Skip Nav Destination
Close Modal
Update search
NARROW
Format
TocHeadingTitle
Date
Availability
1-8 of 8
Michael Levin
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Proceedings Papers
. isal2022, ALIFE 2022: The 2022 Conference on Artificial Life38, (July 18–22, 2022) 10.1162/isal_a_00521
Abstract
View Paper
PDF
The biggest open problems in the life sciences concern the algorithms by which competent subunits (cells) could cooperate to form large-scale structures with new, system-level properties. In synthetic bioengineering, multiple cells of diverse origin can be included in chimeric constructs. To facilitate progress in this field, we sought an understanding of multi-scale decision-making by diverse subunits beyond those observed in frozen accidents of biological phylogeny: abstract models of life-as-it-can-be. Neural Cellular Automata (NCA) are a very good inspiration for understanding current and possible living organisms: researchers managed to create NCA that are able to converge to any morphology. In order to simulate a more dynamic situation, we took the NCA model and generalized it to consider multiple NCA rules. We then used this generalized model to change the behavior of a NCA by injecting other types of cells (adversaries) and letting them take over the entire organism to solve a different task. Next we demonstrate that it is possible to stop aging in an existing NCA by injecting adversaries that follow a different rule. Finally, we quantify a distance between NCAs and develop a procedure that allows us to find adversaries close to the original cells.
Proceedings Papers
. isal2020, ALIFE 2020: The 2020 Conference on Artificial Life5, (July 13–18, 2020) 10.1162/isal_a_00353
Abstract
View Paper
PDF
Today's engineered robots are often made from reliable yet dumb parts, which greatly limits their adaptive functionality but ensures that their subsystems do not defect from the overall purpose. In contrast, a key aspect of Life is that biological systems have competency at each level - they are made of collectives of cells, tissues, organs, etc. each of which has local goals, which orchestrates the noise and fragility at lower levels towards highly robust system-level behaviors. The cooperation and competition across scales in living systems results in great plasticity, and in basal cognition - memory and decision-making outside the brain that can provide essential inspiration for artificial life and robotics. In this talk, I will outline the remarkable properties of complex body regeneration in some species, in which cellular collectives remember and work toward a specific anatomical outcome. We have now uncovered some of the mechanisms by which cells represent target morphologies and execute the anatomical homeostasis that enables them to reach these goals despite radical perturbations. The mechanism of this error reduction loop and pattern memory is bioelectrical, and I will describe the new tools with which we can now directly read out these anatomical setpoints in all cell types. Best of all, we can now re-write them in vivo, producing lines of 2-headed flatworms and other drastically altered animal anatomies by brief modulation of the bioelectric patterning software running on genomically un-edited (wild-type) cellular hardware. By cracking the morphogenetic code and understanding how anatomical decisions are implemented by distribute bioelectrical computations in tissues, we get closer to our endgame: a reverse anatomical compiler that will enable top-down design of living form at the level of patterning modules, not by micromanaging the molecular machine code on which much of biology is focused today. I will conclude by sketching out the implications of this field for not only biomedicine but also for new machine learning architectures and for the creation of computer-designed living organisms. The future belongs to a deep consilience of computer science, cognitive science, and biology to understand the plasticity of multi-scale computational systems and greatly broaden the boundaries of life-as-it-could-be.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life351-358, (July 23–27, 2018) 10.1162/isal_a_00066
Abstract
View Paper
PDF
Increasing evidence points to a role for complex physical phenomena, including mechanical forces and bioelectricity, as drivers of patterning in development and regeneration. We developed a genetic algorithm-based approach to search the space of biophysical simulations for pattern-forming processes and use it to demonstrate that Turing-like patterns can arise purely bioelectrically, without requiring any variation in gene expression. We also identify several bioelectric components that can reinforce and enhance such patterns manifested in cell transmembrane voltages.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life194-201, (July 23–27, 2018) 10.1162/isal_a_00041
Abstract
View Paper
PDF
A key question in developmental biology and regenerative medicine concerns the physiological mechanisms by which cells coordinate their behaviors toward the construction and repair of complex anatomical structures. Gap junctional communication among cells enables bioelectrical signaling within a network that enables collections of cells to cooperate during morphogenesis. During regeneration in amputated planarian flatworms, cells capable of dividing must migrate to areas where new tissue is needed. Moreover, these cells must stop proliferating when the needed structures are completed. We previously proposed a cell-cell communication mechanism that enables structure discovery and regeneration by cell networks. In this paper, we further develop the mechanism to address two important simplifications of the previous model: cell division was not limited to adult stem cells (as it is in vivo ), and adult stem cells did not migrate to injured areas to initiate the regeneration process. Thus, here we limit cell division to a specific cell type (neoblasts) and propose a second message type that guides neoblasts to locations where cell division is needed. Our results show that even after incorporating these two constraints, our cell-cell communication model maintained its regeneration capabilities against a large tissue removal.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life204-205, (July 23–27, 2018) 10.1162/isal_a_00043
Abstract
View Paper
PDF
Many organisms such as planaria, axolotls and deer exhibit prodigious regenerative abilities, being capable of regenerating complex organs or entire body plans. An understanding of how these organisms store and modify their morphological patterning information is necessary to identify modes of control and intervention. Insight into this process is key to the development of novel biomedical applications. In this work, we present the CANN( k ) model: an abstract computational model of pattern regeneration which couples an artificial neural network (ANN) with a k -color cellular automaton (CA). The ANN provides a global information processing system which generates state-dependent update rules for the CA. The CANN( k ) models are constructed to generate target patterns which are stable under perturbations of the pattern. We generate ensembles of CANN(4) models for each of the 4-color patterns, assess their sensitivity to changes of the ANN structure. This provides a novel model for understanding the important biological phenomenon of neural control of cellular morphogenesis in development or regeneration.
Proceedings Papers
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life170-177, (September 4–8, 2017) 10.1162/isal_a_029
Abstract
View Paper
PDF
In this paper we modified a previous cell-cell communication mechanism of dynamic structure discovery and regeneration to account for the presence of noise that could alter the route of messages transmitted across cells. We report results from a large number of simulation runs where noise was applied to the distance and direction of messages dispatched by cells. Based on our analysis of the results, we proposed an “activation” mechanism where missing cells need to receive a certain number of messages first before they divide and recreate missing cells. We then show that, due to the inherent message redundancy in the organism this mechanism improved the performance of the model even when noise is present on packets.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems352-359, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch059
Abstract
View Paper
PDF
Many organisms can regenerate their bodies, but it is currently unclear how they accomplish this feat. In this paper, we introduce a cell-to-cell communication mechanism that allows a 3D arrangement of cells to discover its structure and maintain it in the light of random cell death, even at very high death rates. We report results from simulations of an agent-based model that demonstrate the effectiveness of the proposed approach for Planarian worm-like shapes, but the proposed model is general and applies to any shape.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems528-535, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch085
Abstract
View Paper
PDF
A framework for predictively linking cell-level signaling with larger scale patterning in regeneration and growth has yet to be created within the field of regenerative biology. If this could be achieved, regeneration (controlled cell growth), cancer (uncontrolled cell growth), and birth defects (mispatterning of cell growth) could be more easily understood and manipulated. This paper looks to create a key part of this preliminary framework by using level set methods and a cellular control scheme to predict macroscopic regenerative morphology. This simulation specifically looks at Xenopus laevis tail regeneration, and uses three control regimes to collectively mimic biological regeneration. The algorithm shows promise in creating an abstracted model to predict cell patterning on a macroscopic level.