Past and Recent Endeavours to Simulate Caenorhabditis elegans
Alexey Petrushin, Lorenzo Ferrara and Axel Blau
Dept. of Neuroscience and Brain Technologies (NBT), Italian Institute of Technology (IIT), 16163 Genoa, Italy
Keywords: Brain-Inspired Computation, Nervous System Emulation, Soft Body Simulation, Virtual Embodiment,
Neurocomputational Response Models on Field-Programmable Gate Arrays (Fpgas).
Abstract: Biological nervous systems are robust and highly adaptive information processing entities that excel current
computer architectures in almost all aspects of sensory-motor integration. While they are slow and
inefficient in the serial processing of stimuli or data chains, they outperform artificial computational systems
in seemingly ordinary pattern recognition, orientation or navigation tasks. Even one of the simplest nervous
systems in nature, that of the hermaphroditic nematode Caenorhabditis elegans with just 302 neurons and
less than 8,000 synaptic connections, gives rise to a rich behavioural repertoire that – among controlling
vital functions - encodes different locomotion modalities (crawling, swimming and jumping). It becomes
evident that both robotics and information and computation technology (ICT) would strongly benefit if the
working principles of nervous systems could be extracted and applied to the engineering of brain-mimetic
computational architectures. C. elegans, being one of the five best-characterized animal model systems,
promises to serve as the most manageable organism to elucidate the information coding and control
mechanisms that give rise to complex behaviour. This short paper reviews past and present endeavours to
reveal and harvest the potential of nervous system function in C. elegans.
1 INTRODUCTION
Caenorhabditis elegans, a tiny roundworm (L:
1 mm, Ø 80 µm) with a life span of a few weeks, is
among the five best characterized organisms in
nature (Epstein and Shakes, 1995). With only 2% of
its population being males, the nematode proliferates
predominantly as a quasi-clone through
hermaphrodites. These are comprised of exactly 959
cells, including 95 body wall muscle cells and 302
neurons that fall into 118 classes (Altun and Hall,
2009; 2015). Their nervous system has been
completely mapped by electron microscopy (J. G.
White et al., 1986). The nematode’s behaviour and
its underlying operation principles are the subject of
numerous past and ongoing studies (Bono and Villu
Maricq, 2005; Corsi et al.,2015), which has led to an
extensive body of knowledge on this creature. This
inspired biologists and neurocomputational
researchers at the end of the last century to simulate
not only the C. elegans nervous system, but the
organism and its development in its entirety. We will
briefly summarize and discuss the outcome of these
projects to then focus on the scope of three recent
simulation initiatives, the OpenWorm, the
NEMALOAD and the Si elegans projects.
2 PAST PROJECTS ON
SIMULATING C. elegans
With the advent of sufficiently powerful
computational resources in the 80’s of the last
century, researchers discovered computers for the
simulation of all kinds of natural phenomena, among
them the events in nervous systems. Modelling
neural systems has diverse roots and inspirations,
most of them being inductively derived from first
principles
(e.g., (Hodgkin and Huxley, 1952)) or
deduced from direct observation. The nematode
C. elegans was considered as an ideal system to start
with (Achacoso and Yamamoto, 1992). In 1997,
researchers at the University of Oregon (USA)
proposed
NemaSys. It aimed at developing a
computer simulation environment for C. elegans to
support basic research and education in C. elegans
and systems computational neuroscience. Due to
C. elegans’s simplicity, an anatomically detailed
model of the entire body and nervous system was
perceived as an attainable goal. In a concerted effort
employing electrophysiology, calcium imaging,
quantitative behavioral analysis, laser ablation and
mathematical modelling, one outcome was the
Petrushin, A., Ferrara, L. and Blau, A..
Past and Recent Endeavours to Simulate Caenorhabditis elegans.
In Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2015), pages 133-137
ISBN: 978-989-758-161-8
Copyright
c
 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
133

identification of the mechanism and simple
computational rules by which C. elegans computes
the time derivative of chemosensory input (Ferree
and Lockery, 1999). The results were transcoded
into a phototaxis response algorithm to control and
analyse the trajectories of a custom-made robot
(Morse et al., 1998).
In 1998, ‘The Perfect C. elegans Project’, a
collaboration between researchers with Sony, the
Keio University in Japan and the University of
Maryland in the USA, targeted at introducing
synthetic models of C. elegans to further enhance
our understanding of the underlying principles of its
development and behaviour, and life in general.
Initial efforts focused on a realistic simulation of a
subset of biological observables by providing a Java-
based visualization tool for embryogenesis including
cell position, kinematic interactions between cells,
cell division, cell fate, neural connections and
thermotaxis. Ultimately, a complete synthetic model
of the nematode’s cellular structure and function,
including genetic interactions, was envisioned. The
concepts and first steps were outlined in an initial
report (Kitano et al., 1998).
In 2004, researchers at the Hiroshima and Osaka
universities in Japan aimed at developing a virtual
C. elegans in the ‘Virtual C. elegans Project’. Based
on data on the spatial and structural layout of the
nematode, they proposed a dynamic body model
with muscles to analyse motor control. It was
founded on a neural oscillator circuit to generate
rhythmic movement. It could be shown that the
model qualitatively generates rhythmic movements
similar to wildtype and mutant nematodes. Another
demonstration was a real-coded genetic algorithm to
drive a kinematic locomotion model that responded
to gentle-touch stimuli (Suzuki et al., 2005; Suzuki,
et al., 2005a).
3 ONGOING PROJECTS ON
SIMULATING C. elegans
The OpenWorm project (USA, 2011-present) is an
international open science project to simulate
C. elegans at the cellular level as a bottom-up
simulation on standard computers. The long-term
goal is to model all 959 cells of the C. elegans
hermaphrodite. The first stage is to describe the
worm's locomotion by simulating the 302 neurons
and 95 muscle cells (Szigeti et al., 2014). Among the
currently available modules are a realistic flexible
worm body model including the muscular system
and a partially implemented ventral neural cord (A.
Palyanov et al., 2011; Openworm Browser, 2014). It
is based on the location dataset compiled by the
‘Virtual Worm Project’, an initiative at the
California Institute of Technology that creates an
interactive atlas of the hermaphrodite’s cell-by-cell
anatomy (Grove and Sternberg, 2011).
Around the same time, NEMALOAD
(‘nematode upload’; USA, 2012-present) initiated
the integration of a number of recent experimental
imaging technologies (Marblestone et al., 2013;
Schrödel et al., 2013) to learn how one neuron
affects another in C. elegans. The project is
structured in four subsequent stages that build on
one another. In the molecular biology stage,
C. elegans strains shall be functionalized with
optogenetically encoded sensors and actuators (e.g.,
calcium indicators, photo-stimulators and inhibitors)
for the tracing and manipulation of neural activity.
In the imaging stage, this activity flow shall be
recorded in freely behaving worms at neuronal
resolution. In the perturbation stage, individual
neurons shall be excited optically by means of a
custom-made two-photon digital holography system
to map their contributions to a certain behaviour. In
the final modelling stage, automation tools for the
correlation of neural activity with behaviour shall
allow the development of a dynamic model of the
worm's behaviour in a simulated environment to
mirror the experimentally observed behaviour in its
natural or laboratory environment. This shall
elucidate the underlying information processing
structure.
The most recent concerted effort in emulating
C. elegans is the Si elegans project (EU, 2013-
present). It will provide a closed-loop, open-
access/open-source, peer-contribution platform
being based on brain-mimetic principles for the
emulation and reverse-engineering of C. elegans
nervous system function in a behavioral context.
Thus, the overall objectives are very similar to
previous endeavours. The chosen approach is
slightly different, though. The nervous system will
consist of a dedicated hardware infrastructure. It will
be based on 302 field-programmable gate arrays
(FPGAs), a parallel architecture by nature. The
FPGAs will accommodate distinct neural response
models represented by freely reconfigurable
electronic circuits, one for each C. elegans neuron.
These models may be dynamic (Machado et al.,
2014; Machado et al., 2015). The nematode’s
connectome will be implemented by a light-
projection scheme to warrant interference-free,
parallel information transfer with high temporal
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fidelity (Petrushin et al., 2014; Petrushin et al.,
2015). This biomimetic hardware nervous system
emulation will be controlling a virtually embodied
and physically realistic representation of the
nematode (via soft body physics) in an equally
realistic virtual behavioral arena (e.g., an agar Petri
dish) (Mujika et al., 2014). In there, the virtual
C. elegans will encounter commonly tested stimuli
(e.g., touch, chemicals and/or temperature gradients)
at any pre-defined time. Its sensory experience will
be transmitted to the sensory neurons in the FPGA
network. Based on published knowledge on
network-internal circuitry and signal processing
pathways, the sensory input (and proprioceptive
information) will generate a motor output to instruct
the muscles of the virtual worm on what to do next.
In this closed-loop scenario, it will furthermore be
possible to read out any network state (e.g., synaptic
weights) at any given time for the reverse-
engineering of network function. To make the
Si elegans framework user-friendly for novice and
expert users alike, several model generation (e.g.,
drag-and-drop) and import functionality (e.g., from
existing simulation engines) will be provided
(Krewer et al., 2014). Once the chosen models
generate an output that is comparable to
observations in real laboratory experiments, the
platform will allow the neuroscience community to
better understand, if not anticipate, the neural
mechanisms underlying behaviour. A first version
of the Si elegans platform is expected to go online in
late 2016 for public access and use.
4 SELECTIVE LITERATURE
SURVEY ON RECENT C. elegans
LOCOMOTION MODELS
The most accessible circuit in C. elegans is its body-
wall muscle control system responsible for
locomotion consisting of 75 motor neurons (out of a
total number of 113 motor neurons) of 8 classes that
innervate 79 body wall muscle cells arranged along
the dorsal and ventral cords (Riddle et al., 1997;.
Altun and Hall, 2009; Gjorgjieva et al., 2014; Zhen
and Samuel, 2015). Its output can be visualized
rather easily and is thus verifiable by direct
comparison with time-lapse images of worm
movements. Therefore, the majority of publications
on simulating C. elegans focuses on various aspects
of the sensory-motor loop (Lockery, 2011; Cohen
and Sanders, 2014; J. Gjorgjieva et al., 2014; Zhen
and Samuel, 2015) and its driving inputs (W. R.
Schafer, 2015). Diverse strategies have been
proposed of which only a few are mentioned.
Among them are event-driven models, an
asynchronous system based on pulse modulation
(Claverol et al., 1999), compartmental conductance-
based models exclusively for muscle cells (Boyle
and Cohen, 2008), neuromuscular control systems
that rely on a sensory feedback mechanism based on
bistable dynamics without the need for a modulatory
mechanism except for a proprioceptive response to
the physical environment (Boyle et al., 2012),
dynamic neural networks based on a differential
evolution algorithm in the head and body with a
central pattern generator in between acting on a
locomotion model with 12 multi-joint rigid links
(Deng and Xu, 2014), evolutionary algorithms for
the identification of a minimal klinotaxis network
(Izquierdo and Beer, 2013) and genetic algorithms to
train 3680 synaptic weights within the motor
connectome to replicate behaviours based on
sensory–motor sequences (Portegys, 2015). At this
point, we still lack some of the electrophysiological
and biochemical data (e.g., on the role and effect of
neuromodulators) to decide which of these
approaches (or a combination thereof) best reflect
the biological events that drive locomotion.
5 DISCUSSION
When Sydney Brenner proposed C. elegans as a
model organism to the Medical Research Council
(MRC) in the U.K. in 1963, he stated that 'We intend
to identify every cell in the worm and trace lineages'
(Brenner, 1963). While this goal has been
accomplished, it became clear that this information
is not sufficient to deduce the cells’ contributions to
behaviour. Several key questions are still
unanswered. One of them is our lack of biological
knowledge that would instruct us to what level of
detail a simulation has to drill down to let realistic
behaviour emerge. Will we need to uncover and
formalize the entirety of the molecular machineries
that underpin worm biology or will a more
abstracted, thermodynamics-inspired description
faithfully elicit the observed behaviour in silico?
Although we know most of the neurons’ role and
purpose (e.g., sensory, interneuron, motor,
projection, local/solitary), little is known about the
identity (excitatory or inhibitory) and relevance of
the individual connections (including gap junctions).
Furthermore, evidence suggests the existence of
parallel, sometimes opposing (inhibitory vs.
excitatory) circuits. Similarly challenging are
divergent circuits from a common starting point to
different endpoints. In addition, the neural dynamics
Past and Recent Endeavours to Simulate Caenorhabditis elegans
135

of different neurons are not uniform and even vary
between individuals. Moreover, they may be
modulated by extrasynaptic neural activation
mechanisms including diffusible biochemical
regulators (e.g., neuromodulators) or physical
parameters (e.g., temperature, proprioception)
(Bargmann and Marder, 2013). These, in turn, may
vary with internal states (e.g., starved vs. satiated)
and the environmental conditions. On top of that,
synapses are constantly remodelled not only in
response to behavioral experience, but in a context-
sensitive and time- or activity-dependent manner on
the timescale of milliseconds to weeks (Friston,
2011). Thus, C. elegans’s neural circuit, despite its
quasi-static wiring diagram, features many dynamic
and difficult to capture mechanisms that encode
different behavioral outcomes.
Due to this complexity and the many unknowns,
any simulation approach is almost doomed to start
with naïve and oversimplified assumptions. No
matter how a simulation framework is
conceptualized, the above findings strongly suggest
keeping it as flexible, extensible and scalable as
possible to accommodate new insights into the
mechanisms that govern nervous system function
underlying a particular behavioral phenotype. This
may include the deviation from standard reasoning:
instead of building population or neuron-specific
response models (E. Marder & A. L. Taylor, 2011),
an even more fine-grained approach may become
necessary that provides a variety of adaptive models
for one and the same neuron each responding to
context-specific events.
ACKNOWLEDGEMENTS
The Si elegans project 601215 is funded by the 7
th
Framework Programme (FP7) of the European
Union under FET Proactive, call ICT-2011.9.11:
Neuro-Bio-Inspired Systems (NBIS). We thank all
project collaboration partners for their contributions
and helpful discussions.
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