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Cervera J, Manzanares JA, Levin M, Mafe S. Oscillatory phenomena in electrophysiological networks: The coupling between cell bioelectricity and transcription. Comput Biol Med 2024; 180:108964. [PMID: 39106669 DOI: 10.1016/j.compbiomed.2024.108964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 07/04/2024] [Accepted: 07/27/2024] [Indexed: 08/09/2024]
Abstract
Morphogenetic regulation during embryogenesis and regeneration rely on information transfer and coordination between different regions. Here, we explore theoretically the coupling between bioelectrical and transcriptional oscillations at the individual cell and multicellular levels. The simulations, based on a set of ion channels and intercellular gap junctions, show that bioelectrical and transcriptional waves can electrophysiologically couple distant regions of a model network in phase and antiphase oscillatory states that include synchronization phenomena. In this way, different multicellular regionalizations can be encoded by cell potentials that oscillate between depolarized and polarized states, thus allowing a spatio-temporal coding. Because the electric potential patterns characteristic of development and regeneration are correlated with the spatial distributions of signaling ions and molecules, bioelectricity can act as a template for slow biochemical signals following a hierarchy of experimental times. In particular, bioelectrical gradients that couple cell potentials to transcription rates give to each single cell a rough idea of its location in the multicellular ensemble, thus controlling local differentiation processes that switch on and off crucial parts of the genome.
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Affiliation(s)
- Javier Cervera
- Dept. Termodinàmica, Facultat de Física, Universitat de València, 46100, Burjassot, Spain.
| | - José A Manzanares
- Dept. Termodinàmica, Facultat de Física, Universitat de València, 46100, Burjassot, Spain
| | - Michael Levin
- Dept. of Biology, Tufts University, Medford, MA, 02155, USA; Allen Discovery Center at Tufts University, Medford, MA, 02155, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02215, USA
| | - Salvador Mafe
- Dept. Termodinàmica, Facultat de Física, Universitat de València, 46100, Burjassot, Spain; Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
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Aminzare Z, Kay AR. Mathematical modeling of intracellular osmolarity and cell volume stabilization: The Donnan effect and ion transport. J Gen Physiol 2024; 156:e202413554. [PMID: 38995224 PMCID: PMC11247275 DOI: 10.1085/jgp.202413554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/01/2024] [Accepted: 06/13/2024] [Indexed: 07/13/2024] Open
Abstract
The presence of impermeant molecules within a cell can lead to an increase in cell volume through the influx of water driven by osmosis. This phenomenon is known as the Donnan (or Gibbs-Donnan) effect. Animal cells actively transport ions to counteract the Donnan effect and regulate their volume, actively pumping Na+ out and K+ into their cytosol using the Na+/K+ ATPase (NKA) pump. The pump-leak equations (PLEs) are a system of algebraic-differential equations to model the membrane potential, ion (Na+, K+, and Cl-), and water flux across the cell membrane, which provide insight into how the combination of passive ions fluxes and active transport contribute to stabilizing cell volume. Our broad objective is to provide analytical insight into the PLEs through three lines of investigation: (1) we show that the provision of impermeant extracellular molecules can stabilize the volume of a passive cell; (2) we demonstrate that the mathematical form of the NKA pump is not as important as the stoichiometry for cell stabilization; and (3) we investigate the interaction between the NKA pump and cation-chloride co-transporters (CCCs) on cell stabilization, showing that NCC can destabilize a cell while NKCC and KCC can stabilize it. We incorporate extracellular impermeant molecules, NKA pump, and CCCs into the PLEs and derive the exact formula for the steady states in terms of all the parameters. This analytical expression enables us to easily explore the effect of each of the system parameters on the existence and stability of the steady states.
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Affiliation(s)
- Zahra Aminzare
- Department of Mathematics, University of Iowa, Iowa City, IA, USA
| | - Alan R. Kay
- Department of Biology, University of Iowa, Iowa City, IA, USA
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Friston K, Friedman DA, Constant A, Knight VB, Fields C, Parr T, Campbell JO. A Variational Synthesis of Evolutionary and Developmental Dynamics. ENTROPY (BASEL, SWITZERLAND) 2023; 25:964. [PMID: 37509911 PMCID: PMC10378262 DOI: 10.3390/e25070964] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 07/30/2023]
Abstract
This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from-and influence-each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain-and are constrained by-fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses-and identify points of contact with related mathematical formulations of evolution.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1E 6AP, UK
| | - Daniel A Friedman
- Department of Entomology and Nematology, University of California, Davis, Davis, CA 95616, USA
- Active Inference Institute, Davis, CA 95616, USA
| | - Axel Constant
- Theory and Method in Biosciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - V Bleu Knight
- Active Inference Institute, Davis, CA 95616, USA
- Department of Biology, New Mexico State University, Las Cruces, NM 88003, USA
| | - Chris Fields
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1E 6AP, UK
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Levin M. Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology. Cell Mol Life Sci 2023; 80:142. [PMID: 37156924 PMCID: PMC10167196 DOI: 10.1007/s00018-023-04790-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 05/10/2023]
Abstract
A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, 200 Boston Ave. 334 Research East, Medford, MA, 02155, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, 3 Blackfan St., Boston, MA, 02115, 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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Shreesha L, Levin M. Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25010131. [PMID: 36673272 PMCID: PMC9858125 DOI: 10.3390/e25010131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 05/25/2023]
Abstract
Biological genotypes do not code directly for phenotypes; developmental physiology is the control layer that separates genomes from capacities ascertained by selection. A key aspect is cellular competency, since cells are not passive materials but descendants of unicellular organisms with complex context-sensitive behavioral capabilities. To probe the effects of different degrees of cellular competency on evolutionary dynamics, we used an evolutionary simulation in the context of minimal artificial embryogeny. Virtual embryos consisted of a single axis of positional information values provided by cells' 'structural genes', operated upon by an evolutionary cycle in which embryos' fitness was proportional to monotonicity of the axial gradient. Evolutionary dynamics were evaluated in two modes: hardwired development (genotype directly encodes phenotype), and a more realistic mode in which cells interact prior to evaluation by the fitness function ("regulative" development). We find that even minimal ability of cells with to improve their position in the embryo results in better performance of the evolutionary search. Crucially, we observed that increasing the behavioral competency masks the raw fitness encoded by structural genes, with selection favoring improvements to its developmental problem-solving capacities over improvements to its structural genome. This suggests the existence of a powerful ratchet mechanism: evolution progressively becomes locked in to improvements in the intelligence of its agential substrate, with reduced pressure on the structural genome. This kind of feedback loop in which evolution increasingly puts more effort into the developmental software than perfecting the hardware explains the very puzzling divergence of genome from anatomy in species like planaria. In addition, it identifies a possible driver for scaling intelligence over evolutionary time, and suggests strategies for engineering novel systems in silico and in bioengineering.
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Affiliation(s)
- Lakshwin Shreesha
- UFR Fundamental and Biomedical Sciences, Université Paris Cité, 75006 Paris, France
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
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Yasmin R, Deaton R. Logical computation with self-assembling electric circuits. PLoS One 2022; 17:e0278033. [PMID: 36477295 PMCID: PMC9728908 DOI: 10.1371/journal.pone.0278033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022] Open
Abstract
Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is amenable to exact analysis. Here, the cTAM is extended to the Boolean Circuit Tile Assembly Model (bcTAM) that implements a computationally complete set of Boolean gates through self-assembled and self-controlled growth. The proposed model approximates axonal growth in neural networks and thus, investigates the computational capability of dynamic biological networks, for example, in growing networks of axons. Thus, the bcTAM models the effect of electrical activity on growth and shows how that growth might implement Boolean computations. In this sense, given a set of input voltages, the bcTAM is a system that is able to monitor and make decisions about its own growth.
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Affiliation(s)
- Rojoba Yasmin
- Department of Electrical Engineering, University of Wisconsin Green Bay, Green Bay, WI, United States of America,* E-mail:
| | - Russell Deaton
- Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN, United States of America
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Systems of axon-like circuits for self-assembled and self-controlled growth of bioelectric networks. Sci Rep 2022; 12:13371. [PMID: 35927304 PMCID: PMC9352688 DOI: 10.1038/s41598-022-17103-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/20/2022] [Indexed: 11/09/2022] Open
Abstract
By guiding cell and chemical migration and coupling with genetic mechanisms, bioelectric networks of potentials influence biological pattern formation and are known to have profound effects on growth processes. An abstract model that is amenable to exact analysis has been proposed in the circuit tile assembly model (cTAM) to understand self-assembled and self-controlled growth as an emergent phenomenon that is capable of complex behaviors, like self-replication. In the cTAM, a voltage source represents a finite supply of energy that drives growth until it is unable to overcome randomizing factors in the environment, represented by a threshold. Here, the cTAM is extended to the axon or alternating cTAM model (acTAM) to include a circuit similar to signal propagation in axons, exhibiting time-varying electric signals and a dependence on frequency of the input voltage. The acTAM produces systems of circuits whose electrical properties are coupled to their length as growth proceeds through self-assembly. The exact response is derived for increasingly complex circuit systems as the assembly proceeds. The model exhibits complicated behaviors that elucidate the interactive role of energy, environment, and noise with electric signals in axon-like circuits during biological growth of complex patterns and function.
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Watson RA, Levin M, Buckley CL. Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.823588] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
The truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.
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Levin M. Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds. Front Syst Neurosci 2022; 16:768201. [PMID: 35401131 PMCID: PMC8988303 DOI: 10.3389/fnsys.2022.768201] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/24/2022] [Indexed: 12/11/2022] Open
Abstract
Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, United States
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Cambridge, MA, United States
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Kudithipudi D, Aguilar-Simon M, Babb J, Bazhenov M, Blackiston D, Bongard J, Brna AP, Chakravarthi Raja S, Cheney N, Clune J, Daram A, Fusi S, Helfer P, Kay L, Ketz N, Kira Z, Kolouri S, Krichmar JL, Kriegman S, Levin M, Madireddy S, Manicka S, Marjaninejad A, McNaughton B, Miikkulainen R, Navratilova Z, Pandit T, Parker A, Pilly PK, Risi S, Sejnowski TJ, Soltoggio A, Soures N, Tolias AS, Urbina-Meléndez D, Valero-Cuevas FJ, van de Ven GM, Vogelstein JT, Wang F, Weiss R, Yanguas-Gil A, Zou X, Siegelmann H. Biological underpinnings for lifelong learning machines. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00452-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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12
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Hipólito I. Cognition Without Neural Representation: Dynamics of a Complex System. Front Psychol 2022; 12:643276. [PMID: 35095629 PMCID: PMC8789682 DOI: 10.3389/fpsyg.2021.643276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 10/31/2021] [Indexed: 12/26/2022] Open
Abstract
This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).
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Affiliation(s)
- Inês Hipólito
- Berlin School of Mind and Brain, Institut für Philosophie, Humboldt-Universität zu Berlin, Berlin, Germany
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Grodstein J, Levin M. Stability and robustness properties of bioelectric networks: A computational approach. BIOPHYSICS REVIEWS 2021; 2:031305. [PMID: 38505634 PMCID: PMC10903393 DOI: 10.1063/5.0062442] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/07/2021] [Indexed: 03/21/2024]
Abstract
Morphogenesis during development and regeneration requires cells to communicate and cooperate toward the construction of complex anatomical structures. One important set of mechanisms for coordinating growth and form occurs via developmental bioelectricity-the dynamics of cellular networks driving changes of resting membrane potential which interface with transcriptional and biomechanical downstream cascades. While many molecular details have been elucidated about the instructive processes mediated by ion channel-dependent signaling outside of the nervous system, future advances in regenerative medicine and bioengineering require the understanding of tissue, organ, or whole body-level properties. A key aspect of bioelectric networks is their robustness, which can drive correct, invariant patterning cues despite changing cell number and anatomical configuration of the underlying tissue network. Here, we computationally analyze the minimal models of bioelectric networks and use the example of the regenerating planarian flatworm, to reveal important system-level aspects of bioelectrically derived patterns. These analyses promote an understanding of the robustness of circuits controlling regeneration and suggest design properties that can be exploited for synthetic bioengineering.
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Affiliation(s)
- Joel Grodstein
- Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts 02155, USA
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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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15
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Cancer Niches and Their Kikuchi Free Energy. ENTROPY 2021; 23:e23050609. [PMID: 34069097 PMCID: PMC8156740 DOI: 10.3390/e23050609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/27/2021] [Accepted: 05/07/2021] [Indexed: 12/12/2022]
Abstract
Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself; however, cellular mutations may disrupt the mutual balance between a cell and its niche, where cell proliferation and specialization are released from their autopoietic homeostasis. This induces the construction of cancer niches and maintains their survival. In this paper, we characterise cancer niche construction as a direct consequence of interactions between clusters of cancer and healthy cells. Explicitly, we evaluate these higher-order interactions between niches of cancer and healthy cells using Kikuchi approximations to the free energy. Kikuchi's free energy is measured in terms of changes to the sum of energies of baseline clusters of cells (or nodes) minus the energies of overcounted cluster intersections (and interactions of interactions, etc.). We posit that these changes in energy node clusters correspond to a long-term reduction in the complexity of the system conducive to cancer niche survival. We validate this formulation through numerical simulations of apoptosis, local cancer growth, and metastasis, and highlight its implications for a computational understanding of the etiopathology of cancer.
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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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17
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Bhat A, Parr T, Ramstead M, Friston K. Immunoceptive inference: why are psychiatric disorders and immune responses intertwined? BIOLOGY & PHILOSOPHY 2021; 36:27. [PMID: 33948044 PMCID: PMC8085803 DOI: 10.1007/s10539-021-09801-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 03/27/2021] [Indexed: 06/03/2023]
Abstract
There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a step back from a historical mind-body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems?
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Affiliation(s)
- Anjali Bhat
- Wellcome Centre for Human Neuroimaging, London, UK
- Division of Psychiatry, University College London, London, UK
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Maxwell Ramstead
- Wellcome Centre for Human Neuroimaging, London, UK
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Canada
- Spatial Web Foundation, Los Angeles, CA USA
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, London, UK
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18
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Blackiston D, Lederer E, Kriegman S, Garnier S, Bongard J, Levin M. A cellular platform for the development of synthetic living machines. Sci Robot 2021; 6:6/52/eabf1571. [PMID: 34043553 DOI: 10.1126/scirobotics.abf1571] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022]
Abstract
Robot swarms have, to date, been constructed from artificial materials. Motile biological constructs have been created from muscle cells grown on precisely shaped scaffolds. However, the exploitation of emergent self-organization and functional plasticity into a self-directed living machine has remained a major challenge. We report here a method for generation of in vitro biological robots from frog (Xenopus laevis) cells. These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects. The biological robots arise by cellular self-organization and do not require scaffolds or microprinting; the amphibian cells are highly amenable to surgical, genetic, chemical, and optical stimulation during the self-assembly process. We show that the xenobots can navigate aqueous environments in diverse ways, heal after damage, and show emergent group behaviors. We constructed a computational model to predict useful collective behaviors that can be elicited from a xenobot swarm. In addition, we provide proof of principle for a writable molecular memory using a photoconvertible protein that can record exposure to a specific wavelength of light. Together, these results introduce a platform that can be used to study many aspects of self-assembly, swarm behavior, and synthetic bioengineering, as well as provide versatile, soft-body living machines for numerous practical applications in biomedicine and the environment.
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Affiliation(s)
| | - Emma Lederer
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA
| | - Sam Kriegman
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
| | - Simon Garnier
- Federated Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Joshua Bongard
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA. .,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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19
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Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks. iScience 2021; 24:102131. [PMID: 33748699 PMCID: PMC7970124 DOI: 10.1016/j.isci.2021.102131] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/09/2020] [Accepted: 01/26/2021] [Indexed: 02/08/2023] Open
Abstract
Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs. We show that GRNs from a wide range of model systems are predicted to possess several types of memory, including Pavlovian conditioning. Associative memory offers an alternative strategy for the biomedical use of powerful drugs with undesirable side effects, and a novel approach to understanding the variability and time-dependent changes of drug action. We find evidence of natural selection favoring GRN memory. Vertebrate GRNs overall exhibit more memory than invertebrate GRNs, and memory is most prevalent in differentiated metazoan cell networks compared with undifferentiated cells. Timed stimuli are a powerful alternative for biomedical control of complex in vivo dynamics without genomic editing or transgenes. Gene regulatory networks' dynamics are modified by transient stimuli GRNs have several different types of memory, including associative conditioning Evolution favored GRN memory, and differentiated cells have the most memory capacity Training GRNs offers a novel biomedical strategy not dependent on genetic rewiring
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20
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Srivastava P, Kane A, Harrison C, Levin M. A Meta-Analysis of Bioelectric Data in Cancer, Embryogenesis, and Regeneration. Bioelectricity 2021; 3:42-67. [PMID: 34476377 DOI: 10.1089/bioe.2019.0034] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Developmental bioelectricity is the study of the endogenous role of bioelectrical signaling in all cell types. Resting potentials and other aspects of ionic cell physiology are known to be important regulatory parameters in embryogenesis, regeneration, and cancer. However, relevant quantitative measurement and genetic phenotyping data are distributed throughout wide-ranging literature, hampering experimental design and hypothesis generation. Here, we analyze published studies on bioelectrics and transcriptomic and genomic/phenotypic databases to provide a novel synthesis of what is known in three important aspects of bioelectrics research. First, we provide a comprehensive list of channelopathies-ion channel and pump gene mutations-in a range of important model systems with developmental patterning phenotypes, illustrating the breadth of channel types, tissues, and phyla (including man) in which bioelectric signaling is a critical endogenous aspect of embryogenesis. Second, we perform a novel bioinformatic analysis of transcriptomic data during regeneration in diverse taxa that reveals an electrogenic protein to be the one common factor specifically expressed in regeneration blastemas across Kingdoms. Finally, we analyze data on distinct Vmem signatures in normal and cancer cells, revealing a specific bioelectrical signature corresponding to some types of malignancies. These analyses shed light on fundamental questions in developmental bioelectricity and suggest new avenues for research in this exciting field.
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Affiliation(s)
- Pranjal Srivastava
- Rye High School, Rye, New York, USA; Current Affiliation: College of Chemistry, University of California, Berkeley, Berkeley, California, USA
| | - Anna Kane
- Department of Biology, Allen Discovery Center, Tufts University, Medford, Massachusetts, USA
| | - Christina Harrison
- Department of Biology, Allen Discovery Center, Tufts University, Medford, Massachusetts, USA
| | - Michael Levin
- Department of Biology, Allen Discovery Center, Tufts University, Medford, Massachusetts, USA
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21
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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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Cervera J, Ramirez P, Levin M, Mafe S. Community effects allow bioelectrical reprogramming of cell membrane potentials in multicellular aggregates: Model simulations. Phys Rev E 2020; 102:052412. [PMID: 33327213 DOI: 10.1103/physreve.102.052412] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/04/2020] [Indexed: 12/11/2022]
Abstract
Bioelectrical patterns are established by spatiotemporal correlations of cell membrane potentials at the multicellular level, being crucial to development, regeneration, and tumorigenesis. We have conducted multicellular simulations on bioelectrical community effects and intercellular coupling in multicellular aggregates. The simulations aim at establishing under which conditions a local heterogeneity consisting of a small patch of cells can be stabilized against a large aggregate of surrounding identical cells which are in a different bioelectrical state. In this way, instructive bioelectrical information can be persistently encoded in spatiotemporal patterns of separated domains with different cell polarization states. The multicellular community effects obtained are regulated both at the single-cell and intercellular levels, and emerge from a delicate balance between the degrees of intercellular coupling in: (i) the small patch, (ii) the surrounding bulk, and (iii) the interface that separates these two regions. The model is experimentally motivated and consists of two generic voltage-gated ion channels that attempt to establish the depolarized and polarized cell states together with coupling conductances whose individual and intercellular different states permit a dynamic multicellular connectivity. The simulations suggest that community effects may allow the reprogramming of single-cell bioelectrical states, in agreement with recent experimental data. A better understanding of the resulting electrical regionalization can assist the electroceutical correction of abnormally depolarized regions initiated in the bulk of normal tissues as well as suggest new biophysical mechanisms for the establishment of target patterns in multicellular engineering.
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Affiliation(s)
- Javier Cervera
- Departamento Termodinàmica, Universitat de València, E-46100 Burjassot, Spain
| | - Patricio Ramirez
- Departamento Física Aplicada, Universidad Politécnica de Valencia, E-46022 Valencia, Spain
| | - Michael Levin
- Department of Biology and Allen Discovery Center at Tufts University, Medford, Massachusetts 02155-4243, USA
| | - Salvador Mafe
- Departamento Termodinàmica, Universitat de València, E-46100 Burjassot, Spain
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Abstract
Neuromorphic systems are currently experiencing a rapid upswing due to the fact that today's CMOS (complementary metal oxide silicon) based technologies are increasingly approaching their limits. In particular, for the area of machine learning, energy consumption of today's electronics is an important limitation, that also contributes toward the ever-increasing impact of digitalization on our climate. Thus, in order to better meet the special requirements of unconventional computing, new physical substrates for bio-inspired computing schemes are extensively exploited. The aim of this Guest Edited Collection is to provide a platform for interdisciplinary research along three main lines: memristive materials and devices, emulation of cellular learning (neurons and synapses), and unconventional computing and network schemes.
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