A list of OpenWorm scientific publications.

Periodicity in the Embryo: emergence of order in space, diffusion of order in time

6 January 2021, Biosystems, 204, 104405. doi:10.1016/j.biosystems.2021.104405

Bradly Alicea, Jesse Parent, Ujjwal Singh

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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Data-theoretical Synthesis of the Early Developmental Process

22 December 2020, Neuroinformatics, doi:10.1007/s12021-020-09508-1.

Bradly Alicea, Richard Gordon, Thomas E. Portegys

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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Raising the Connectome: the emergence of neuronal activity and behavior in C. elegans

15 September 2020, Frontiers in Cellular Neuroscience, 14, 524791. doi:10.3389/fncel.2020.524791

Bradly Alicea

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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The Emergent Connectome in Caenorhabditis elegans Embryogenesis

25 September 2018, Biosystems, 173, 247-255. doi:10.1016/j.biosystems.2018.09.016

Bradly Alicea, DevoWorm Group

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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Cell differentiation processes as spatial networks: Identifying four-dimensional structure in embryogenesis

20 September 2018, Biosystems, 173, 235-246. doi:10.1016/j.biosystems.2018.09.009

Bradly Alicea, Richard Gordon

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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OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

10 September 2018, Phil. Trans. R. Soc. B, DOI: 10.1098/rstb.2017.0382

Gopal P. Sarma, Chee Wai Lee, Tom Portegys, Vahid Ghayoomie, Travis Jacobs, Bradly Alicea, Matteo Cantarelli, Michael Currie, Richard C. Gerkin, Shane Gingell, Padraig Gleeson, Richard Gordon, Ramin M. Hasani, Giovanni Idili, Sergey Khayrulin, David Lung, Andrey Palyanov, Mark Watts and Stephen D. Larson

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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Towards systematic, data-driven validation of a collaborative, multi-scale model of Caenorhabditis elegans

10 September 2018, Phil. Trans. R. Soc. B, DOI: 10.1098/rstb.2017.0381

Richard C. Gerkin, Russell J. Jarvis and Sharon M. Crook

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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Geppetto: a reusable modular open platform for exploring neuroscience data and models

10 September 2018, Phil. Trans. R. Soc. B, DOI: 10.1098/rstb.2017.0380

Matteo Cantarelli, Boris Marin, Adrian Quintana, Matt Earnshaw, Robert Court, Padraig Gleeson, Salvador Dura-Bernal, R. Angus Silver and Giovanni Idili

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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c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans

10 September 2018, Phil. Trans. R. Soc. B, doi:10.1098/rstb.2017.0379

Padraig Gleeson, David Lung, Radu Grosu, Ramin Hasani and Stephen D. Larson

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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Three-dimensional simulation of the Caenorhabditis elegans body and muscle cells in liquid and gel environments for behavioural analysis

10 September 2018, Phil. Trans. R. Soc. B, doi:10.1098/rstb.2017.0376

Andrey Palyanov, Sergey Khayrulin and Stephen D. Larson

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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Unit Testing, Model Validation, and Biological Simulation

August 10 2016, F1000Research, 5:1946 (2016), DOI: 10.12688/f1000research.9315.1

Gopal P. Sarma, Travis W. Jacobs, Mark D. Watts, S. Vahid Ghayoomie, Stephen D. Larson, and Rick C. Gerkin

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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Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development Via Differentiation Trees of Embryos

18 August 2016, Biology, 5(3), 33. doi:10.3390/biology5030033

Bradly Alicea and Richard Gordon

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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Application of smoothed particle hydrodynamics to modeling mechanisms of biological tissue

March 08 2016, Adv. Eng. Software, DOI: 10.1016/j.advengsoft.2016.03.002

Andrey Palyanov, Sergey Khayrulin, and Stephen Larson

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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The OpenWorm Project: currently available resources and future plans

July 18 2015, BMC Neuroscience, DOI: 10.1186/1471-2202-16-S1-P141

Padraig Gleeson, Matteo Cantarelli, Michael Currie, Jim Hokanson, Giovanni Idili, Sergey Khayrulin, Andrey Palyanov, Balazs Szigeti and Stephen Larson

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OpenWorm: an open-science approach to modeling Caenorhabditis elegans

November 03 2014, Front. Comput. Neurosci., DOI: 10.3389/fncom.2014.00137

Balázs Szigeti, Padraig Gleeson, Michael Vella, Sergey Khayrulin, Andrey Palyanov, Jim Hokanson, Michael Currie, Matteo Cantarelli, Giovanni Idili and Stephen Larson

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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Beyond the connectome hairball: Rational visualizations and analysis of the C. elegans connectome as a network graph using hive plots

July 11 2013, Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. DOI: 10.3389/conf.fninf.2013.09.00032

Pedro Tabacof, Tim Busbice, Stephen D. Larson (

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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Integration of predictive-corrective incompressible SPH and Hodgkin-Huxley based models in the OpenWorm in silico model of C. elegans

July 8 2013, BMC Neuroscience 2013, 14(Suppl 1):P209 DOI:10.1186/1471-2202-14-S1-P209

Michael Vella (Department of Physiology, Development and Neuroscience, University of Cambridge), Andrey Palyanov, Sergey Khayrulin (A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Acad. Lavrentjev pr., Russia)

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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Towards a virtual C. elegans: A framework for simulation and visualization of the neuromuscular system in a 3D physical environment

Aug 2012, In Silico Biology, 11(3):137-147 DOI:10.3233/ISB-2012-0445

Andrey Palyanov, Sergey Khayrulin, Stephen D Larson, Alexander Dibert A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Acad. Lavrentjev pr., Russia.

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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The NeuroML C. elegans Connectome

September 11 2012 - Neuroinformatics 2012 Abstract Book

Tim Busbice (Interintelligence Research), Padraig Gleeson (University College London), Sergey Khayrulin (A.P. Ershov Institute of Informatics Systems), Matteo Cantarelli (, Alexander Dibert (A.P. Ershov Institute of Informatics Systems), Giovanni Idili (, Andrey Palyanov (A.P. Ershov Institute of Informatics Systems), Stephen Larson (

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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Managing Complexity in Multi-Algorithm, Multi-Scale Biological Simulations: An Integrated Software Engineering and Neuroinformatics Approach

September 4 2011 - Neuroinformatics 2012 Abstract Book

Giovanni Idili (, Matteo Cantarelli (, Marius Buibas (Department of Bioengineering, University of California, San Diego, La Jolla, CA), Tim Busbice (InterIntelligence Research, Los Angeles, CA), Jay Coggan (Salk Institute, La Jolla, CA), Christian Grove (WormBase, California Institute of Technology, Pasadena CA), Sergey Khayrulin (A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Novosibirsk, Russia), Andrey Palyanov (A.P. Ershov Institute of Informatics Systems SB RAS, Lab. of Complex Systems Simulation, Novosibirsk, Russia), Stephen Larson (Whole Brain Project, University of California, San Diego, La Jolla, CA)

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,, Clones;, 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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