1
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Ko JM, Reginato W, Wolff A, Lobo D. Mechanistic regulation of planarian shape during growth and degrowth. Development 2024; 151:dev202353. [PMID: 38619319 PMCID: PMC11128284 DOI: 10.1242/dev.202353] [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] [Received: 09/15/2023] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here, we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth as a result of a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.
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Affiliation(s)
- Jason M. Ko
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Waverly Reginato
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Andrew Wolff
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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2
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Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. NPJ Syst Biol Appl 2024; 10:35. [PMID: 38565850 PMCID: PMC10987498 DOI: 10.1038/s41540-024-00361-5] [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] [Received: 11/21/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, Baltimore, Baltimore, MD, USA.
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3
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Stillman NR, Mayor R. Generative models of morphogenesis in developmental biology. Semin Cell Dev Biol 2023; 147:83-90. [PMID: 36754751 PMCID: PMC10615838 DOI: 10.1016/j.semcdb.2023.02.001] [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] [Received: 12/20/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023]
Abstract
Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods.
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Affiliation(s)
- Namid R Stillman
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK.
| | - Roberto Mayor
- Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT, UK; Center for Integrative Biology, Faculty of Sciences, Universidad Mayor; Santiago, Chile Santiago, Chile..
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4
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Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550573. [PMID: 37546866 PMCID: PMC10402059 DOI: 10.1101/2023.07.26.550573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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5
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Hazan H, Levin M. Exploring the Behavior of Bioelectric Circuits Using Evolution Heuristic Search. Bioelectricity 2022. [DOI: 10.1089/bioe.2022.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Hananel Hazan
- Allen Discovery Center at Tufts University, Medford, Massachusetts, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, Massachusetts, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts, USA
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6
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Abstract
Extracting mechanistic knowledge from the spatial and temporal phenotypes of morphogenesis is a current challenge due to the complexity of biological regulation and their feedback loops. Furthermore, these regulatory interactions are also linked to the biophysical forces that shape a developing tissue, creating complex interactions responsible for emergent patterns and forms. Here we show how a computational systems biology approach can aid in the understanding of morphogenesis from a mechanistic perspective. This methodology integrates the modeling of tissues and whole-embryos with dynamical systems, the reverse engineering of parameters or even whole equations with machine learning, and the generation of precise computational predictions that can be tested at the bench. To implement and perform the computational steps in the methodology, we present user-friendly tools, computer code, and guidelines. The principles of this methodology are general and can be adapted to other model organisms to extract mechanistic knowledge of their morphogenesis.
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Affiliation(s)
- Jason M Ko
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
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7
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Lobo D. Formalizing Phenotypes of Regeneration. Methods Mol Biol 2022; 2450:663-679. [PMID: 35359335 PMCID: PMC9761515 DOI: 10.1007/978-1-0716-2172-1_36] [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: 06/14/2023]
Abstract
Regeneration experiments can produce complex phenotypes including morphological outcomes and gene expression patterns that are crucial for the understanding of the mechanisms of regeneration. However, due to their inherent complexity, variability between individuals, and heterogeneous data spreading across the literature, extracting mechanistic knowledge from them is a current challenge. Toward this goal, here we present protocols to unambiguously formalize the phenotypes of regeneration and their experimental procedures using precise mathematical morphological descriptions and standardized gene expression patterns. We illustrate the application of the methodology with step-by-step protocols for planaria and limb regeneration phenotypes. The curated datasets with these methods are not only helpful for human scientists, but they represent a key formalized resource that can be easily integrated into downstream reverse engineering methodologies for the automatic extraction of mechanistic knowledge. This approach can pave the way for discovering comprehensive systems-level models of regeneration.
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Affiliation(s)
- Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
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8
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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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9
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Mousavi R, Konuru SH, Lobo D. Inference of dynamic spatial GRN models with multi-GPU evolutionary computation. Brief Bioinform 2021; 22:6217729. [PMID: 33834216 DOI: 10.1093/bib/bbab104] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/15/2021] [Accepted: 03/09/2021] [Indexed: 02/06/2023] Open
Abstract
Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| | - Sri Harsha Konuru
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
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10
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From 'molecules of life' to new therapeutic approaches, an evolution marked by the advent of artificial intelligence: the cases of chronic pain and neuropathic disorders. Drug Discov Today 2021; 26:1070-1075. [PMID: 33482341 DOI: 10.1016/j.drudis.2021.01.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/25/2020] [Accepted: 01/12/2021] [Indexed: 11/21/2022]
Abstract
The large families of the molecules of life are at the origin of the discovery of new compounds with which to treat disease. The arrival of artificial intelligence (AI) has considerably modified the search for innovative bioactive drugs and their therapeutic applications. Conventional approaches at different organizational research levels have emerged and, thus, AI associated with gene and cell therapies could supplant conventional pharmacotherapy and facilitate the diagnosis of pathologies. Using the examples of chronic pain and neuropathic disorders, which affect a large number of patients, I illustrate here how AI could generate new therapeutic approaches, why some compounds are seen as recreational drugs and others as medicinal drugs, and why, in some countries, psychedelic drugs are considered as potential therapeutic drugs but not in others.
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11
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Hari A, Lobo D. Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks. Nucleic Acids Res 2020; 48:W427-W435. [PMID: 32442279 PMCID: PMC7319574 DOI: 10.1093/nar/gkaa409] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/20/2020] [Accepted: 05/06/2020] [Indexed: 12/19/2022] Open
Abstract
Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.
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Affiliation(s)
- Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA
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12
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Levin M. The Biophysics of Regenerative Repair Suggests New Perspectives on Biological Causation. Bioessays 2020; 42:e1900146. [PMID: 31994772 DOI: 10.1002/bies.201900146] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 12/03/2019] [Indexed: 12/13/2022]
Abstract
Evolution exploits the physics of non-neural bioelectricity to implement anatomical homeostasis: a process in which embryonic patterning, remodeling, and regeneration achieve invariant anatomical outcomes despite external interventions. Linear "developmental pathways" are often inadequate explanations for dynamic large-scale pattern regulation, even when they accurately capture relationships between molecular components. Biophysical and computational aspects of collective cell activity toward a target morphology reveal interesting aspects of causation in biology. This is critical not only for unraveling evolutionary and developmental events, but also for the design of effective strategies for biomedical intervention. Bioelectrical controls of growth and form, including stochastic behavior in such circuits, highlight the need for the formulation of nuanced views of pathways, drivers of system-level outcomes, and modularity, borrowing from concepts in related disciplines such as cybernetics, control theory, computational neuroscience, and information theory. This approach has numerous practical implications for basic research and for applications in regenerative medicine and synthetic bioengineering.
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Affiliation(s)
- 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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13
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Castillo-Lara S, Pascual-Carreras E, Abril JF. PlanExp: intuitive integration of complex RNA-seq datasets with planarian omics resources. Bioinformatics 2020; 36:1889-1895. [PMID: 31647529 DOI: 10.1093/bioinformatics/btz802] [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: 05/08/2019] [Revised: 10/16/2019] [Accepted: 10/23/2019] [Indexed: 01/16/2023] Open
Abstract
MOTIVATION There is an increasing amount of transcriptomic and genomic data available for planarians with the advent of both traditional and single-cell RNA sequencing technologies. Therefore, exploring, visualizing and making sense of all these data in order to understand planarian regeneration and development can be challenging. RESULTS In this work, we present PlanExp, a web-application to explore and visualize gene expression data from different RNA-seq experiments (both traditional and single-cell RNA-seq) for the planaria Schmidtea mediterranea. PlanExp provides tools for creating different interactive plots, such as heatmaps, scatterplots, etc. and links them with the current sequence annotations both at the genome and the transcript level thanks to its integration with the PlanNET web application. PlanExp also provides a full gene/protein network editor, a prediction of genetic interactions from single-cell RNA-seq data, and a network expression mapper that will help researchers to close the gap between systems biology and planarian regeneration. AVAILABILITY AND IMPLEMENTATION PlanExp is freely available at https://compgen.bio.ub.edu/PlanNET/planexp. The source code is available at https://compgen.bio.ub.edu/PlanNET/downloads. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- S Castillo-Lara
- Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Catalonia, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB); Universitat de Barcelona, Catalonia, Spain
| | - E Pascual-Carreras
- Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Catalonia, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB); Universitat de Barcelona, Catalonia, Spain
| | - J F Abril
- Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Catalonia, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB); Universitat de Barcelona, Catalonia, Spain
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14
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Hwang J, Hari A, Cheng R, Gardner JG, Lobo D. Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus. Biotechnol Bioeng 2020; 117:3876-3890. [PMID: 32833226 DOI: 10.1002/bit.27544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 07/11/2020] [Accepted: 08/19/2020] [Indexed: 12/12/2022]
Abstract
Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.
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Affiliation(s)
- Jeanice Hwang
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Raymond Cheng
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Jeffrey G Gardner
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
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15
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Hoel E, Levin M. Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control. Commun Integr Biol 2020; 13:108-118. [PMID: 33014263 PMCID: PMC7518458 DOI: 10.1080/19420889.2020.1802914] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/22/2020] [Accepted: 07/26/2020] [Indexed: 02/07/2023] Open
Abstract
The biological sciences span many spatial and temporal scales in attempts to understand the function and evolution of complex systems-level processes, such as embryogenesis. It is generally assumed that the most effective description of these processes is in terms of molecular interactions. However, recent developments in information theory and causal analysis now allow for the quantitative resolution of this question. In some cases, macro-scale models can minimize noise and increase the amount of information an experimenter or modeler has about "what does what." This result has numerous implications for evolution, pattern regulation, and biomedical strategies. Here, we provide an introduction to these quantitative techniques, and use them to show how informative macro-scales are common across biology. Our goal is to give biologists the tools to identify the maximally-informative scale at which to model, experiment on, predict, control, and understand complex biological systems.
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Affiliation(s)
- Erik Hoel
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
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16
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Abstract
AbstractThe main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
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17
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Fields C, Levin M. Does regeneration recapitulate phylogeny? Planaria as a model of body-axis specification in ancestral eumetazoa. Commun Integr Biol 2020; 13:27-38. [PMID: 32128026 PMCID: PMC7039665 DOI: 10.1080/19420889.2020.1729601] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/07/2020] [Accepted: 02/09/2020] [Indexed: 12/31/2022] Open
Abstract
Metazoan body plans combine well-defined primary, secondary, and in many bilaterians, tertiary body axes with structural asymmetries at multiple scales. Despite decades of study, how axis-defining symmetries and system-defining asymmetries co-emerge during both evolution and development remain open questions. Regeneration studies in asexual planaria have demonstrated an array of viable forms with symmetrized and, in some cases, duplicated body axes. We suggest that such forms may point toward an ancestral eumetazoan form with characteristics of both cnidarians and placazoa.
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Affiliation(s)
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA
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18
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Roy J, Cheung E, Bhatti J, Muneem A, Lobo D. Curation and annotation of planarian gene expression patterns with segmented reference morphologies. Bioinformatics 2020; 36:2881-2887. [DOI: 10.1093/bioinformatics/btaa023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 12/07/2019] [Accepted: 01/14/2020] [Indexed: 12/30/2022] Open
Abstract
Abstract
Motivation
Morphological and genetic spatial data from functional experiments based on genetic, surgical and pharmacological perturbations are being produced at an extraordinary pace in developmental and regenerative biology. However, our ability to extract knowledge from these large datasets are hindered due to the lack of formalization methods and tools able to unambiguously describe, centralize and interpret them. Formalizing spatial phenotypes and gene expression patterns is especially challenging in organisms with highly variable morphologies such as planarian worms, which due to their extraordinary regenerative capability can experimentally result in phenotypes with almost any combination of body regions or parts.
Results
Here, we present a computational methodology and mathematical formalism to encode and curate the morphological outcomes and gene expression patterns in planaria. Worm morphologies are encoded with mathematical graphs based on anatomical ontology terms to automatically generate reference morphologies. Gene expression patterns are registered to these standard reference morphologies, which can then be annotated automatically with anatomical ontology terms by analyzing the spatial expression patterns and their textual descriptions. This methodology enables the curation and annotation of complex experimental morphologies together with their gene expression patterns in a centralized standardized dataset, paving the way for the extraction of knowledge and reverse-engineering of the much sought-after mechanistic models in planaria and other regenerative organisms.
Availability and implementation
We implemented this methodology in a user-friendly graphical software tool, PlanGexQ, freely available together with the data in the manuscript at https://lobolab.umbc.edu/plangexq.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joy Roy
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Eric Cheung
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Junaid Bhatti
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Abraar Muneem
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
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19
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Herath S, Lobo D. Cross-inhibition of Turing patterns explains the self-organized regulatory mechanism of planarian fission. J Theor Biol 2020; 485:110042. [DOI: 10.1016/j.jtbi.2019.110042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/24/2019] [Accepted: 10/10/2019] [Indexed: 12/13/2022]
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20
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Ko JM, Lobo D. Continuous Dynamic Modeling of Regulated Cell Adhesion: Sorting, Intercalation, and Involution. Biophys J 2019; 117:2166-2179. [PMID: 31732144 PMCID: PMC6895740 DOI: 10.1016/j.bpj.2019.10.032] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 09/19/2019] [Accepted: 10/22/2019] [Indexed: 12/14/2022] Open
Abstract
Cell-cell adhesion is essential for tissue growth and multicellular pattern formation and crucial for the cellular dynamics during embryogenesis and cancer progression. Understanding the dynamical gene regulation of cell adhesion molecules (CAMs) responsible for the emerging spatial tissue behaviors is a current challenge because of the complexity of these nonlinear interactions and feedback loops at different levels of abstraction-from genetic regulation to whole-organism shape formation. To extend our understanding of cell and tissue behaviors due to the regulation of adhesion molecules, here we present a novel, to our knowledge, model for the spatial dynamics of cellular patterning, growth, and shape formation due to the differential expression of CAMs and their regulation. Capturing the dynamic interplay between genetic regulation, CAM expression, and differential cell adhesion, the proposed continuous model can explain the complex and emergent spatial behaviors of cell populations that change their adhesion properties dynamically because of inter- and intracellular genetic regulation. This approach can demonstrate the mechanisms responsible for classical cell-sorting behaviors, cell intercalation in proliferating populations, and the involution of germ layer cells induced by a diffusing morphogen during gastrulation. The model makes predictions on the physical parameters controlling the amplitude and wavelength of a cellular intercalation interface, as well as the crucial role of N-cadherin regulation for the involution and migration of cells beyond the gradient of the morphogen Nodal during zebrafish gastrulation. Integrating the emergent spatial tissue behaviors with the regulation of genes responsible for essential cellular properties such as adhesion will pave the way toward understanding the genetic regulation of large-scale complex patterns and shapes formation in developmental, regenerative, and cancer biology.
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Affiliation(s)
- Jason M Ko
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland; Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, Maryland.
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21
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Abstract
As the leading cause of death in cancer, there is an urgent need to develop treatments to target the dissemination of primary tumor cells to secondary organs, known as metastasis. Bioelectric signaling has emerged in the last century as an important controller of cell growth, and with the development of current molecular tools we are now beginning to identify its role in driving cell migration and metastasis in a variety of cancer types. This review summarizes the currently available research for bioelectric signaling in solid tumor metastasis. We review the steps of metastasis and discuss how these can be controlled by bioelectric cues at the level of a cell, a population of cells, and the tissue. The role of ion channel, pump, and exchanger activity and ion flux is discussed, along with the importance of the membrane potential and the relationship between ion flux and membrane potential. We also provide an overview of the evidence for control of metastasis by external electric fields (EFs) and draw from examples in embryogenesis and regeneration to discuss the implications for endogenous EFs. By increasing our understanding of the dynamic properties of bioelectric signaling, we can develop new strategies that target metastasis to be translated into the clinic.
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Affiliation(s)
- Samantha L. Payne
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, Massachusetts
| | - Madeleine J. Oudin
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts
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22
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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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23
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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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24
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Churchill CDM, Winter P, Tuszynski JA, Levin M. EDEn-Electroceutical Design Environment: Ion Channel Tissue Expression Database with Small Molecule Modulators. iScience 2019; 11:42-56. [PMID: 30590250 PMCID: PMC6308252 DOI: 10.1016/j.isci.2018.12.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 10/22/2018] [Accepted: 12/06/2018] [Indexed: 02/06/2023] Open
Abstract
The emerging field of bioelectricity has revealed numerous new roles for ion channels beyond the nervous system, which can be exploited for applications in regenerative medicine. Developing such biomedical interventions for birth defects, cancer, traumatic injury, and bioengineering first requires knowledge of ion channel targets expressed in tissues of interest. This information can then be used to select combinations of small molecule inhibitors and/or activators that manipulate the bioelectric state. Here, we provide an overview of electroceutical design environment (EDEn), the first bioinformatic platform that facilitates the design of such therapeutic strategies. This database includes information on ion channels and ion pumps, linked to known chemical modulators and their properties. The database also provides information about the expression levels of the ion channels in over 100 tissue types. The graphical interface allows the user to readily identify chemical entities that can alter the electrical properties of target cells and tissues.
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Affiliation(s)
| | - Philip Winter
- Department of Oncology, University of Alberta, Edmonton, AB T6G 1Z2, Canada
| | - Jack A Tuszynski
- Department of Physics, University of Alberta, Edmonton, AB T6G 2E1, Canada; Department of Oncology, University of Alberta, Edmonton, AB T6G 1Z2, Canada
| | - Michael Levin
- Allen Discovery Center, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA.
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25
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Feltes BC, Grisci BI, Poloni JDF, Dorn M. Perspectives and applications of machine learning for evolutionary developmental biology. Mol Omics 2018; 14:289-306. [PMID: 30168572 DOI: 10.1039/c8mo00111a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.
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Affiliation(s)
- Bruno César Feltes
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
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26
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Lee FJ, Williams KB, Levin M, Wolfe BE. The Bacterial Metabolite Indole Inhibits Regeneration of the Planarian Flatworm Dugesia japonica. iScience 2018; 10:135-148. [PMID: 30521984 PMCID: PMC6280633 DOI: 10.1016/j.isci.2018.11.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 10/31/2018] [Accepted: 11/12/2018] [Indexed: 02/07/2023] Open
Abstract
Planarian flatworms have been used for over a century as models for regeneration. Planarians live in aquatic environments with constant exposure to microbes, but the mechanisms by which bacteria may mediate planarian regeneration are largely unknown. We characterized the microbiome of laboratory populations of the planarian Dugesia japonica and determined how individual bacteria impact D. japonica regeneration. Eight to ten taxa in the phyla Bacteroidetes and Proteobacteria consistently occur across planarian colonies housed in different research laboratories. Individual members of the D. japonica microbiome can delay regeneration including the development of eye spots and blastema formation. The microbial metabolite indole is produced in significant quantities by two bacteria that are consistently found in the D. japonica microbiome and contributes to delays in regeneration. Collectively, these results provide a baseline understanding of the bacteria associated with the planarian D. japonica and demonstrate how metabolite production by host-associated microbes can affect regeneration. The planarian worm Dugesia japonica is colonized by Bacteroidetes and Proteobacteria Many of these bacteria can be cultured and experimentally manipulated Some bacteria can inhibit regeneration, including eye and blastema formation Indole produced by planarian-associated bacteria contributes to regeneration delays
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Affiliation(s)
- Fredrick J Lee
- Allen Discovery Center at Tufts University, Medford, MA 02155, 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
| | - Benjamin E Wolfe
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA.
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27
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Stone R, Portegys T, Mikhailovsky G, Alicea B. Origins of the Embryo: Self-organization through cybernetic regulation. Biosystems 2018; 173:73-82. [DOI: 10.1016/j.biosystems.2018.08.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 08/13/2018] [Accepted: 08/13/2018] [Indexed: 12/12/2022]
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28
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Peiretti F, Brunel JM. Artificial Intelligence: The Future for Organic Chemistry? ACS OMEGA 2018; 3:13263-13266. [PMID: 31458044 PMCID: PMC6645362 DOI: 10.1021/acsomega.8b01773] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 10/03/2018] [Indexed: 05/21/2023]
Abstract
On the basis of a recent article "Predicting reaction performance in C-N cross-coupling using machine learning" that appeared in Science, we had decided to highlight the way forward for artificial intelligence in chemistry. Synthesis of molecules remains one of the most important challenges in organic chemistry, and the standard approach involved by a chemist to solve a problem is based on experience and constitutes a repetitive, time-consuming task, often resulting in nonoptimized solutions. Thus, considering the recent phenomenal progresses that have been made in machine learning, there is little doubt that these systems, once fully operational in organic chemistry, will dramatically speed up development of new drugs and will constitute the future of chemistry.
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Affiliation(s)
- Franck Peiretti
- Faculté
de Médecine, Aix Marseille Université,
INSERM, INRA, C2VN, 27
Bd Jean Moulin, 13385 Marseille, France
| | - Jean Michel Brunel
- Faculté
de Pharmacie, U1261, INSERM, UMR-MD1 (Membranes
et Cibles Thérapeutiques), IRBA, Aix-Marseille Université, 27 Bd Jean Moulin, 13385 Marseille, France
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29
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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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30
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Abstract
Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.
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31
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Abstract
Decoding how tissue properties emerge across multiple spatial and temporal scales from the integration of local signals is a grand challenge in quantitative biology. For example, the collective behavior of epithelial cells is critical for shaping developing embryos. Understanding how epithelial cells interpret a diverse range of local signals to coordinate tissue-level processes requires a systems-level understanding of development. Integration of multiple signaling pathways that specify cell signaling information requires second messengers such as calcium ions. Increasingly, specific roles have been uncovered for calcium signaling throughout development. Calcium signaling regulates many processes including division, migration, death, and differentiation. However, the pleiotropic and ubiquitous nature of calcium signaling implies that many additional functions remain to be discovered. Here we review a selection of recent studies to highlight important insights into how multiple signals are transduced by calcium transients in developing epithelial tissues. Quantitative imaging and computational modeling have provided important insights into how calcium signaling integration occurs. Reverse-engineering the conserved features of signal integration mediated by calcium signaling will enable novel approaches in regenerative medicine and synthetic control of morphogenesis.
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Affiliation(s)
- Pavel A. Brodskiy
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, 205 McCourtney Hall, Notre Dame, IN 46556, USA
| | - Jeremiah J. Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, 205 McCourtney Hall, Notre Dame, IN 46556, USA
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32
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Castillo-Lara S, Abril JF. PlanNET: homology-based predicted interactome for multiple planarian transcriptomes. Bioinformatics 2018; 34:1016-1023. [PMID: 29186384 PMCID: PMC5860622 DOI: 10.1093/bioinformatics/btx738] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 10/24/2017] [Accepted: 11/23/2017] [Indexed: 01/30/2023] Open
Abstract
Motivation Planarians are emerging as a model organism to study regeneration in animals. However, the little available data of protein-protein interactions hinders the advances in understanding the mechanisms underlying its regenerating capabilities. Results We have developed a protocol to predict protein-protein interactions using sequence homology data and a reference Human interactome. This methodology was applied on 11 Schmidtea mediterranea transcriptomic sequence datasets. Then, using Neo4j as our database manager, we developed PlanNET, a web application to explore the multiplicity of networks and the associated sequence annotations. By mapping RNA-seq expression experiments onto the predicted networks, and allowing a transcript-centric exploration of the planarian interactome, we provide researchers with a useful tool to analyse possible pathways and to design new experiments, as well as a reproducible methodology to predict, store, and explore protein interaction networks for non-model organisms. Availability and implementation The web application PlanNET is available at https://compgen.bio.ub.edu/PlanNET. The source code used is available at https://compgen.bio.ub.edu/PlanNET/downloads. Contact jabril@ub.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- S Castillo-Lara
- Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - J F Abril
- Computational Genomics Laboratory, Genetics, Microbiology and Statistics Department, Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Catalonia, Spain
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33
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Fields C, Levin M. Are Planaria Individuals? What Regenerative Biology is Telling Us About the Nature of Multicellularity. Evol Biol 2018. [DOI: 10.1007/s11692-018-9448-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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34
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Sullivan KG, Levin M. Inverse Drug Screening of Bioelectric Signaling and Neurotransmitter Roles: Illustrated Using a Xenopus Tail Regeneration Assay. Cold Spring Harb Protoc 2018; 2018:pdb.prot099937. [PMID: 29437995 DOI: 10.1101/pdb.prot099937] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Xenopus embryos and larvae are an ideal model system in which to study the interplay between genetics, physiology, and anatomy in the control of structure and function. An important emerging field is the study of bioelectric signaling, the exchange of ion- and neurotransmitter-mediated messages among all types of cells (not just nerve and muscle cells), in the regulation of growth and form during embryogenesis, regeneration, and cancer. To facilitate the mechanistic investigation of bioelectric events in vivo, it is necessary to identify the endogenous signaling machinery involved in any patterning process of interest. This protocol uses the tail regeneration assay in Xenopus to perform an inverse drug screen; tiers of known compounds are used to probe the involvement of increasingly specific classes of bioelectric and neurotransmitter machinery. By using a hierarchical approach, large classes of targets are ruled out in early rounds, focusing attention on progressively narrower sets of proteins. Such a screen avoids many of the limitations of a molecular-genetic targeting approach and provides a rapid and efficient way to focus on specific targets. Usually, <10 experiments are needed to determine whether bioelectrics and/or neurotransmitter signaling are involved in the process of interest. This protocol describes the strategy in the context of a semiquantitative analysis of tail regeneration but can be applied to any assay in Xenopus or other small aquatic model system (e.g., zebrafish). Given the ever-increasing toolkit of chemical genetics, such screens represent a powerful and versatile methodology for probing the physiological circuits underlying pattern regulation.
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Affiliation(s)
- Kelly G Sullivan
- Biology Department, and Allen Discovery Center at Tufts University, Medford, Massachusetts 02155
| | - Michael Levin
- Biology Department, and Allen Discovery Center at Tufts University, Medford, Massachusetts 02155
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35
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Sharpe J. Computer modeling in developmental biology: growing today, essential tomorrow. Development 2017; 144:4214-4225. [DOI: 10.1242/dev.151274] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
D'Arcy Thompson was a true pioneer, applying mathematical concepts and analyses to the question of morphogenesis over 100 years ago. The centenary of his famous book, On Growth and Form, is therefore a great occasion on which to review the types of computer modeling now being pursued to understand the development of organs and organisms. Here, I present some of the latest modeling projects in the field, covering a wide range of developmental biology concepts, from molecular patterning to tissue morphogenesis. Rather than classifying them according to scientific question, or scale of problem, I focus instead on the different ways that modeling contributes to the scientific process and discuss the likely future of modeling in developmental biology.
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Affiliation(s)
- James Sharpe
- Systems Biology Unit, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
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36
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Pietak A, Levin M. Bioelectric gene and reaction networks: computational modelling of genetic, biochemical and bioelectrical dynamics in pattern regulation. J R Soc Interface 2017; 14:20170425. [PMID: 28954851 PMCID: PMC5636277 DOI: 10.1098/rsif.2017.0425] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 08/31/2017] [Indexed: 12/17/2022] Open
Abstract
Gene regulatory networks (GRNs) describe interactions between gene products and transcription factors that control gene expression. In combination with reaction-diffusion models, GRNs have enhanced comprehension of biological pattern formation. However, although it is well known that biological systems exploit an interplay of genetic and physical mechanisms, instructive factors such as transmembrane potential (Vmem) have not been integrated into full GRN models. Here we extend regulatory networks to include bioelectric signalling, developing a novel synthesis: the bioelectricity-integrated gene and reaction (BIGR) network. Using in silico simulations, we highlight the capacity for Vmem to alter steady-state concentrations of key signalling molecules inside and out of cells. We characterize fundamental feedbacks where Vmem both controls, and is in turn regulated by, biochemical signals and thereby demonstrate Vmem homeostatic control, Vmem memory and Vmem controlled state switching. BIGR networks demonstrating hysteresis are identified as a mechanisms through which more complex patterns of stable Vmem spots and stripes, along with correlated concentration patterns, can spontaneously emerge. As further proof of principle, we present and analyse a BIGR network model that mechanistically explains key aspects of the remarkable regenerative powers of creatures such as planarian flatworms. The functional properties of BIGR networks generate the first testable, quantitative hypotheses for biophysical mechanisms underlying the stability and adaptive regulation of anatomical bioelectric pattern.
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Affiliation(s)
- Alexis Pietak
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA
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Ghosh S. Application of Computational Methods in Planaria Research: A Current Update. J Integr Bioinform 2017; 14:/j/jib.ahead-of-print/jib-2017-0007/jib-2017-0007.xml. [PMID: 28682786 PMCID: PMC6042806 DOI: 10.1515/jib-2017-0007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 04/13/2017] [Indexed: 11/15/2022] Open
Abstract
Planaria is a member of the Phylum Platyhelminthes including flatworms. Planarians possess the unique ability of regeneration from adult stem cells or neoblasts and finds importance as a model organism for regeneration and developmental studies. Although research is being actively carried out globally through conventional methods to understand the process of regeneration from neoblasts, biology of development, neurobiology and immunology of Planaria, there are many thought provoking questions related to stem cell plasticity, and uniqueness of regenerative potential in Planarians amongst other members of Phylum Platyhelminthes. The complexity of receptors and signalling mechanisms, immune system network, biology of repair, responses to injury are yet to be understood in Planaria. Genomic and transcriptomic studies have generated a vast repository of data, but their availability and analysis is a challenging task. Data mining, computational approaches of gene curation, bioinformatics tools for analysis of transcriptomic data, designing of databases, application of algorithms in deciphering changes of morphology by RNA interference (RNAi) approaches, understanding regeneration experiments is a new venture in Planaria research that is helping researchers across the globe in understanding the biology. We highlight the applications of Hidden Markov models (HMMs) in designing of computational tools and their applications in Planaria decoding their complex biology.
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Durant F, Morokuma J, Fields C, Williams K, Adams DS, Levin M. Long-Term, Stochastic Editing of Regenerative Anatomy via Targeting Endogenous Bioelectric Gradients. Biophys J 2017; 112:2231-2243. [PMID: 28538159 PMCID: PMC5443973 DOI: 10.1016/j.bpj.2017.04.011] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 03/30/2017] [Accepted: 04/14/2017] [Indexed: 12/22/2022] Open
Abstract
We show that regenerating planarians' normal anterior-posterior pattern can be permanently rewritten by a brief perturbation of endogenous bioelectrical networks. Temporary modulation of regenerative bioelectric dynamics in amputated trunk fragments of planaria stochastically results in a constant ratio of regenerates with two heads to regenerates with normal morphology. Remarkably, this is shown to be due not to partial penetrance of treatment, but a profound yet hidden alteration to the animals' patterning circuitry. Subsequent amputations of the morphologically normal regenerates in water result in the same ratio of double-headed to normal morphology, revealing a cryptic phenotype that is not apparent unless the animals are cut. These animals do not differ from wild-type worms in histology, expression of key polarity genes, or neoblast distribution. Instead, the altered regenerative bodyplan is stored in seemingly normal planaria via global patterns of cellular resting potential. This gradient is functionally instructive, and represents a multistable, epigenetic anatomical switch: experimental reversals of bioelectric state reset subsequent regenerative morphology back to wild-type. Hence, bioelectric properties can stably override genome-default target morphology, and provide a tractable control point for investigating cryptic phenotypes and the stochasticity of large-scale epigenetic controls.
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Affiliation(s)
- Fallon Durant
- Allen Discovery Center at Tufts University, and Department of Biology, Tufts University, Medford, Massachusetts
| | - Junji Morokuma
- Allen Discovery Center at Tufts University, and Department of Biology, Tufts University, Medford, Massachusetts
| | | | - Katherine Williams
- Allen Discovery Center at Tufts University, and Department of Biology, Tufts University, Medford, Massachusetts
| | - Dany Spencer Adams
- Allen Discovery Center at Tufts University, and Department of Biology, Tufts University, Medford, Massachusetts
| | - Michael Levin
- Allen Discovery Center at Tufts University, and Department of Biology, Tufts University, Medford, Massachusetts.
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Neumann A, Kim DK, Perhar G, Arhonditsis GB. Integrative analysis of the Lake Simcoe watershed (Ontario, Canada) as a socio-ecological system. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017; 188:308-321. [PMID: 28002784 DOI: 10.1016/j.jenvman.2016.11.073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 11/23/2016] [Accepted: 11/27/2016] [Indexed: 06/06/2023]
Abstract
Striving for long-term sustainability in catchments dominated by human activities requires development of interdisciplinary research methods to account for the interplay between environmental concerns and socio-economic pressures. In this study, we present an integrative analysis of the Lake Simcoe watershed, Ontario, Canada, as viewed from the perspective of a socio-ecological system. Key features of our analysis are (i) the equally weighted consideration of environmental attributes with socioeconomic priorities and (ii) the identification of the minimal number of key socio-hydrological variables that should be included in a parsimonious watershed management framework, aiming to establish linkages between urbanization trends and nutrient export. Drawing parallels with the concept of Hydrological Response Units, we used Self-Organizing Mapping to delineate spatial organizations with similar socio-economic and environmental attributes, also referred to as Socio-Environmental Management Units (SEMUs). Our analysis provides evidence of two SEMUs with contrasting features, the "undisturbed" and "anthropogenically-influenced", within the Lake Simcoe watershed. The "undisturbed" cluster occupies approximately half of the Lake Simcoe catchment (45%) and is characterized by low landscape diversity and low average population density <0.4 humans ha-1. By contrast, the socio-environmental functional properties of the "anthropogenically-influenced" cluster highlight the likelihood of a stability loss in the long-run, as inferred from the distinct signature of urbanization activities on the tributary nutrient export, and the loss of subwatershed sensitivity to natural mechanisms that may ameliorate the degradation patterns. Our study also examines how the SEMU concept can augment the contemporary integrated watershed management practices and provides directions in order to promote environmental programs for lake conservation and to increase public awareness and engagement in stewardship initiatives.
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Affiliation(s)
- Alex Neumann
- Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, 1065 Military Trail, Toronto, Ontario M1C 1A4, Canada
| | - Dong-Kyun Kim
- Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, 1065 Military Trail, Toronto, Ontario M1C 1A4, Canada
| | - Gurbir Perhar
- Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, 1065 Military Trail, Toronto, Ontario M1C 1A4, Canada
| | - George B Arhonditsis
- Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, 1065 Military Trail, Toronto, Ontario M1C 1A4, Canada.
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García-Quismondo M, Levin M, Lobo D. Modeling regenerative processes with membrane computing. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.11.017] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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41
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Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus. Sci Rep 2017; 7:41339. [PMID: 28128301 PMCID: PMC5269672 DOI: 10.1038/srep41339] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 12/16/2016] [Indexed: 12/20/2022] Open
Abstract
Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.
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42
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Lobo D, Levin M. Computing a Worm: Reverse-Engineering Planarian Regeneration. EMERGENCE, COMPLEXITY AND COMPUTATION 2017. [DOI: 10.1007/978-3-319-33921-4_24] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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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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Owlarn S, Bartscherer K. Go ahead, grow a head! A planarian's guide to anterior regeneration. ACTA ACUST UNITED AC 2016; 3:139-55. [PMID: 27606065 PMCID: PMC5011478 DOI: 10.1002/reg2.56] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 04/08/2016] [Accepted: 04/15/2016] [Indexed: 12/12/2022]
Abstract
The unique ability of some planarian species to regenerate a head de novo, including a functional brain, provides an experimentally accessible system in which to study the mechanisms underlying regeneration. Here, we summarize the current knowledge on the key steps of planarian head regeneration (head‐versus‐tail decision, anterior pole formation and head patterning) and their molecular and cellular basis. Moreover, instructive properties of the anterior pole as a putative organizer and in coordinating anterior midline formation are discussed. Finally, we highlight that regeneration initiation occurs in a two‐step manner and hypothesize that wound‐induced and existing positional cues interact to detect tissue loss and together determine the appropriate regenerative outcomes.
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Affiliation(s)
- Suthira Owlarn
- Max Planck Research Group Stem Cells and Regeneration Max Planck Institute for Molecular Biomedicine Von-Esmarch-Str. 5448149 Münster Germany; Medical Faculty University of Münster Albert-Schweitzer-Campus 148149 Münster Germany; CiM-IMPRS Graduate School Schlossplatz 548149 Münster Germany
| | - Kerstin Bartscherer
- Max Planck Research Group Stem Cells and Regeneration Max Planck Institute for Molecular Biomedicine Von-Esmarch-Str. 5448149 Münster Germany; Medical Faculty University of Münster Albert-Schweitzer-Campus 148149 Münster Germany
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45
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Campbell JO. Universal Darwinism As a Process of Bayesian Inference. Front Syst Neurosci 2016; 10:49. [PMID: 27375438 PMCID: PMC4894882 DOI: 10.3389/fnsys.2016.00049] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 05/25/2016] [Indexed: 11/25/2022] Open
Abstract
Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.
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46
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Lobo D, Morokuma J, Levin M. Computational discovery andin vivovalidation ofhnf4as a regulatory gene in planarian regeneration. Bioinformatics 2016; 32:2681-5. [DOI: 10.1093/bioinformatics/btw299] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 05/04/2016] [Indexed: 11/14/2022] Open
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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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48
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Liu B, Zhou C, Li G, Zhang H, Zeng E, Liu Q, Ma Q. Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses. Sci Rep 2016; 6:23030. [PMID: 26975728 PMCID: PMC4792141 DOI: 10.1038/srep23030] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 02/22/2016] [Indexed: 12/18/2022] Open
Abstract
Regulons are the basic units of the response system in a bacterial cell, and each consists of a set of transcriptionally co-regulated operons. Regulon elucidation is the basis for studying the bacterial global transcriptional regulation network. In this study, we designed a novel co-regulation score between a pair of operons based on accurate operon identification and cis regulatory motif analyses, which can capture their co-regulation relationship much better than other scores. Taking full advantage of this discovery, we developed a new computational framework and built a novel graph model for regulon prediction. This model integrates the motif comparison and clustering and makes the regulon prediction problem substantially more solvable and accurate. To evaluate our prediction, a regulon coverage score was designed based on the documented regulons and their overlap with our prediction; and a modified Fisher Exact test was implemented to measure how well our predictions match the co-expressed modules derived from E. coli microarray gene-expression datasets collected under 466 conditions. The results indicate that our program consistently performed better than others in terms of the prediction accuracy. This suggests that our algorithms substantially improve the state-of-the-art, leading to a computational capability to reliably predict regulons for any bacteria.
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Affiliation(s)
- Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Chuan Zhou
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Guojun Li
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Hanyuan Zhang
- Systems Biology and Biomedical Informatics (SBBI) Laboratory University of Nebraska-Lincoln, Lincoln, NE 68588-0115, USA
| | - Erliang Zeng
- Department of Biology, University of South Dakota, Vermillion, SD 57069, USA.,Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA.,BioSNTR, Brookings, SD, USA
| | - Qi Liu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qin Ma
- Department of Plant Science, South Dakota State University, Brookings, SD, 57006, USA.,BioSNTR, Brookings, SD, USA
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49
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Lobo D, Hammelman J, Levin M. MoCha: Molecular Characterization of Unknown Pathways. J Comput Biol 2016; 23:291-7. [PMID: 26950055 DOI: 10.1089/cmb.2015.0211] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.
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Affiliation(s)
- Daniel Lobo
- 1 Department of Biological Sciences, University of Maryland , Baltimore County, Baltimore, Maryland
| | - Jennifer Hammelman
- 2 Center for Regenerative and Developmental Biology, and Department of Biology, Tufts University , Medford, Massachusetts
| | - Michael Levin
- 2 Center for Regenerative and Developmental Biology, and Department of Biology, Tufts University , Medford, Massachusetts
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50
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Emmons-Bell M, Durant F, Hammelman J, Bessonov N, Volpert V, Morokuma J, Pinet K, Adams DS, Pietak A, Lobo D, Levin M. Gap Junctional Blockade Stochastically Induces Different Species-Specific Head Anatomies in Genetically Wild-Type Girardia dorotocephala Flatworms. Int J Mol Sci 2015; 16:27865-96. [PMID: 26610482 PMCID: PMC4661923 DOI: 10.3390/ijms161126065] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Revised: 11/06/2015] [Accepted: 11/10/2015] [Indexed: 12/13/2022] Open
Abstract
The shape of an animal body plan is constructed from protein components encoded by the genome. However, bioelectric networks composed of many cell types have their own intrinsic dynamics, and can drive distinct morphological outcomes during embryogenesis and regeneration. Planarian flatworms are a popular system for exploring body plan patterning due to their regenerative capacity, but despite considerable molecular information regarding stem cell differentiation and basic axial patterning, very little is known about how distinct head shapes are produced. Here, we show that after decapitation in G. dorotocephala, a transient perturbation of physiological connectivity among cells (using the gap junction blocker octanol) can result in regenerated heads with quite different shapes, stochastically matching other known species of planaria (S. mediterranea, D. japonica, and P. felina). We use morphometric analysis to quantify the ability of physiological network perturbations to induce different species-specific head shapes from the same genome. Moreover, we present a computational agent-based model of cell and physical dynamics during regeneration that quantitatively reproduces the observed shape changes. Morphological alterations induced in a genomically wild-type G. dorotocephala during regeneration include not only the shape of the head but also the morphology of the brain, the characteristic distribution of adult stem cells (neoblasts), and the bioelectric gradients of resting potential within the anterior tissues. Interestingly, the shape change is not permanent; after regeneration is complete, intact animals remodel back to G. dorotocephala-appropriate head shape within several weeks in a secondary phase of remodeling following initial complete regeneration. We present a conceptual model to guide future work to delineate the molecular mechanisms by which bioelectric networks stochastically select among a small set of discrete head morphologies. Taken together, these data and analyses shed light on important physiological modifiers of morphological information in dictating species-specific shape, and reveal them to be a novel instructive input into head patterning in regenerating planaria.
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Affiliation(s)
- Maya Emmons-Bell
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
| | - Fallon Durant
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
| | - Jennifer Hammelman
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
| | - Nicholas Bessonov
- Institute of Problems of Mechanical Engineering, Russian Academy of Sciences, Saint Petersburg 199178, Russia;
| | - Vitaly Volpert
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne 69622, France;
| | - Junji Morokuma
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
| | - Kaylinnette Pinet
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
| | - Dany S. Adams
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
| | | | - Daniel Lobo
- Department of Biological Sciences, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA;
| | - Michael Levin
- Center for Regenerative and Developmental Biology and Department of Biology, Tufts University, 200 Boston Avenue, Suite 4600, Medford, MA 02155, USA; (M.E.-B.); (F.D.); (J.H.); (J.M.); (K.P.); (D.S.A.)
- Correspondence: ; Tel.: +1-617-627-6161; Fax: +1-617-627-6121
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