Input Encoding Proposal for Behavioral Experiments with a Virtual

C. elegans Representation

Gorka Epelde

, Andoni Mujika

, Roberto Álvarez

, Peter Leškovský

, Alessandro de Mauro

Finn Krewer

and Axel Blau

eHealth and Biomedical, Vicomtech-IK4, Mikeletegi Pasealekua, Donostia-San Sebastián, Spain

College of Engineering and Informatics, National University of Ireland Galway, University Road, Galway, Ireland

Dept. of Neuroscience and Brain Technologies (NBT), Fondazione Istituto Italiano di Tecnologia (IIT), 16163 Genoa, Italy

Keywords: Behavioral Experiment Input Encoding, C, elegans, Complex Systems Simulation, in Silico Simulation.

Abstract: This paper discusses a Caenorhabditis elegans (C. elegans) nematode behavioral experiment input

encoding. It proposes a common digital representation for behavioral studies with C. elegans. This work is a

step forward towards the reproducibility and comparability of in silico simulations of the nematode with

real-world experiments. The digital representation is divided into environmental and experimental

configurations. The behavioral input is structured by duration-based behavioral experiment types at the top

level (i.e. interaction at a specific time, interaction from t

– t

and overall duration) and by interaction type

(i.e. mechanotaxis, chemotaxis, thermotaxis, galvanotaxis and phototaxis) for each duration-based type. The

environment configuration is composed of the identification of the worm’s mutation type, worm crowding,

initial location, configuration of the assay plate, and obstacle settings. Parameters are defined by an XML

schema to ensure the interoperability with other simulation solutions. It is being implemented and tested in

the context of the Si elegans project.

1 INTRODUCTION

The reproducibility and comparability of

experiments is key to scientific progress. Small

variations in the conditions of an assay or in the

applied behavioral stimuli can dramatically change

the results of an experiment (Hart, 2005). Based on

this consideration, researchers usually detail their in

vivo experiments with respect to the animal’s

properties, the environmental conditions and the

applied stimuli.

For the animal of interest, the nematode

Caenorhabditis elegans (C. elegans), the scientific

community has already detailed the requirements for

in vivo behavioral assays such as the definition of

the type of animals to be assayed with respect to

control strains, their feeding status, cultivation

conditions, the ambient conditions, the scoring of

perceived behavior, the statistical analysis and the

reporting of results (Hart, 2005). Once defined, the

different assay considerations detail each C. elegans

behavioral experiment, which can then be grouped

by the observed behavioral response types.

With the advent of the in silico modelling and

simulation of living organisms in different

biomedical research areas (e.g., computational

neuroscience or systems biology), the minimum

information content of a simulation needs to be

agreed on in order to ensure reproducibility within

simulations, comparability between different

simulation systems and comparability between in

vivo and in silico experiments (Waltemath et al.,

2011). In this respect, different initiatives have

attempted to standardize the computational models,

simulation experiment definitions, graphical

visualizations, results provision, and vocabularies

including the provision of guidelines on their

effective reuse. To the best of our knowledge the

existing approaches do not support the encoding of

inputs for behavioral experiments with simple life

forms but rather target on the description of inputs at

signal level, as is considered by distinct biological

models of cells or neurons.

The contribution of this paper is the proposal of

a digital representation to encode simulated

experiments and stimuli at behavioral level for C.

elegans.

Epelde, G., Mujika, A., Alvarez, R., Leškovský, P., Mauro, A., Krewer, F. and Blau, A..

Input Encoding Proposal for Behavioral Experiments with a Virtual C. elegans Representation.

In Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2015), pages 115-120

ISBN: 978-989-758-161-8

115

We first analyze the related work in the field of

simulation experiment reproducibility (Section 2).

Section 3 presents an innovative approach to define

the behavioral experiment input for the C. elegans

nematode simulation experiments. In Section 4, we

summarize the paper and present our conclusions.

2 RELATED WORK

In the in silico biomedical modelling and simulation

research areas, efforts have aimed at standardizing

different models and processes as well as integrating

specialized software into unified platforms (Amari et

al., 2002; Cannon et al., 2007; Teeters et al., 2008;

Ghosh et al., 2011; Dräger et al., 2014). Their main

goal was the reproducibility, comparability of

simulated experiments and the reusability of

building blocks to provide the integration of more

complex simulations or of multilevel modelling and

simulation tools.

In the systems biology research area, (Dräger et

al., 2014) examined diverse modelling standards and

data formats that are currently in use within the

scientiﬁc community together with databases, from

which relevant resources that conform with these

formats can be obtained. (Ghosh et al., 2011)

described the types of software tools that are

required at different research stages, current options

that are available for researchers, challenges and

prospects for modelling the effects of genetic

changes on physiology and the concept of an

integrated biomedical research platform.

In the computational neuroscience research area,

(Cannon et al., 2007; Davison et al., 2009; Crook et

al., 2013) examined the interoperability and the

interfacing of neuroscience modelling software and

neuronal network simulations. (Amari et al., 2002;

Teeters et al., 2008) discussed the data sharing and

the integration of shared databases and tools for

integrative neuroscientific research.

(Dräger et al., 2014) suggested a structure to

revise the state of the art in standardization and

interoperability efforts. The review is structured into

modeling guidelines, model encoding formats,

simulation procedures, graphical model visualization

and numerical results representation.

Modelling guidelines generally include

requirements for the minimum information given in

an experiment as well as ontologies that describe the

controlled term vocabularies that should be used in

the model encoding formats. The Minimum

Information Required in the Annotation of Models

(MIRIAM) guidelines promote the exchange and

reuse of biochemical computational models (Le

Novère et al., 2005). While mentioning the need for

result reproducibility, MIRIAM does not cover the

minimum requirements necessary for simulating the

models. In contrast, the Minimum Information

About a Simulation Experiment (MIASE) sets out to

define the minimum requirements for simulation

descriptions, and allows thus for unambiguous

reproduction of experiment (Waltemath, Adams,

Beard, et al., 2011). With respect to ontologies, the

Kinetic Simulation Algorithm Ontology (KiSAO)

organizes algorithms to simulate models (Courtot et

al., 2014), and the Systems Biology Ontology (SBO)

proposes a collection of terms that describe the

structure of a model, its components as well as

modeling frameworks and processes (Courtot et al.,

2014). With a focus on C. elegans, a hierarchically

structured, controlled vocabulary of terms that

standardizes phenotype descriptions, namely the

Worm Phenotype Ontology (WPO), is defined

(Schindelman et al., 2011).

Several formats that encode biological models

have been proposed. The most representative

formats include the Systems Biology Markup

Language (SBML) to represent biochemical network

models (Hucka et al., 2003), CellML for defining

mathematical models of cellular functions (Lloyd et

al., 2004) and NeuroML for describing data-driven

models of neurons and neural networks (Gleeson et

al., 2010). Regarding inter-model interoperability,

PyNN (Python Neural Networks) is a programming

interface common to multiple neuronal network

simulators, which allows to write a simulation script

once in the Python programming language and run it

without modiﬁcation on any supported simulator

(Davison et al., 2009).

With respect to simulation procedures, the

Simulation Experiment Description Mark-up

Language (SED-ML) provides a standardized,

machine-readable format (Waltemath, Adams,

Bergmann, et al., 2011) for the information required

by MIASE to enable the reproduction of simulation

experiments (Waltemath, Adams, Beard, et al.,

2011). Besides models identification, MIASE

requires a precise description of simulation steps

together with all the information for obtaining

numerical results to be reported in scientific

publications. In SED-ML documents, the simulation

experiment input is defined at a low model

parametric level because the models used e.g.,

SBML, CellML or NeuroML are biological models.

It is therefore not well suited for defining the

behavioral experiment input of a living organism in

an in silico simulation.

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Graphical visualizations of biological models

have been used to facilitate the understanding of a

model in publications and textbooks. As in other

analyzed parts of a simulation component,

standardization efforts have led to visualization

recommendations and data formats for the exchange

of the resulting visualization. Representative efforts

are (i) the Systems Biology Graphical Notation

(SBGN) (Le Novère et al., 2009) and the

corresponding markup language (SBGN-ML)

developed to visualize, store and exchange the

visualization of biological networks (Iersel et al.,

2012); and (ii) a framework defined for visualization

of CellML models, which allows the visualization of

the physical model or its biological interpretation

(Wimalaratne et al., 2009).

Finally, specific file formats have been proposed

for numerical results provision. The Numerical

Markup Language (NuML) (NuML Project, 2015),

originates from the numerical aspects of the Systems

Biology Results Markup Language (SBRML) (Dada

et al., 2010), with the aim of reusing it in multiple

other standardization efforts. In the area of

presenting results, ongoing work is carried out by

the OpenWorm project to validate the results of

behavioral simulation experiments and to compare

the perceived locomotion behavior of in vivo

experiments with in silico experiments (OpenWorm

Project, 2015).

Behavioral experiments are run by psychologists

for neurobehavioral research on humans (Mueller et

al., 2014) by using tools such as (Neurobehavioral

Systems, 2015). But we do not see a direct way for

exploiting these approaches in living organisms’ in

silico experiments.

After reviewing the related work on

interoperability and standardization of

computational neuroscience and systems biology

research areas, no specific solution seems to exist

that tackles the behavioral experiment input

encoding in general or specific to the C. elegans

nematode.

3 MATERIAL AND METHODS

According to rule 2B of the MIESE guidelines

(Waltemath et al., 2011), all information needed for

the correct implementation of the necessary

simulation steps must be included through precise

descriptions or references to unambiguous

information sources. When it comes to projects such

as OpenWorm (Szigeti et al., 2014) or Si elegans

(Blau et al., 2014) that aim to simulate a complete

life form, a behavioral input encoding is required.

As an effort to standardize the C. elegans behavioral

experiments input encoding, the following proposal

is presented. It was inspired by a collection of C.

elegans behavior experiments described in the

WormBook (Hart, 2005) and in recent C. elegans in

vivo research reports (Gabel et al., 2007; Ward et al.,

2008). The latter cover behavioral responses to

behavioral input types that are not yet included in

the WormBook.

The proposed digital representation is divided in

the environment and the experiment configurations.

The behavioral experiment is structured by duration-

based behavioral experiment types at the top level.

The following three behavioral experiment types

have been defined:

▪ Interaction at a specific time t

▪ Interaction from t

to t

▪ Experiment-wide configuration

Each duration-based experiment allows for the

definition of one or more interactions of each type.

For each duration-based experiment type, each of

the behavioral input types (i.e. mechanotaxis,

chemotaxis, thermotaxis, galvanotaxis, and

phototaxis) as identified by (Hart, 2005; Gabel et al.,

2007; Ward et al., 2008) is allowed. Behaviors

reported by (Hart, 2005), which are not sensory

behavioral inputs (i.e. locomotion, feeding, egg-

laying, mating, reproduction or defecation), have not

been considered. Figure 1 depicts an example of a

duration-based behavioral experiment.

Figure 1: Example of an experiment definition based on

different durations. It includes two temporal types:

“Interaction from t

– t

” for a change in temperature

labelled “Temperature change in time” and others of the

type “Interaction at specific time”.

On a third level, the concrete behavioral

experiments inputs are defined in the XML schema

developed by (Si elegans Consortium, 2015a).

An example experiment definition section of a

behavioral experiment input that contains multiple

interaction instances is presented in Figure 2.

Input Encoding Proposal for Behavioral Experiments with a Virtual C. elegans Representation

117

Figure 2: Example of the experiment definition section of

a behavioral experiment input encoding.

Figure 3: Example of the environment definition section of

a behavioral experiment input encoding.

The environment configuration structure and

elements have been extracted from the WormBook

experiments review (Hart, 2005). The environment

configuration is composed of the worm’s mutation

identification, crowding, location, plate

configuration, and obstacles configuration, which

have been parameterized. An example environment

definition section of a behavioral experiment input

that describes the experiment’s environment together

with the worm’s details is presented in Figure 3. A

complete behavioral experiment input encoding

example is provided in

(Si elegans Consortium,

2015b).

4 CONCLUSIONS AND FUTURE

WORK

In this paper, we have presented a proposal for a C.

elegans behavioral experiments input encoding

strategy. Its aim is to standardize the behavioral

input configuration and the simulation parameters

(e.g., environment configuration) for different C.

elegans nematode simulation frameworks. This

standardization effort targets at an interoperability

between these frameworks to allow for reproducible

and comparable simulations and for sharing

simulation building blocks. A brief review of the

state of the art of standardization and interoperability

efforts in the computational neuroscience and

systems biology research areas has led us to define a

behavioral experiment input encoding language from

scratch. The definition procedure was introduced

and the logic behind the markup language was

explained. Additionally, an example for an

experiment was provided together with the

corresponding XML schema of the defined markup

language; a complete example as part of the Si

elegans project simulation solution is available at the

following online repository: https://github.com/Si-

elegans/behavioural_experiment_definition . Future

work will include the development of import/export

modules for interoperability with similar simulation

platforms.

ACKNOWLEDGEMENTS

The Si elegans project is funded by the 7th

Framework Programme (FP7) of the European

Union under FET Proactive, call ICT-2011.9.11:

Neuro-Bio-Inspired Systems (NBIS). Finn Krewer is

supported by the Irish Research Council under the

EMBARK funding scheme.

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