agenda - elixir · elixir innovation & sme event march 19, wageningen jakob de vlieg, vp - sr....

20/03/2015

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Computational Life Sciences Future of R&D in the big data era

ELIXIR Innovation & SME event

March 19, Wageningen

Jakob de Vlieg, VP - Sr. Expert Lead

Agenda

why do we need it?

A definition of CLS

Introducing Bayer Health Care and CropSciences R&D

Bayer CropSciences (BCS) strategy

Role of CLS to support integrative & data-driven R&D

Some real life CLS examples from CropSciences

Data! Data! Data! I can't make bricks without clay

- Sherlock Holmes

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CLS: Computational Life Sciences

A Definition:

• Improving R&D decision making and execution based on data-

driven insights

• Combining and analyzing data from multiple sources

• Participating in the value creation

• CLS is linked

with all data-

driven R&D

activities

• CLS does not

fit in a “box”

R&D Bayer HealthCare Drug Discovery at a Glance

• Bayer HealthCare Overview • March 2014 Page 4

• Hematology

• Biologics Research & Development

• Science Hub US

• Oncology

• Gynecological Therapies

• Common Mechanism Research

• Cardiology / Hematology

• Biologics Research& Development

• Common Mechanism Research

• Animal Health Research

• GDD Innovation Center China

• Bayer Tsinghua Center for Innovative Drug Discovery

Investment in R&D 2013: € 2 billion

Investment in R&D of 2013 total net sales: 10.8%

R&D Employees 2013: 7,800

Berkeley /

San Francisco

Berlin Wuppertal / Cologne /

Monheim

Beijing

Computational life sciences

External collaborations: e.g. Broad Institute and Bayer joined

forces to develop novel treatment options in cancer therapy

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Global network of R&D sites is supplemented by more than 130 field testing stations

Singapore

Saskatoon

Davis

Lubbock

RTP / Morrisville

Hyderabad

Sao Paulo

Gent Haelen

Monheim Frankfurt Lyon

Sophia

R&D Bayer CropSciences: R&D network

encompasses ~4400 employees in 13 major hubs

• R&D investment of ca €880 million in 2013; ~4400 R&D employees

• Full year sales: € 8,819m

• Total BCS: 22,400 employees; >120 countries

External collaborations: e.g. Targenomix (spin-off of Max Planck Institute) to discover new MoA and

active ingredients to increase agricultural productivity

Leveraging synergies by developing combined

life science technology platforms for Health

Care and Crop Sciences

Ion channel

platform

Computational Life Sciences to manage, analyze, combine and re-use data

to create scientific breakthroughs and business value

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Bayer CropSciences: an integrated approach

across Small Molecules, Seeds and Biologics to

develop integrated crop solutions

Breeding & traits: adjusting the

plant characteristics to improve

quality & yield

Crop Protection: Improving plant

health by protecting the crops

against external stress factors

Breeding

& Traits

Chemistry Biologics

Yield

Quality

Customer Needs

Integrated crop solutions:

Leverage the synergies across

all R&D areas

Page 7 • BCS Info Days • June 2014 Computational life sciences

Page 8 • Scorecard • 2014

A particular CropScience challenge:

Huge diversity of species

Mammals (human safety) Fish / …

(tox models)

Center graphic generated at http://itol.embl.de/, Letunic and Bork, Bioinformatics 23(1) 2006

Photos from http://www.cropscience.bayer.com/en/Crop-Compendium.aspx

Oomycetes (plant pathogenes)

Insects (pests; beneficials)

Ascomycetes (plant pathogenes)

Basidiomycetes (plant pathogenes)

Nematodes (plant pests)

Plants (weeds; crops; seeds, traits)

Bacteria (traits; biologics; diseases)

Enormous diversity of

relevant species

Only for a small fraction,

(proper) genome

information is available

Huge opportunities for

Computational Life

Scientists

http://itol.embl.de/



http://www.cropscience.bayer.com/en/Crop-Compendium.aspx



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Source: Illustration from CropLife America, adapted by Bayer CropScience

… this would be the land

available for agriculture (1.5 billion hectares / ~ 3%)

High need for innovation in CropSciences to

deliver safer & more effective next generation

products

• BCS Info Days • June 2014 Page 9

If this was the Earth… (Surface of 50.9 billion hectares)


A global challenge to meet food supply

*Source: United Nations Available farmland per person is expected to decrease dramatically

At the other hand … need to increase food production by 70% to meet

demand by 2050

World population*

Arable land

per person

1950 2000 2050

0.52 ha 0.26 ha 0.19 ha

2,529,346,000 6,115,367,000 9,600,000,000

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We need constant innovation to deliver safer

& more effective next generation products

Growing pressure on resources

• Crops needed for food,

feed, fiber and renewable

raw materials

• Water scarcity

The complexity of sustainable agriculture and the interdependencies

of various factors increase the need for innovation

Climate Change

• Understand the crops

under challenging

environment

Understand practical

agriculture

• Across the world

• Across the seasons

Growing world population • Increasing food &

energy demand

• Decreasing farmland

per capita

• Increasing market

volatility

Increasing Regulatory

demand

• Increasing standards

• Lack of harmonization of

regulatory requirements

Page 11 • BCS Info Days • June 2014

The Success Story...

The success of the Roundup®

Ready system and the nearly

exclusive reliance on glyphosate

for weed control…

… this caused massive development

of weed shifts and resistance...

dose rate

But Nature Strikes Back

x 2 x 16 x 4 x 8

1996 2008

Page 12

Today

Limited Number of Modes of Action leads to

an increased risk of resistance worldwide

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CLS will help exploring & connecting massive datasets

to bring unprecedented innovation power

Overview of key CLS technologies

Genotype Phenotype

Environment

Data Sciences

Question-

based

R&D

Enable a data-intensive, multidisciplinary

research process: technology & culture

Cross-type data

integration (structured

& unstructured data)

Data-driven &

multi-models

simulations

High Performance

Computing: connected

computers & fast

networks

Visualization & analytics

(e.g. experimental design,

text mining, pattern

recognition, algorithms)

Key CLS technologies


Technology trends in agriculture; digital farming

producing enormeous amounts of data

• Computational Life Sciences at Bayer • Project Kickoff • June 27, 2014 Page 14 Source AT Kearny

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Page 15

Chlorophyll stress can be measured to identify

diseases

Source: Mriya Agroholding, Ternopil Oblast, field 109-A, 971,2 ha, Winter Wheat, Odessa 1432 variety sown 30.09.2012

Page 16 16

Yield from combine

Stratego YLD As-

Applied from

Sprayer

NDVI Imagery

(0.5M resolution)

NIR Imagery

(0.5M resolution)

Elevation

from NED

• Source N. Hummel, BCS US

Powerful visualisation and innovative

data sciences solutions needed

to create value

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Chemistry

Seed Bank

Environment Data

Seed Bank

Germplasm

Breeding Pops & Materials

Pedigree

Phenomics

Yield & Components

Quality Traits

Plant Health Traits

Omics

Genomics

Transcriptomics

Proteomics

Metabolomics

Epigenomics

Environment

Weather

Soil Type

Crop Modeling

Phenomics Data

Omics Data

Predictive

Analytics

“Big Data” Layer 1. Data integration

Capture heterogeneous datasets

Data integrity, consistency, accessibility

2. Data & tools connection Connect and mine data for

better leads in trait and breeding

targets for small molecules & biologics,

Improved compound (de)selection

3. Cross-fertilization Cross-functional best-in-class algorithms

High performance computing environment

Computational Life Science in BCS – extract relevant information from big data

Chemistry

HT Screening

Chem. Processes

Struct. Biology

CLS example from Bayer CropSciences: bridging (structural) biology and (computational) chemistry

Example: Prediction and design of traits tolerant to

HPPD Inhibitor herbicides (pigment synthesis inhibitors)

“Research Techn.”

HPPD-X-ray structures with

different active HPPD

inhibitors

• Able to select 11 out of 1000+

HPPD sequences as candidates

tolerant to the herbicide

• Significantly reducing variants &

sequences to be tested

• Predicted sequences showed the

desired tolerance to the

herbicides in planta.

Structural bioinformatics

to identify residues

needed for binding and

protein conformation.

Gudrun Lange, Robert Klein, Michael Beck et al. CLS BCS R&D

HPPD = Hydroxyphenyl pyruvate dioxygenase

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CLS to identify broad spectrum fungicide

Biologics much quicker with less resources

Source: Dan Joo, CLS BCS Biologics R&D

• To Identify novel fungicide Biologics leads, ca. 400

Paenibacillus strains to be profiled

• Traditional approach require ~ 26 weeks based on a

full characterization for each strain

• CLS genome sequencing and annotation pipeline

significantly reduced time and workload

Approach

• Hundreds of strains could be narrowed down to 3

meaningful lead strains with desired properties

• Characterized with only a fraction of time and

resources

Outcome & Impact

Optimal balance between wet and

dry life sciences

CLS

Ontology

Platform

Ontologies and Controlled vocabularies

in CLS

Centralized management of the terminologies

used across functions within Bayer CropScience

Comparative

Genomics

Genome

Annotation Functional

Annotation

Germplasm

Contracts

Safety

Risk

Plant Quality

Assurance Traceability

Vectoring

Platform

Breeding

Platform

Acronym Ontology

TO Trait Ontology

GO Gene Ontology

LO Locations Ontology

SO Sequence Ontology

TAX Taxonomy of Species

ECO Evidence Code Ontology

EDAM EMBRACE Data & Methods Ontology

Acronym Controlled Vocabulary

XC Experimental Conditions Vocabulary

MU Measure & Units Vocabulary

TECH Technologies Vocabulary

PROV Providers Vocabulary

Vegetables

Small Molecules

(Molecular) Breeding

Trait Research

Biologics

Support Functions (Legal, IP, Stewardship)

SO GO

SO GO

SO GO

LO TAX

LO TAX

LO

Source: Yann-Francois Bizouerne & Erick Antezana, BCS R&D

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Modern graph database for efficient

storage of biological information

Multiple genome-scale approaches require effective ways to describe, connect and

organize our biological data

In-house graph database stores > 338M relevant Triples: “subject - predicate -

object” for mining and answering questions like:

• Which T. aestivum genes are orthologous to the gene

linked to my A. thaliana affymetrix identifier?

• Which genes are specifically expressed in the root?

• ... and are transcription factors also

responsive to drought?

• And so on…

Page 21 • CLS • September 2014

HPC

BioGrid Source “Ontologies and Semantic Web technologies

for knowledge discovery in Big Data” by E.

Antezana, CLS Ghent

Gene expression

profiling

Gene Passport / Wiki

Functional annotation,

protein domains, sub-

cellular localization, etc.

Comparative Genomics

Genetic maps

RNA-seq

Microarrays

Clustering

Co-expression

Genome Annotations

Intuitive & powerful Data Analysis

interfaces to boost decision making

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Pod Shattering is a Problem in

Canola

Pre-harvest release of seeds from mature pods towards the end of the season


Canola pods have “pre-set breaking

points”

Natural & essential process, but

one that can drive canola farmers to

despair

When the pod is ripe it splits

open & then releasing the seeds

inside

E.g. by strong winds pods break

open too early….seeds fall to the

ground and no longer usable

Mechanical “Swathing” to prevent yield

loss by seed pods open prematurely

swathed field maturation - drying

harvesting

swathing


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Resistance to pod shattering is an important

target trait

R

V S

DZ R

V

Transversal section

DZ : Dehiscence zone

R : Replum; S : Septum; V : Valve

From Dinneny et al. BioEssays 27:42–49

IPage 25 • CLS • September 2014

• Pods are composed of two halves (valves) that are held strongly together by a specialized

tissue

• When the pod is ripe the tissue disintegrates; splits open & releasing the black seeds inside

• Discovered that Transcription factor gene Ind (Indeshiscence) has a key role in tissue

disintegration

• Approach: make ind gene defective to reduce pre-release of the seed (by preventing the

formation of the lignified enb layer; dehiscence zone)

2 IND homeologs

genome

B.napus genome sequence

Bioinformatics and omics revealed 2

ind Homeologs

chromosome

location

A3 C3

Platform

Genome Annotation


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Both, A3 and C3 ind gene relevant to modify pod shattering

chromosome

location

A3 C3

2 Functional - Highly Expressed ind

homeologs in Pod Valves

B.napus transcript atlas

RNA sequencing of: - 10 B. napus tissues - 12 developmental stages


KO

ind1 ind2

WT

Genotype Shattering Relative yield

Optimizing the Ind1-Ind2 genotype is critical

to achieve the desired effect

Pod

phenotype

High

Limited

None

100%

100 - 117%

Not harvestable

Hyb

rid s

eed p

roduction

25% functional IND

A hybrid with this ind genotype cannot be produced as it would

require a 4 x KO parent which is not harvestable


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A Single Amino Acid Substitution in the basic

Helix-Loop-Helix structure of IND provided the solution

ind1 ind2

Hybrid genotype

b b

b b

b b

IND is a transcription factor binding to DNA with a Helix-Loop-Helix motif

However, IND can only bind to the target DNA as a dimer

Parental genotypes

25% functional IND


A Single Mutant Allele Solves a Big Issue in Canola

Pod Shatter Reduction Launched in Canada in 2014


Opportunity to

harvest canola fields

at just the right time

without having to

swath them first

This gives the plants

enough time

to ripen & form

better-quality seeds

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Some key CLS challenges for data-

driven and integrative R&D

Page 31

• Simultaneously manage security and promoting sharing of data across

functions (key words: technology hopping, data-sharing culture, manage business risks)

• Digital scientists to bridge the gap between Data and R&D workflows

• Data stewardship & interoperability; make data available and usable

• New ways to automate data integration e.g. to manage time sensitive

data…to manage structured and unstructured data

• User-friendly interfaces for experts and non-experts

• Connect fast developing data science solutions with stable IT enterprise

architectures: balancing robustness and agility

• the FAIR principe: Findable – Accessible– Interoperable – Reusable

Acknowledgements

Erick Antezana

Bart Lambert

Laurent Viau

Yann Bizouerne

Steven Robbens

Jack van Handenhove

Stephane Bourot

Benjamin Laga

Joan Wong

Aurélie Defferrard

Xi Wang

Marc Cornelissen

Jens Hollunder

Eddy Beck

Rene van Schaik

Dan Joo

Gudrun Lange

Sabien Vulsteke

Henning Redestig

Gitta Erdmann

And many others

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Forward-Looking Statements

This presentation may contain forward-looking statements based on current

assumptions and forecasts made by Bayer Group or subgroup management.

Various known and unknown risks, uncertainties and other factors could lead to

material differences between the actual future results, financial situation,

development or performance of the company and the estimates given here.

These factors include those discussed in Bayer’s public reports which are

available on the Bayer website at www.bayer.com.

The company assumes no liability whatsoever to update these forward-looking

statements or to conform them to future events or developments.


Thank you!


agenda - elixir · elixir innovation & sme event march 19, wageningen jakob de vlieg, vp - sr....

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