A list of OpenWorm scientific publications.
6 January 2021, Biosystems, 204, 104405. doi:10.1016/j.biosystems.2021.104405
Does embryonic development exhibit characteristic temporal features? This is apparent in evolution, where evolutionary change has been shown to occur in bursts of activity. Using two animal models (Nematode, Caenorhabditis elegans and Zebrafish, Danio rerio) and simulated data, we demonstrate that temporal heterogeneity exists in embryogenesis at the cellular level, and may have functional consequences. Cell proliferation and division from cell tracking data is subject to analysis to characterize specific features in each model species. Simulated data is then used to understand what role this variation might play in producing phenotypic variation in the adult phenotype. This goes beyond a molecular characterization of developmental regulation to provide a quantitative result at the phenotypic scale of complexity.
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22 December 2020, Neuroinformatics, doi:10.1007/s12021-020-09508-1.
Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model system Caenorhabditis elegans, we examine time-dependent properties of the embryonic phenotype and utilize the unique life-history properties to demonstrate how these emergent properties can be linked together by data analysis and theory-building. We also focus on embryogenetic differentiation processes, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. Examining embryogenetic dynamics from 200 to 400 min post-fertilization provides basic quantitative information on developmental tempo and process. To summarize, theory construction techniques are summarized and proposed as a way to rigorously interpret our data. Our proposed approach to a formal data representation that can provide critical links across life-history, anatomy and function.
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15 September 2020, Frontiers in Cellular Neuroscience, 14, 524791. doi:10.3389/fncel.2020.524791
The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of Caenorhabditis elegans will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.
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25 September 2018, Biosystems, 173, 247-255. doi:10.1016/j.biosystems.2018.09.016
The relatively new field of connectomics provides us with a unique window into nervous system function. In the model organism Caenorhabditis elegans, this promise is even greater due to the relatively small number of cells (302) in its nervous system. While the adult C. elegans connectome has been characterized, the emergence of these networks in development has yet to be established. In this paper, we approach this problem using secondary data describing the birth times of terminally-differentiated cells as they appear in the embryo and a connectomics model for nervous system cells in the adult hermaphrodite. By combining these two sources of data, we can better understand patterns that emerge in an incipient connectome. This includes identifying at what point in embryogenesis the cells of a connectome first comes into being, potentially observing some of the earliest neuron-neuron interactions, and making comparisons between the formally-defined connectome and developmental cell lineages. An analysis is also conducted to root terminally-differentiated cells in their developmental cell lineage precursors. This reveals subnetworks with different properties at 300 min of embryogenesis. Additional investigations reveal the spatial position of neuronal cells born during pre-hatch development, both within and outside the connectome model, in the context of all developmental cells in the embryo. Overall, these analyses reveal important information about the birth order of specific cells in the connectome, key building blocks of global connectivity, and how these structures correspond to key events in early development.
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20 September 2018, Biosystems, 173, 235-246. doi:10.1016/j.biosystems.2018.09.009
One overarching principle of eukaroytic development is the generative spatial emergence and self-organization of cell populations. As cells divide and differentiate, they and their descendents form a spatiotemporal explicit and increasingly compartmentalized complex system. Yet despite this comparmentalization, there is selective functional overlap between these structural components. While contemporary tools such as lineage trees and molecular signaling networks prvide a window into this complexity, they do not characterize embryogenesis as a global process. Using a four-dimensional spatial representation, major features of the developmental process are revealed. To establish the role of developmental mechanisms that turn a spherical embryo into a highly asymmetrical adult phenotype, we can map the outcomes of the cell division process to a complex network model. This representational model provides information about the top-down mechanisms relevant to the differentiation process. In a complementary manner, looking for phenomena such as superdiffusive positioning and sublineage-based anatomical clustering incorporates dynamic information to our parallel view of embryogenesis. Characterizing the spatial organization and geometry of embryos in this way allows for novel indicators of developmental patterns both within and between organisms.
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10 September 2018, Phil. Trans. R. Soc. B, DOI: 10.1098/rstb.2017.0382
The adoption of powerful software tools and computational methods from the software industry by the scientific research
community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve
the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm
Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis
elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental
biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent
sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future
directions.
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10 September 2018, Phil. Trans. R. Soc. B, DOI: 10.1098/rstb.2017.0381
The OpenWorm Project is an international open-source collaboration to create a multi-scale model of the organism
Caenorhabditis elegans. At each scale, including subcellular, cellular, network and behaviour, this project employs
one or more computational models that aim to recapitulate the corresponding biological system at that scale.
This requires that the simulated behaviour of each model be compared with experimental data both as the model is
continuously refined and as new experimental data become available. Here we report the use of SciUnit, a software framework
for model validation, to attempt to achieve these goals. During project development, each model is continuously subjected to
data-driven ‘unit tests’ that quantitatively summarize model-data agreement, identifying modelling progress and highlighting
particular aspects of each model that fail to adequately reproduce known features of the biological organism and its components.
This workflow is publicly visible via both GitHub and a web application and accepts community contributions to ensure that modelling
goals are transparent and well-informed.
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10 September 2018, Phil. Trans. R. Soc. B, DOI: 10.1098/rstb.2017.0380
Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools
for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications,
including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize
computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for Drosophila
melanogaster neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data.
Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a
Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent
their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way.
The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly
customizable frontend.
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10 September 2018, Phil. Trans. R. Soc. B, doi:10.1098/rstb.2017.0379
The OpenWorm project has the ambitious goal of producing a highly detailed in silico model of the nematode Caenorhabditis elegans.
A crucial part of this work will be a model of the nervous system encompassing all known cell types and connections. The appropriate
level of biophysical detail required in the neuronal model to reproduce observed high-level behaviours in the worm has yet to be determined.
For this reason, we have developed a framework, c302, that allows different instances of neuronal networks to be generated incorporating
varying levels of anatomical and physiological detail, which can be investigated and refined independently or linked to other tools
developed in the OpenWorm modelling toolchain.
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10 September 2018, Phil. Trans. R. Soc. B, doi:10.1098/rstb.2017.0376
To better understand how a nervous system controls the movements of an organism, we have created a three-dimensional computational
biomechanical model of the Caenorhabditis elegans body based on real anatomical structure. The body model is created with a particle
system–based simulation engine known as Sibernetic, which implements the smoothed particle–hydrodynamics algorithm. The model includes
an elastic body-wall cuticle subject to hydrostatic pressure. This cuticle is then driven by body-wall muscle cells that contract and
relax, whose positions and shape are mapped from C. elegans anatomy, and determined from light microscopy and electron micrograph data.
We show that by using different muscle activation patterns, this model is capable of producing C. elegans-like behaviours,
including crawling and swimming locomotion in environments with different viscosities, while fitting multiple additional known biomechanical
properties of the animal.
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August 10 2016, F1000Research, 5:1946 (2016), DOI: 10.12688/f1000research.9315.1
The growth of the software industry has gone hand in hand with the development of tools
and cultural practices for ensuring the reliability of complex pieces of software.
These tools and practices are now acknowledged to be essential to the management of
modern software. As computational models and methods have become increasingly common
in the biological sciences, it is important to examine how these practices can accelerate
biological software development and improve research quality. In this article, we give a
focused case study of our experience with the practices of unit testing and test-driven
development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans.
We identify and discuss the challenges of incorporating test-driven development into a
heterogeneous, data-driven project, as well as the role of model validation tests, a
category of tests unique to software which expresses scientific models.
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18 August 2016, Biology, 5(3), 33. doi:10.3390/biology5030033
Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.
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March 08 2016, Adv. Eng. Software, DOI: 10.1016/j.advengsoft.2016.03.002
A prerequisite for simulating the biophysics of complex biological tissues and whole organisms are computational descriptions of biological matter that are flexible and can interface with materials of different viscosities, such as liquid. The landscape of software that is easily available to do such work is limited and lacks essential features necessary for combining elastic matter with simulations of liquids. Here we present an open source software package called Sibernetic, designed for the physical simulation of biomechanical matter (membranes, elastic matter, contractile matter) and environments (liquids, solids and elastic matter with variable physical properties). At its core, Sibernetic is built as an extension to Predictive–Corrective Incompressible Smoothed Particle Hydrodynamics (PCISPH). Sibernetic is built on top of OpenCL, making it possible to run simulations on CPUs or GPUs, and has 3D visualization
support built on top of OpenGL. Several test examples of the software running and reproducing physical experiments, as well as performance benchmarks, are presented and future directions are discussed.
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July 18 2015, BMC Neuroscience, DOI: 10.1186/1471-2202-16-S1-P141
November 03 2014, Front. Comput. Neurosci., DOI: 10.3389/fncom.2014.00137
OpenWorm is an international collaboration with the aim of understanding how the behavior of Caenorhabditis elegans (C. elegans)
emerges from its underlying physiological processes. The project has developed a modular simulation engine to create computational
models of the worm. The modularity of the engine makes it possible to easily modify the model, incorporate new experimental data and
test hypotheses. The modeling framework incorporates both biophysical neuronal simulations and a novel fluid-dynamics-based soft-tissue
simulation for physical environment-body interactions. The project's open-science approach is aimed at overcoming the difficulties of
integrative modeling within a traditional academic environment. In this article the rationale is presented for creating the OpenWorm
collaboration, the tools and resources developed thus far are outlined and the unique challenges associated with the project are discussed.
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July 11 2013, Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. DOI: 10.3389/conf.fninf.2013.09.00032
The C. elegans connectome (White et al., 1986) is currently the most detailed connectome data set at the neuronal circuit level that is publicly available. Represented as a network graph, it consists of edges that distinguish between gap junctions and chemical synapses, weighted by synapse count, with nodes that represent neurons whose identities are unambiguous and well known.
We have found exploration of the C. elegans connectome using hive plots to lead to the discovery of interesting qualitative structure that was previously not obvious, enabling this structure to be further pursued quantitatively using complex network mathematics.
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July 8 2013, BMC Neuroscience 2013, 14(Suppl 1):P209 DOI:10.1186/1471-2202-14-S1-P209
OpenWorm is an international collaboration with the aim of producing an integrative computational model of Caenorhabditis elegans to further the understanding of how macroscopic behaviour of the organism emerges from aggregated biophysical processes. A core component of the project involves the integration of electrophysiological modelling and predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH) to model how neuronal and muscle dynamics effect the nematode's behaviour.
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Aug 2012, In Silico Biology, 11(3):137-147 DOI:10.3233/ISB-2012-0445
The nematode C. elegans is the only animal with a known neuronal wiring diagram, or "connectome". During the last three decades, extensive studies of the C. elegans have provided wide-ranging data about it, but few systematic ways of integrating these data into a dynamic model have been put forward. Here we present a detailed demonstration of a virtual C. elegans aimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current demonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord. Our virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal activity to groups of motor neurons. To account for the relatively slow propagation velocity and the attenuation of neuronal signals, we introduced "pseudo neurons" into our model to simulate simplified neuronal dynamics. The pseudo neurons also provide a good way of visualizing the nervous system's structure and activity dynamics.
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September 11 2012 - Neuroinformatics 2012 Abstract Book
We have merged and extended the C. elegans connectome (Varshney et al., 2006) and a three-dimensional cellular anatomy model (Grove & Sternberg, 2011) in the context of the OpenWorm project, an open source project to build a data integration and simulation framework for the C. elegans.
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September 4 2011 - Neuroinformatics 2012 Abstract Book
Computational biology is asserting itself as an important approach to understanding complex biological systems.
In order to be able to effectively manage the complexity that comes with integrating and maintaining coarse-grained architectures, tools, digital information artifacts and code-bases, it is important for computational biology to fully embrace software engineering methodologies and best practices and follow the lead of the simulation based research in the physical sciences.
Taking cues from pioneering projects in computational neuroscience that are addressing this problem (MOOSE, http://j.mp/gSZZNF, Clones; http://j.mp/gzC5CP), we describe our approach to the integration of close-to-the-metal massively parallel simulations with high-level abstractions through the use of design patterns, including emerging paradigms for GPU-based parallel programming.
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