Modelling, visualizing and understanding the neural dynamics of Caenorhabditis elegans
Dr Eli Shlizerman, University of Washington, USA
Connectomes of organisms, such as the nematode Caenorhabditis elegans (C. elegans), have been mapped on various scales: from macro to single neuron level. In addition, decades of research in describing biophysical processes have provided foundations for modeling single neuron dynamics as well as synaptic and electric processes between neurons. Thereby, models which incorporate biophysical dynamical system acting on top of the static connectome, called dynomes, become more detailed and realizable. Availability of near-complete connectome data along with experimental quantification of responses and interactions allowed us to develop a detailed dynome model for C. elegans’ somatic nervous system. Employing an interactive visualization platform to simulate the dynome we apply various stimuli regimes and show robust low-dimensional bifurcation structures which drive a variety of multistable neural voltage modes, such as fixed points, limit cycles, multi-oscillatory dynamics. Comparison of these modes with experimental studies allows us to link behavioral states with network responses in the form of low-dimensional attractors and transitions between them.
Modelling the neural network of C. elegans at multiple scales with c302
Dr Padraig Gleeson, University College London, UK
Computational models of the nervous system are developed at multiple scales to answer questions about how low level interactions between biological entities lead to higher level functions. Models of the nematode C. elegans have also been created at levels from individual neurons and muscles, subcircuits responsible for processing specific sensory inputs, body wide processes including locomotion, and detailed nervous system/musculature models. These models usually select a subset of anatomical and physiological properties of the worm and can address a specific set of questions relevant to that level of detail. The OpenWorm project has developed c302, a framework in Python which aims to facilitate creation of models of the nervous system and musculature of C. elegans, comprising all known cells or subsets thereof, and incorporating varying levels of detail for neurons, muscles and synapses. Information on the numbers, types, and polarity of synaptic connections are included in the models from the structured information on these gathered by the project. The models generated can be used with a variety of tools for model visualisation, simulation and analysis. Dr Padraig Gleeson will present c302 and show how these nervous system models are being incorporated into detailed 3D worm body models in OpenWorm.
Taming complexity: controlling networks
Professor Albert-László Barabási, Northeastern University and Harvard Medical School, USA
The ultimate proof of our understanding of biological or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered and natural systems towards a desired state, we lack a framework to control complex self-organized systems. Here Albert-László will explore the controllability of an arbitrary complex network, identifying the set of driver nodes whose time-dependent control can guide the system’s entire dynamics. Virtually all technological and biological networks must be able to control their internal processes. Given that, issues related to control deeply shape the topology and the vulnerability of real systems. Consequently, unveiling the control principles of real networks, the goal of our research, forces us to address series of fundamental questions pertaining to our understanding of complex systems. Finally, Albert-László will discuss how control principles inform our ability to predict neurons involved in specific processes in the brain, offering an avenue to experimentally falsify and test the predictions of network control.
Geppetto - An open platform for biology data exploration, visualization and simulation
Mr Matteo Cantarelli, OpenWorm Foundation, USA
Geppetto (geppetto.org) is an open-source web-based platform to explore and simulate neuroscience data and models. The platform, originally designed to support the simulation of a cell-level model of C. elegans as part of the OpenWorm project, has grown into a generic framework suitable for various neuroscience applications, offering out of the box solutions for data visualisation, integration and simulation. Geppetto is today used by Open Source Brain (opensourcebrain.org), to explore and simulate computational neuroscience models described in NeuroML version 2 with a variety of simulators and by the Virtual Fly Brain (virtualflybrain.org) to explore and visualise anatomy (including neuropil, segmented neurons and gene expression pattern data) and ontology knowledge base of Drosophila melanogaster. Geppetto is also being used to build a new experimental UI for the NEURON simulation environment based on Python and Jupyter. WormSim (wormsim.org) embeds Geppetto to let users explore dynamic mechanical and electrophysiological models of C. elegans produced by the OpenWorm project. Geppetto is capable of reading and visualising experimental data in the NWB format (nwb.org) to allow experimental and computational neuroscientists to share and compare data and models using a common platform.
Reproducibility and rigour: testing the data driven model in C. elegans
Professor Sharon Crook, Arizona State University, USA
Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the mechanisms underlying neuronal and network dynamics. We have contributed to a successful system of interoperable, open source tools to address issues around creating, exchanging, and re-using models in neuroscience. In spite of this promising movement toward model sharing and reproducibility in the neuroscience community, it is extremely rare to see a specific, rigorous statement of the criteria used for evaluating models against experimental data. Another collaborative project from our group is providing a flexible infrastructure for assessing the scope and quality of models. The goal is to integrate experimental data with modeling efforts for more efficiency, better transparency, and greater impact of computational models in neuroscience research. We highlight examples of model validation from the C. elegans nervous system and also propose how hierarchical model validation, proceeding from the testing of small model components all the way to entire systems, can be used to systematically build a biologically-inspired model of an entire organism.