hpc at nibr - hpc advisory council€¦ · hpc at nibr nick holway scientific computing group, nibr...

HPC at NIBR

Nick Holway

Scientific Computing Group, NIBR

HPC Advisory Council, Lugano

Twitter: @nickholway LinkedIn: https://www.linkedin.com/in/nickholway/

April 2017

Novartis Institutes for

Biomedical Research

(NIBR)

HPC means we can get better, more targeted drugs to patients quicker

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Novartis Institutes for Biomedical Research

Today’s talk

1. Introducing Novartis

2. A very quick introduction to drug discovery

3. What our HPC looks like

4. Examples of how we use HPC to accelerate drug discovery

5. Outlook

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Introducing Novartis


Novartis

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Innovative Medicines

Sandoz

Pharmaceuticals business unit

Oncologybusiness unit

Alcon

R&DR&D


Drug discovery and early development

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~6,000Scientists /

7 sites globally

~90New Molecular Entities

~400Research projects

>500Ongoing

clinical trials>400 Computational Scientists


NIBR

ATI

CVM

Other

ID

MSD

ONC

IO

OPH

RESP

NEURO

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Organised around prevalent Disease Areas

Note: Distribution of ~90 New Molecular Entities at NIBR

Immuno-Oncology

Oncology

Ophthalmology

Respiratory Diseases

Neuroscience

Autoimmunity, Transplantation & Inflammation

Cardiovascular & Metabolism

Infectious Diseases

Musculoskeletal

A very quick introduction to

drug discovery


What’s a drug?

• ”A pharmaceutical drug is a drug used to diagnose, cure, treat, or prevent

disease” (https://en.wikipedia.org/wiki/Pharmaceutical_drug)

• Examples of some medicines and their “mechanisms”:

– Cancer: blocking cells dividing by disrupting DNA replication or the cells’ internal

skeletons

– Infectious diseases: disrupting bacterial cell walls

– Depression: Preventing serotonin re-uptake in nerve cells in the brain

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https://en.wikipedia.org/wiki/Pharmaceutical_drug)


The path to a new medicine

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Discovery Clinical trials EvaluationPost-

approval

Target

selection

Drug

research and

design

Preclinical

research

Proof of

Concept

5–15patients

Phase I

20–100healthy

volunteers

and/or

patients

Phase II

100–500patients

Phase III

1000–5000patients

Submission

Review by

regulatory

authority

Phase IV

Post-marketing

surveillance and

research

Manufacturing

Investigational New Drug (IND)

Application submitted

NDA/ BLA*

submittedApproval of

one new medicine

10 – 15 years

>10 000Compounds

<250Compounds

<5Compounds

*New Drug Application / Biologics Licence Application

What our HPC looks like


HPC at NIBR - Hardware

• x86 servers– Intel Xeon CPUs– 128-768GB RAM– FDR Infiniband– 10GigE

• Specialised nodes– Nvidia GPUs– >=1TB RAM

• Isilon storage – CIFS/NFS– 10GigE to Arista switches

• Lustre– Scratch

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HPC at NIBR - Software

• RHEL 6.x

• Univa Grid Engine for scheduling

• Software compilation & configuration

– Easybuild

– Modules

– GCC, Intel, Nvidia compilers

• Languages: C++, Fortran, CUDA, Python, R, Matlab

• Libraries: *MPI, MKL etc

• The software stack is identical on Linux desktops and “scientific servers”

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HPC at NIBR - Humans

• HPC is provided by the Scientific Computing Group (SciComp)

• Global team (Europe, USA, Asia)

• Complementary backgrounds and skills

– Sysadmins

– Mathematicians

– Scientists

• HPCWire award winners in 2014

• Other teams in NIBR Informatics provide storage, Linux servers, etc.

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HPC at NIBR: Community

• We’ve worked very hard to build an interdisciplinary group of informatics

scientists to share knowledge

• Various activities

– Fortnightly informal talks

– Social events

– Deep Learning “bootcamp”

– 24hr virtual multi-site workshop (Shanghai -> California!)

• This started out from the grassroots and has now been formally funded within

the Company

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HPC elsewhere in Novartis

• Today’s talk covers Research; however HPC is used elsewhere in the

Company for

– Modelling Drug absorption, metabolism & secretion (PK/PD)

– Processing data from Clinical Trials

– Predicting where in the lungs inhaled drugs go (CFD)

• The cluster used for this work is much more tightly controlled and tested than

NIBR’s systems

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Examples of how we use HPC

to accelerate drug discovery


Using HPC in early drug discovery

• There are many different ways NIBR scientists use HPC

– Molecular dynamics

– NGS analysis

– Ligand-protein docking

– Image analysis

– Cryo-EM analysis

• Our usage is similar to a university with biology and chemistry departments

• In today’s talk I’ll focus on using HPC to accelerate phenotypic assays

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Phenotypic assays

• Traditionally our scientists have used biochemical assays in early stage drug

discovery

– Assays use an isolated enzyme or protein and measure fluorescence etc.

– This tells us very little about the cells and how they react

• Increasingly our scientists are using “phenotypic assays” using cells grown in a

lab

– Scientists can see the impact of their drug on an entire cell or population of

cells

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Example: wound healing

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24 hrsImages from http://cellprofiler.org/examples/#Wound


What is High Content Screening

(HCS)

• A method for identifying molecules which alter the phenotype of cells (eg cell

shape, number etc) or small organisms (eg Malaria parasites)

• Using robotics & automated microscopes a large number of potential drugs can

be ”screened” in a few hours or days

• Assays can generate a lot of data

– Videos

– Millions of images

– >600TB/yr for some HCS instruments

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Accelerating MND/ALS disease

research with GPUs

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In-vitro model for neuromuscular

junctions

• Faulty junctions between motor neurons and muscle cells are implicated in

MND/ALS

• We’d like to create a drug which corrects this

• Motor neurons & myotube (muscle fibre) cells were “co-cultured” in a “plate” to

which drug candidates are added

• Cells were imaged in real time to measure their contractility

• This is very hard to see by eye and also hard to segment using computers

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What do the cells look like?

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Figure: I Hossain


Motion estimated with Optic Flow

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Different contracting regions

Total area under contraction

Figure: I Hossain


Impact of HPC

• A good joint project between bench scientists, lab automation experts &

informaticians

• 80x increase of throughput compared to CPU

• NIBR scientists have access to new method of monitoring myotube contractility

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Deep learning for HCS image

analysis

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CNNs for HCS image analysis

• HCS analysis is traditionally performed using tools such as CellProfiler, Fiji or

commercial tools

• Deep Learning approaches are becoming increasingly used for image analysis

• A team has investigated Convolutional Neural Networks for deriving images’

phenotypes

• They used only the images’ pixel intensity values with no a priori knowledge

• They used public and Novartis datasets

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Outcome

• The images were classified better than conventional methods

• This is included tracking a response to drugs

• There was no need to design a unique pipeline for the processing

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Interested in knowing more?

• This work has been published (including some code) in Godinez et al: “A multi-

scale convolutional neural network for phenotyping high-content cellular

images”, Bioinformatics btx069, https://doi.org/10.1093/bioinformatics/btx069

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https://doi.org/10.1093/bioinformatics/btx069


Pushing HPC to non-technical

scientists

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Why bench scientists need HPC

(and don’t realise it!)

• Bench scientists generally do not know how to programme or use the Linux

command line

• Many scientists’ data has grown too big to be processed on a single

workstation

• This means they have to wait a long time for the data to be processed and also

they may need to wait for an informatician to become available

• If you can give a scientist the tools to analyse their data at scale then they get

their data sooner and enables the informaticians to focus on more complex

tasks

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Pushing HCS analysis to bench

scientists

• Our scientists create pipelines using CellProfiler (http://cellprofiler.org/) using

the normal GUI on their laptops

• The Pipeline is then uploaded to a central server at each site

• The scientist can kick off a analysis run on the cluster using the same webpage

that they use to visualise their images

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http://cellprofiler.org/)


Screenshots of the GUI

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Also (ab)using Jenkins

• Our scientists have automated cluster submission using the continuous integration tool, Jenkins, again with a web front end

• The work has been published: https://doi.org/10.1177/1087057116679993

• Source freely available at https://github.com/Novartis/Jenkins-LSCI

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https://doi.org/10.1177/1087057116679993

https://github.com/Novartis/Jenkins-LSCI

Outlook


HPC Trends

• GPUs / Intel Phi / FPGAs

– Deep learning

– Cryo-EM

• Real time collection & processing of data from clinical trials

• Integration of “big data” technologies such as Apache Spark into HPC

• HPC in the cloud

– Currently most useful for bursting or embarrassingly parallel jobs

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Thank you

Back up

HPC in the cloud


HPC in the cloud

• NIBR have used Amazon EC2 for compute workloads

– Cycle computing

• ISVs eg DNANexus

– Bioinformatics NGS

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Docking at scale in the cloud

• Ligand-protein docking is “to predict the position and orientation of a ligand (a

small molecule) when it is bound to a protein receptor or enzyme” (Wikipedia)

• Embarrassingly parallel - compute-heavy / data-light

• We used the cloud to screen 10 million molecules against a cancer target

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How we did it

• Cycle computing’s software (Cycle server, Cyclecloud)

• Over 10,000 EC2 spot instances

– Extensive benchmarking to select instance type

• Licence files (licence servers cannot cope with the load)

• Proprietary compounds run in NIBR’s VPC, others in “public”

• See http://opensource.nibr.com/videos/aws-litster/ and

http://cyclecomputing.com/novartis-taps-cloud-hpc-for-faster-drug-discovery-

better-science/

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http://opensource.nibr.com/videos/aws-litster/

http://cyclecomputing.com/novartis-taps-cloud-hpc-for-faster-drug-discovery-better-science/


Where we’re going in the cloud

• “Cloud by default” for many non-HPC applications

• Clinical data (subject to “informed consent”)

• HPC where appropriate

– IB etc for tightly-coupled parallel jobs usually unavailable

– Data locality challenging

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