How To Find Out What A Worm Is Thinking

Identifying Behavioral Strategies through

Large Scale Phenotyping

and Statistical Analysis

Stephen Helms, Ph.D.

March 12, 2014 – SURFsara Data & Computing Infrastructure Event FOM Institute AMOLF, Amsterdam, Netherlands

Leon Avery (VCU), Greg Stephens (VU Amsterdam/OIST), Tom Shimizu (AMOLF)

How Do We Understand Complex Systems

With Many Parts?

(Also a general “big data” question!)

A Model Complex System Traditional approaches for understanding complex biological systems Statistical approach for understanding biological systems Data and computation problems Proposed computational platform

Outlook for the future

A Simple Model Nervous System:

C. elegans

Stimuli Response The Worm • ~1000 total cells • 302 neurons • 95 muscles • ~20000 genes • Smell (volatile odors) • Taste (soluble chemicals) • Feel (touch, heat) • Movement • Neural activity • Biochemical reactions

A Biologist’s Toolbox

• Break individual parts, see what

happens

Genetics

• Look at how parts chemically

interact

Biochemistry

• See where the parts are

Cell Biology

End result:

•

A list of lots of details about what individual genes and proteins are doing

Idea:

Finding Simple Models Through

Quantitative, Comparative Studies

•

Build quantitative models

that are

just

complicated

enough to explain the phenotypes

we can observe and care about

•

Compare models

across multiple strains and

species to see what phenotypes

biology

cares

about

•

The molecular and cellular details can be filled in

later using traditional approaches

•

Model system: Motile behavior

–

Behavior is the output of all the complicated systems

C. elegans Behavior

•

Undulatory motion

•

Occasional reversals

•

Occasional sharp

“omega” turns

•

Continuous turning

Gray and Lissmann (1964) J. Exp. Biol. 41:135-54, Croll (1975) J Zool. 176:159–176, Croll (1975) Adv Parasitol 13:71–122, Pierce-Shimomura et al. (1999) J. Neurosci. 19:9557-69. Iino, Y. & Yoshida, K. (2009) J. Neurosci. 29:5370-80. Helms (2013) Figshare.http://dx.doi.org/10.6084/m9.figshare.705155

Experimental Overview

Record video of freely moving worms up to 30

minutes

Sampling Behavioral Variability:

Individual, Intra- and Inter-Species

Holovachov, O. et al. (2009) Nematology 11(6):927-950. Chiang, J.-T.A. et al. (2006) J. Exp. Biol. 209(10):1859-73. Andersen, E.C. et al. (2012) Nat. Genet. 44(3):285-90. Up to 20 individuals

Building Quantitative Models

• Correlation functions • Phase spaces

• Fitting linear models

Deterministic dynamics

• Distributions

Stochastic components

• Monte carlo simulations • Comparison with

statistics of data

Comparing Quantitative Models

Parameter

Data Challenges

Storage

• Videos are large • 240 GB/h raw

• 12 GB/h compressed • Using ~1 TB of storage

for a proof of concept project

• Want to scale up: • # individuals by

10-fold

• Sampling rate by 3-fold

Processing

• >3-fold slower than data collection on a desktop computer • Results in:

• A backlog of data to analyze

• A long delay before experiments can be interpreted

Sharing

• Videos are too big to regularly transfer around

• Extracted data is also big

• 2 GB for the proof of concept project

• Limited ability for others to explore the data themselves

Proposal:

Centrally located

data processing

and

analysis services

at SARA

SARA

Video storage Video processing Standard analyses

Experimental Users (AMOLF, VCU, etc.)

Generate videos Visualize data Develop analyses

Theory Users (VU, OIST, etc.)

Visualize data Develop analyses

Exchange datasets and analysis results (few GBs, weekly)

Upload videos Download datasets

(hundreds of GBs, daily at peak)

Download datasets (tens of GBs, weekly) •Loading large (>10 GB) videos •Processing 104_-106 frames / video

How SURFnet/SURFsara/eScience Center

Are Helping

Storage

• SURFsara will provide up to 20 TB of storage for the video data

Processing

• SURFsara will provide computing resources • Cloud or grid

• eScience Center is helping with migrating analysis code to run on HPC infrastructure

Sharing

• SURFnet is connecting the involved institutes with SURFsara using high-speed lightpath connections • FOM Institute AMOLF • VU • Okinawa Institute of Science and Tech. • Virginia

Commonwealth University

Growth Prospects

• Open source aspects of C. elegans community

– WormBook - textbook – WormBase - genetics – WormAtlas - anatomy – etc.

• As an analysis service available to other researchers

– Motility is widely used as a simple phenotype by C. elegans researchers

• Collaborative development of new analysis methods

– Other researchers developing statistical analysis approaches for worm behavior

• Integration of neuronal imaging data

– Ongoing experiments in the systems biology group at AMOLF

These Are General Challenges

• Increasing temporal and spatial

resolution



more data

Advances in

imaging sensors

• Increasing experimental throughput



more data, access to statistical

approaches

Advances in

experimental

techniques

• Distortion of data due to compression

artifacts is a major concern among

experimentalists

Lack of

compression

Acknowledgements

•

Enlighten Your Research 4 and Global Teams

–

Nicole Gregoire (SURFnet)

–

Sylvia Kuijpers (SURFnet)

–

Jan Bot (SURFsara)

–

Frank Seinstra (eScience Center)

•

eScience Center

–

Rob van Nieuwpoort

–

Elena Ranguelova

•

Everyone else involved @ SURFnet, SURFsara

•

Local ICT members