 06511, USA
| | - Sam Kriegman
- Department of Computer Science, University of Vermont, E428 Innovation Hall, Burlington, VT, 05405, USA
| | - Michael Levin
- Department of Biology, Allen Discovery Center at Tufts University, Tufts University, 200 Boston Ave. Suite 4604, Medford, MA, 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Cir, Boston, MA, 02115, USA
| | - Josh Bongard
- Department of Computer Science, University of Vermont, E428 Innovation Hall, Burlington, VT, 05405, USA
| | - Rebecca Kramer-Bottiglio
- School of Engineering & Applied Science, Yale University, 9 Hillhouse Avenue, New Haven, CT, 06511, USA
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11
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Howison T, Hauser S, Hughes J, Iida F. Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review. ARTIFICIAL LIFE 2021; 26:484-506. [PMID: 33493077 DOI: 10.1162/artl_a_00330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.
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Affiliation(s)
- Toby Howison
- University of Cambridge, Bio-Inspired Robotics Lab.
| | - Simon Hauser
- University of Cambridge, Bio-Inspired Robotics Lab
| | - Josie Hughes
- University of Cambridge, Bio-Inspired Robotics Lab
| | - Fumiya Iida
- University of Cambridge, Bio-Inspired Robotics Lab
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12
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Life, death, and self: Fundamental questions of primitive cognition viewed through the lens of body plasticity and synthetic organisms. Biochem Biophys Res Commun 2020; 564:114-133. [PMID: 33162026 DOI: 10.1016/j.bbrc.2020.10.077] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/25/2020] [Accepted: 10/28/2020] [Indexed: 12/16/2022]
Abstract
Central to the study of cognition is being able to specify the Subject that is making decisions and owning memories and preferences. However, all real cognitive agents are made of parts (such as brains made of cells). The integration of many active subunits into a coherent Self appearing at a larger scale of organization is one of the fundamental questions of evolutionary cognitive science. Typical biological model systems, whether basal or advanced, have a static anatomical structure which obscures important aspects of the mind-body relationship. Recent advances in bioengineering now make it possible to assemble, disassemble, and recombine biological structures at the cell, organ, and whole organism levels. Regenerative biology and controlled chimerism reveal that studies of cognition in intact, "standard", evolved animal bodies are just a narrow slice of a much bigger and as-yet largely unexplored reality: the incredible plasticity of dynamic morphogenesis of biological forms that house and support diverse types of cognition. The ability to produce living organisms in novel configurations makes clear that traditional concepts, such as body, organism, genetic lineage, death, and memory are not as well-defined as commonly thought, and need considerable revision to account for the possible spectrum of living entities. Here, I review fascinating examples of experimental biology illustrating that the boundaries demarcating somatic and cognitive Selves are fluid, providing an opportunity to sharpen inquiries about how evolution exploits physical forces for multi-scale cognition. Developmental (pre-neural) bioelectricity contributes a novel perspective on how the dynamic control of growth and form of the body evolved into sophisticated cognitive capabilities. Most importantly, the development of functional biobots - synthetic living machines with behavioral capacity - provides a roadmap for greatly expanding our understanding of the origin and capacities of cognition in all of its possible material implementations, especially those that emerge de novo, with no lengthy evolutionary history of matching behavioral programs to bodyplan. Viewing fundamental questions through the lens of new, constructed living forms will have diverse impacts, not only in basic evolutionary biology and cognitive science, but also in regenerative medicine of the brain and in artificial intelligence.
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Hockings N, Howard D. New Biological Morphogenetic Methods for Evolutionary Design of Robot Bodies. Front Bioeng Biotechnol 2020; 8:621. [PMID: 32637404 PMCID: PMC7317032 DOI: 10.3389/fbioe.2020.00621] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/20/2020] [Indexed: 11/18/2022] Open
Abstract
We present some currently unused morphogenetic mechanisms from evolutionary biology and guidelines for transfer to evolutionary robotics. (1) DNA patterns providing mutation of mutability, lead to canalization of evolvable bauplans, via kin selection. (2) Morphogenetic mechanisms (i) Epigenetic cell lines provide functional cell types, and identification of cell descent. (ii) Local anatomical coordinates based on diffusion of morphogens, facilitate evolvable genetic parameterizations of complex phenotypes (iii) Remodeling in response to mechanical forces facilitates robust production of well-integrated phenotypes of greater complexity than the genome. An approach is proposed for the tractable application of mutation-of-mutability and morphogenetic mechanisms in evolutionary robotics. The purpose of these methods, is to facilitate production of robot mechanisms of the subtlety, efficiency, and efficacy of the musculoskeletal and dermal systems of animals.
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Affiliation(s)
- Nick Hockings
- Robotics and Autonomous Systems Group, Cyber-Physical Systems, Data61, Commonwealth Scientific and Industrial Research Organization (CSIRO), Pullenvale, QLD, Australia
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14
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Van der Mude A. Structure encoding in DNA. J Theor Biol 2020; 492:110205. [PMID: 32070719 DOI: 10.1016/j.jtbi.2020.110205] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 12/29/2019] [Accepted: 02/14/2020] [Indexed: 12/21/2022]
Abstract
It is proposed that transposons and related long non-coding RNA define the fine structure of body parts. Although morphogens have long been known to direct the formation of many gross structures in early embryonic development, they do not have the necessary precision to define a structure down to the individual cellular level. Using the distinction between procedural and declarative knowledge in information processing as an analogy, it is hypothesized that DNA encodes fine structure in a manner that is different from the genetic code for proteins. The hypothesis states that repeated or near-repeated sequences that are in transposons and non-coding RNA define body part structures. As the cells in a body part go through the epigenetic process of differentiation, the action of methylation serves to inactivate all but the relevant structure definitions and some associated cell type genes. The transposons left active will then physically modify the DNA sequence in the heterochromatin to establish the local context in the three-dimensional body part structure. This brings the encoded definition of the cell type to the histone. The histone code for that cell type starts the regulatory cascade that turns on the genes associated with that particular type of cell, transforming it from a multipotent cell to a fully differentiated cell. This mechanism creates structures in the musculoskeletal system, the organs of the body, the major parts of the brain, and other systems.
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15
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Pietak A, Bischof J, LaPalme J, Morokuma J, Levin M. Neural control of body-plan axis in regenerating planaria. PLoS Comput Biol 2019; 15:e1006904. [PMID: 30990801 PMCID: PMC6485777 DOI: 10.1371/journal.pcbi.1006904] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 04/26/2019] [Accepted: 02/26/2019] [Indexed: 01/01/2023] Open
Abstract
Control of axial polarity during regeneration is a crucial open question. We developed a quantitative model of regenerating planaria, which elucidates self-assembly mechanisms of morphogen gradients required for robust body-plan control. The computational model has been developed to predict the fraction of heteromorphoses expected in a population of regenerating planaria fragments subjected to different treatments, and for fragments originating from different regions along the anterior-posterior and medio-lateral axis. This allows for a direct comparison between computational and experimental regeneration outcomes. Vector transport of morphogens was identified as a fundamental requirement to account for virtually scale-free self-assembly of the morphogen gradients observed in planarian homeostasis and regeneration. The model correctly describes altered body-plans following many known experimental manipulations, and accurately predicts outcomes of novel cutting scenarios, which we tested. We show that the vector transport field coincides with the alignment of nerve axons distributed throughout the planarian tissue, and demonstrate that the head-tail axis is controlled by the net polarity of neurons in a regenerating fragment. This model provides a comprehensive framework for mechanistically understanding fundamental aspects of body-plan regulation, and sheds new light on the role of the nervous system in directing growth and form.
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Affiliation(s)
- Alexis Pietak
- Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America
| | - Johanna Bischof
- Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America
- Department of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Joshua LaPalme
- Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America
- Department of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Junji Morokuma
- Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America
- Department of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, Massachusetts, United States of America
- Department of Biology, Tufts University, Medford, Massachusetts, United States of America
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16
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Levin M, Pietak AM, Bischof J. Planarian regeneration as a model of anatomical homeostasis: Recent progress in biophysical and computational approaches. Semin Cell Dev Biol 2019; 87:125-144. [PMID: 29635019 PMCID: PMC6234102 DOI: 10.1016/j.semcdb.2018.04.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/03/2018] [Accepted: 04/06/2018] [Indexed: 12/22/2022]
Abstract
Planarian behavior, physiology, and pattern control offer profound lessons for regenerative medicine, evolutionary biology, morphogenetic engineering, robotics, and unconventional computation. Despite recent advances in the molecular genetics of stem cell differentiation, this model organism's remarkable anatomical homeostasis provokes us with truly fundamental puzzles about the origin of large-scale shape and its relationship to the genome. In this review article, we first highlight several deep mysteries about planarian regeneration in the context of the current paradigm in this field. We then review recent progress in understanding of the physiological control of an endogenous, bioelectric pattern memory that guides regeneration, and how modulating this memory can permanently alter the flatworm's target morphology. Finally, we focus on computational approaches that complement reductive pathway analysis with synthetic, systems-level understanding of morphological decision-making. We analyze existing models of planarian pattern control and highlight recent successes and remaining knowledge gaps in this interdisciplinary frontier field.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA 02155, United States; Biology Department, Tufts University, Medford, MA 02155, United States.
| | - Alexis M Pietak
- Allen Discovery Center at Tufts University, Medford, MA 02155, United States
| | - Johanna Bischof
- Allen Discovery Center at Tufts University, Medford, MA 02155, United States; Biology Department, Tufts University, Medford, MA 02155, United States
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17
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Cussat-Blanc S, Harrington K, Banzhaf W. Artificial Gene Regulatory Networks-A Review. ARTIFICIAL LIFE 2019; 24:296-328. [PMID: 30681915 DOI: 10.1162/artl_a_00267] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.
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Affiliation(s)
| | - Kyle Harrington
- University of Idaho, Computational and Physical Systems Group, Virtual Technology and Design.
| | - Wolfgang Banzhaf
- Michigan State University, BEACON Center for the Study of Evolution in Action, Department of Computer Science and Engineering.
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18
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Pietak A, Levin M. Bioelectrical control of positional information in development and regeneration: A review of conceptual and computational advances. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 137:52-68. [PMID: 29626560 PMCID: PMC10464501 DOI: 10.1016/j.pbiomolbio.2018.03.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 03/23/2018] [Accepted: 03/26/2018] [Indexed: 12/16/2022]
Abstract
Positional information describes pre-patterns of morphogenetic substances that alter spatio-temporal gene expression to instruct development of growth and form. A wealth of recent data indicate bioelectrical properties, such as the transmembrane potential (Vmem), are involved as instructive signals in the spatiotemporal regulation of morphogenesis. However, the mechanistic relationships between Vmem and molecular positional information are only beginning to be understood. Recent advances in computational modeling are assisting in the development of comprehensive frameworks for mechanistically understanding how endogenous bioelectricity can guide anatomy in a broad range of systems. Vmem represents an extraordinarily strong electric field (∼1.0 × 106 V/m) active over the thin expanse of the plasma membrane, with the capacity to influence a variety of downstream molecular signaling cascades. Moreover, in multicellular networks, intercellular coupling facilitated by gap junction channels may induce directed, electrodiffusive transport of charged molecules between cells of the network to generate new positional information patterning possibilities and characteristics. Given the demonstrated role of Vmem in morphogenesis, here we review current understanding of how Vmem can integrate with molecular regulatory networks to control single cell state, and the unique properties bioelectricity adds to transport phenomena in gap junction-coupled cell networks to facilitate self-assembly of morphogen gradients and other patterns. Understanding how Vmem integrates with biochemical regulatory networks at the level of a single cell, and mechanisms through which Vmem shapes molecular positional information in multicellular networks, are essential for a deep understanding of body plan control in development, regeneration and disease.
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Affiliation(s)
| | - Michael Levin
- Allen Discovery Center at Tufts, USA; Center for Regenerative and Developmental Biology, Tufts University, Medford, MA, USA
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19
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Levin M, Martyniuk CJ. The bioelectric code: An ancient computational medium for dynamic control of growth and form. Biosystems 2018; 164:76-93. [PMID: 28855098 PMCID: PMC10464596 DOI: 10.1016/j.biosystems.2017.08.009] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/20/2017] [Accepted: 08/22/2017] [Indexed: 12/19/2022]
Abstract
What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, Biology Department, Tufts University, 200 Boston Avenue, Suite 4600 Medford, MA 02155, USA.
| | - Christopher J Martyniuk
- Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida Genetics Institute, Interdisciplinary Program in Biomedical Sciences Neuroscience, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32611, USA
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20
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Vujovic V, Rosendo A, Brodbeck L, Iida F. Evolutionary Developmental Robotics: Improving Morphology and Control of Physical Robots. ARTIFICIAL LIFE 2017; 23:169-185. [PMID: 28513207 DOI: 10.1162/artl_a_00228] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated during the evolutionary process and autonomously built using additive fabrication. We present two cases with evo-devo experiments and one with evolution, and we hypothesize that the addition of a developmental stage can be used within robotics to improve performance. Moreover, our results show that a nonlinear system-environment interaction exists, which explains the nontrivial locomotion patterns observed. In the future, robots will be present in our daily lives, and this work introduces for the first time physical robots that evolve and grow while interacting with the environment.
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Affiliation(s)
- Vuk Vujovic
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Andre Rosendo
- Contact author
- Department of Engineering, Trumpington Street, The University of Cambridge, Cambridge, CB21PZ, UK. E-mail: (A.R.)
| | - Luzius Brodbeck
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Fumiya Iida
- Department of Engineering, Trumpington Street, The University of Cambridge, Cambridge, CB21PZ, UK. E-mail: (A.R.)
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21
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Pascalie J, Potier M, Kowaliw T, Giavitto JL, Michel O, Spicher A, Doursat R. Developmental Design of Synthetic Bacterial Architectures by Morphogenetic Engineering. ACS Synth Biol 2016; 5:842-61. [PMID: 27244532 DOI: 10.1021/acssynbio.5b00246] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Synthetic biology is an emerging scientific field that promotes the standardized manufacturing of biological components without natural equivalents. Its goal is to create artificial living systems that can meet various needs in health care or energy domains. While most works are focused on the individual bacterium as a chemical reactor, our project, SynBioTIC, addresses a novel and more complex challenge: shape engineering; that is, the redesign of natural morphogenesis toward a new kind of developmental 3D printing. Potential applications include organ growth, natural computing in biocircuits, or future vegetal houses. To create in silico multicellular organisms that exhibit specific shapes, we construe their development as an iterative process combining fundamental collective phenomena such as homeostasis, patterning, segmentation, and limb growth. Our numerical experiments rely on the existing Escherichia coli simulator Gro, a physicochemical computation platform offering reaction-diffusion and collision dynamics solvers. The synthetic bioware of our model executes a set of rules, or genome, in each cell. Cells can differentiate into several predefined types associated with specific actions (divide, emit signal, detect signal, die). Transitions between types are triggered by conditions involving internal and external sensors that detect various protein levels inside and around the cell. Indirect communication between bacteria is relayed by morphogen diffusion and the mechanical constraints of 2D packing. Starting from a single bacterium, the overall architecture emerges in a purely endogenous fashion through a series of developmental stages, inlcuding proliferation, differentiation, morphogen diffusion, and synchronization. The genome can be parametrized to control the growth and features of appendages individually. As exemplified by the L and T shapes that we obtain, certain precursor cells can be inhibited while others can create limbs of varying size (divergence of the homology). Such morphogenetic phenotypes open the way to more complex shapes made of a recursive array of core bodies and limbs and, most importantly, to an evolutionary developmental exploration of unplanned functional forms.
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Affiliation(s)
- Jonathan Pascalie
- Complex Systems
Institute, Paris Ile-de-France (ISC-PIF), CNRS UPS3611, Paris, France
- Algorithmic,
Complexity and Logic Laboratory (LACL), Université Paris-Est Créteil, Créteil, France
- Computer
Science Research Institute (IRIT), CNRS UMR5505, Université de Toulouse, Toulouse, France
| | - Martin Potier
- Algorithmic,
Complexity and Logic Laboratory (LACL), Université Paris-Est Créteil, Créteil, France
| | - Taras Kowaliw
- Complex Systems
Institute, Paris Ile-de-France (ISC-PIF), CNRS UPS3611, Paris, France
| | - Jean-Louis Giavitto
- Institute for Research
and Coordination Acoustic/Music (IRCAM), CNRS UMR9912, Paris, France
| | - Olivier Michel
- Algorithmic,
Complexity and Logic Laboratory (LACL), Université Paris-Est Créteil, Créteil, France
| | - Antoine Spicher
- Algorithmic,
Complexity and Logic Laboratory (LACL), Université Paris-Est Créteil, Créteil, France
| | - René Doursat
- Complex Systems
Institute, Paris Ile-de-France (ISC-PIF), CNRS UPS3611, Paris, France
- Informatics
Research Centre (IRC), Manchester Metropolitan University, Manchester M1 5GD, U.K
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22
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Pietak A, Levin M. Exploring Instructive Physiological Signaling with the Bioelectric Tissue Simulation Engine. Front Bioeng Biotechnol 2016; 4:55. [PMID: 27458581 PMCID: PMC4933718 DOI: 10.3389/fbioe.2016.00055] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Accepted: 06/21/2016] [Indexed: 12/23/2022] Open
Abstract
Bioelectric cell properties have been revealed as powerful targets for modulating stem cell function, regenerative response, developmental patterning, and tumor reprograming. Spatio-temporal distributions of endogenous resting potential, ion flows, and electric fields are influenced not only by the genome and external signals but also by their own intrinsic dynamics. Ion channels and electrical synapses (gap junctions) both determine, and are themselves gated by, cellular resting potential. Thus, the origin and progression of bioelectric patterns in multicellular tissues is complex, which hampers the rational control of voltage distributions for biomedical interventions. To improve understanding of these dynamics and facilitate the development of bioelectric pattern control strategies, we developed the BioElectric Tissue Simulation Engine (BETSE), a finite volume method multiphysics simulator, which predicts bioelectric patterns and their spatio-temporal dynamics by modeling ion channel and gap junction activity and tracking changes to the fundamental property of ion concentration. We validate performance of the simulator by matching experimentally obtained data on membrane permeability, ion concentration and resting potential to simulated values, and by demonstrating the expected outcomes for a range of well-known cases, such as predicting the correct transmembrane voltage changes for perturbation of single cell membrane states and environmental ion concentrations, in addition to the development of realistic transepithelial potentials and bioelectric wounding signals. In silico experiments reveal factors influencing transmembrane potential are significantly different in gap junction-networked cell clusters with tight junctions, and identify non-linear feedback mechanisms capable of generating strong, emergent, cluster-wide resting potential gradients. The BETSE platform will enable a deep understanding of local and long-range bioelectrical dynamics in tissues, and assist the development of specific interventions to achieve greater control of pattern during morphogenesis and remodeling.
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Affiliation(s)
- Alexis Pietak
- Allen Discovery Center at Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, USA
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23
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Durant F, Lobo D, Hammelman J, Levin M. Physiological controls of large-scale patterning in planarian regeneration: a molecular and computational perspective on growth and form. REGENERATION (OXFORD, ENGLAND) 2016; 3:78-102. [PMID: 27499881 PMCID: PMC4895326 DOI: 10.1002/reg2.54] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 02/18/2016] [Accepted: 02/22/2016] [Indexed: 12/12/2022]
Abstract
Planaria are complex metazoans that repair damage to their bodies and cease remodeling when a correct anatomy has been achieved. This model system offers a unique opportunity to understand how large-scale anatomical homeostasis emerges from the activities of individual cells. Much progress has been made on the molecular genetics of stem cell activity in planaria. However, recent data also indicate that the global pattern is regulated by physiological circuits composed of ionic and neurotransmitter signaling. Here, we overview the multi-scale problem of understanding pattern regulation in planaria, with specific focus on bioelectric signaling via ion channels and gap junctions (electrical synapses), and computational efforts to extract explanatory models from functional and molecular data on regeneration. We present a perspective that interprets results in this fascinating field using concepts from dynamical systems theory and computational neuroscience. Serving as a tractable nexus between genetic, physiological, and computational approaches to pattern regulation, planarian pattern homeostasis harbors many deep insights for regenerative medicine, evolutionary biology, and engineering.
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Affiliation(s)
- Fallon Durant
- Department of Biology, Allen Discovery Center at Tufts University, Tufts Center for Regenerative and Developmental BiologyTufts UniversityMA02155USA
| | - Daniel Lobo
- Department of Biological SciencesUniversity of MarylandBaltimore County, 1000 Hilltop CircleBaltimoreMD21250USA
| | - Jennifer Hammelman
- Department of Biology, Allen Discovery Center at Tufts University, Tufts Center for Regenerative and Developmental BiologyTufts UniversityMA02155USA
| | - Michael Levin
- Department of Biology, Allen Discovery Center at Tufts University, Tufts Center for Regenerative and Developmental BiologyTufts UniversityMA02155USA
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24
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Law R, Levin M. Bioelectric memory: modeling resting potential bistability in amphibian embryos and mammalian cells. Theor Biol Med Model 2015; 12:22. [PMID: 26472354 PMCID: PMC4608135 DOI: 10.1186/s12976-015-0019-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 09/27/2015] [Indexed: 12/20/2022] Open
Abstract
Background Bioelectric gradients among all cells, not just within excitable nerve and muscle, play instructive roles in developmental and regenerative pattern formation. Plasma membrane resting potential gradients regulate cell behaviors by regulating downstream transcriptional and epigenetic events. Unlike neurons, which fire rapidly and typically return to the same polarized state, developmental bioelectric signaling involves many cell types stably maintaining various levels of resting potential during morphogenetic events. It is important to begin to quantitatively model the stability of bioelectric states in cells, to understand computation and pattern maintenance during regeneration and remodeling. Method To facilitate the analysis of endogenous bioelectric signaling and the exploitation of voltage-based cellular controls in synthetic bioengineering applications, we sought to understand the conditions under which somatic cells can stably maintain distinct resting potential values (a type of state memory). Using the Channelpedia ion channel database, we generated an array of amphibian oocyte and mammalian membrane models for voltage evolution. These models were analyzed and searched, by simulation, for a simple dynamical property, multistability, which forms a type of voltage memory. Results We find that typical mammalian models and amphibian oocyte models exhibit bistability when expressing different ion channel subsets, with either persistent sodium or inward-rectifying potassium, respectively, playing a facilitative role in bistable memory formation. We illustrate this difference using fast sodium channel dynamics for which a comprehensive theory exists, where the same model exhibits bistability under mammalian conditions but not amphibian conditions. In amphibians, potassium channels from the Kv1.x and Kv2.x families tend to disrupt this bistable memory formation. We also identify some common principles under which physiological memory emerges, which suggest specific strategies for implementing memories in bioengineering contexts. Conclusion Our results reveal conditions under which cells can stably maintain one of several resting voltage potential values. These models suggest testable predictions for experiments in developmental bioelectricity, and illustrate how cells can be used as versatile physiological memory elements in synthetic biology, and unconventional computation contexts. Electronic supplementary material The online version of this article (doi:10.1186/s12976-015-0019-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert Law
- Department of Neuroscience, Brown University, Box G, Providence, RI, 02912, USA.
| | - Michael Levin
- Department of Biology and Tufts Center for Regenerative and Developmental Biology, Tufts University, 200 Boston Avenue, Medford, MA, 02155, USA.
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25
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Programming the emergence in morphogenetically architected complex systems. Acta Biotheor 2015; 63:295-308. [PMID: 26024971 DOI: 10.1007/s10441-015-9262-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Accepted: 05/25/2015] [Indexed: 10/23/2022]
Abstract
Large sets of elements interacting locally and producing specific architectures reliably form a category that transcends the usual dividing line between biological and engineered systems. We propose to call them morphogenetically architected complex systems (MACS). While taking the emergence of properties seriously, the notion of MACS enables at the same time the design (or "meta-design") of operational means that allow controlling and even, paradoxically, programming this emergence. To demonstrate our claim, we first show that among all the self-organized systems studied in the field of Artificial Life, the specificity of MACS essentially lies in the close relation between their emergent properties and functional properties. Second, we argue that to be a MACS a system does not need to display more than weak emergent properties. Third, since the notion of weak emergence is based on the possibility of simulation, whether computational or mechanistic via machines, we see MACS as good candidates to help design artificial self-architected systems (such as robotic swarms) but also harness and redesign living ones (such as synthetic bacterial films).
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Blackiston DJ, Shomrat T, Levin M. The stability of memories during brain remodeling: A perspective. Commun Integr Biol 2015; 8:e1073424. [PMID: 27066165 PMCID: PMC4802789 DOI: 10.1080/19420889.2015.1073424] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 07/13/2015] [Indexed: 01/10/2023] Open
Abstract
One of the most important features of the nervous system is memory: the ability to represent and store experiences, in a manner that alters behavior and cognition at future times when the original stimulus is no longer present. However, the brain is not always an anatomically stable structure: many animal species regenerate all or part of the brain after severe injury, or remodel their CNS toward a new configuration as part of their life cycle. This raises a fascinating question: what are the dynamics of memories during brain regeneration? Can stable memories remain intact when cellular turnover and spatial rearrangement modify the biological hardware within which experiences are stored? What can we learn from model species that exhibit both, regeneration and memory, with respect to robustness and stability requirements for long-term memories encoded in living tissues? In this Perspective, we discuss relevant data in regenerating planaria, metamorphosing insects, and hibernating ground squirrels. While much remains to be done to understand this remarkable process, molecular-level insight will have important implications for cognitive science, regenerative medicine of the brain, and the development of non-traditional computational media in synthetic bioengineering.
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Affiliation(s)
- Douglas J Blackiston
- Center for Regenerative and Developmental Biology and Department of Biology; Tufts University ; Medford, MA USA
| | - Tal Shomrat
- Department of Neurobiology; Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus; Jerusalem, Israel; School of Marine Sciences, Ruppin Academic Center; Michmoret, Israel
| | - Michael Levin
- Center for Regenerative and Developmental Biology and Department of Biology; Tufts University ; Medford, MA USA
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27
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Brodbeck L, Hauser S, Iida F. Morphological Evolution of Physical Robots through Model-Free Phenotype Development. PLoS One 2015; 10:e0128444. [PMID: 26091255 PMCID: PMC4474803 DOI: 10.1371/journal.pone.0128444] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 04/27/2015] [Indexed: 11/18/2022] Open
Abstract
Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.
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Affiliation(s)
- Luzius Brodbeck
- Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- * E-mail:
| | - Simon Hauser
- Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland
- Biorobotics Laboratory, EPFL—Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Fumiya Iida
- Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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28
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Friston K, Levin M, Sengupta B, Pezzulo G. Knowing one's place: a free-energy approach to pattern regulation. J R Soc Interface 2015; 12:20141383. [PMID: 25788538 PMCID: PMC4387527 DOI: 10.1098/rsif.2014.1383] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 02/24/2015] [Indexed: 01/15/2023] Open
Abstract
Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization-of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs-and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model-predicting the signals sensed by cells in the target morphology-and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies-that currently focus on molecular pathways-with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics.
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Affiliation(s)
- Karl Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, Queen Square, London, UK
| | - Michael Levin
- Biology Department, Center for Regenerative and Developmental Biology, Tufts University, Medford, USA
| | - Biswa Sengupta
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, Queen Square, London, UK
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
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