dawn of a golden age for microbial engineering platt.pdf · dawn of a golden age for microbial...

Copyright 2015, Amyris Inc. All rights reserved

Dawn of a Golden Age for microbial engineering

Track 3, Session 1, Monday 20th July

Darren Platt,

Vice President Data Science


ETHANOL

Amyris engineers living factories that produce chemicals from

sustainable sources

SUGARCANE

phosphoenol-pyruvate

pyruvate

“Baker’s Yeast”Saccharomyces cerevisiae

IsoprenoidSynthesis

acetaldehyde

Acetyl CoA

ISOPRENE

GPP

FPP

FRAGRANCE OILS

ARTEMISINIC ACID

monoterpenes

sesquiterpenes

IPP/DMAPP

FARNESENE

CAROTENOIDS


Artemisinin – component of WHO recommended treatment for malaria

• Shorter, reliable production cycle

• Stable long term price

• Currently supplying 30% of world need via Sanofi

Artemisinin

http://www.who.int/malaria/areas/treatment/overview/en/


Farnesene – versatile C15 building block

Dimer + H2 (squalane)Myralene ™ (partial H2)

Farnesane (full H2)

Polymers / Co polymers

http://biossance.com

http://muckdaddy.com


Diesel MetricAmyrisDiesel

HEFA ASTM-D975 Diesel

Energy Density MJ/kg 44.25 40 to 43 n/a

Cetane Number No. 56 48 40 min.

Thermal StabilityPres. Drop (mmHg)Tube Deposit (No.)

0 (@360°C)0

n/a25 max. (@275°C)

3 max.

Flash Point °C 108 38 52

Density kg/m3 765-780 730-770 n/a

Freeze Point °C <-100 -40 n/a

Sulfur PPM 0 n/a 15 max.

GHG Reduction1 % 70 50 n/a

Amyris diesel delivers top tier fuel performance

1 RGHG reduction by EPA RFS methodology and at 24% fermentation yield of cane sugar


Industry Challenges

Industry Overview Amyris Solution• Wide range of emollients – Amyris is

focused on addressing needs at the high to mid range (+$900m mkt)

• Amyris squalane is performance and cost advantaged vs. olive/shark today

Source: Nikko Chemicals

Yen/kg

• Chemically convert farnesene into squalane and hemisqualane

Fermentation

Farnesene Squalane

Downstream Processing Hemisqualane

Sugar• Consumers shifting towards

naturals• Price and supply volatility – by-

products of petrochem or agro sources

• Quality inconsistency

Source: SRI, Squalane data provided by Amyris customers (2kta @ $30/kg today)

JapanSharkOliveAmyris

• Volatile price and quality exemplified by squalane

Impact• Currently reaching >100m consumers• Current usage in ~400 brands

$45

$11

$7

$4

$15

• Squalane $30/kg, 2-4 kta• Lanolin $14.24/kg, 8 kta• Silicones $11.3/kg, 33.5 kta• Natural Wax $8.38/kg, 6 kta• Esters $7.26/kg, 33.5 kta

• Alkoxylates• Glycerides• Fatty Alcohols

• Fatty Acids• Other

Am

ino

Aci

ds

and

Po

ly P

epti

des

List

Pri

ce (

$/kg

)

Shark (High)

Shark (Low)

Olive (High)

Olive (Low)

Greening the emollient industry

A new era for metabolic engineering


Lessons from millions of strains inform faster progressEf

fici

ency

(yi

eld

)

FarneseneFirst Generation

FarneseneSecond Generation

Fragrance 1

Fragrance 3

Fragrance 2Ph I

Months

Each product benefits from knowledge gained in previous cycles


Design Build Test Learn

Data

center

Data

center

QC DNA

Transform

strain

Design

DNA

Assemble

DNA

QC

strain

Grow

strain

Measure

strain

Analyze

data

We engineer yeast strains that make the products you need to compete and meet the needs of consumers.

We have state of the art:

• Computational DNA design tools

• DNA construction AND QC tools

• Microscale assays that are highly predictive of fermentor performance

• Lab scale fermentor processes that have near perfect prediction of manufacturing scale

• World’s largest Yeast Engineering Data Repository

Our framework for driving faster product development


Amyris has industrialized the science of engineering living factories

350 strains per person20 strains per person

2015: state of the art = 24-48 parts at a time at 6 genomic locations, for 1000 different strains every 3 weeks at ~ $0.02 / basepair

Test platformAutomation

Automation covers most of the DBTL cycle


LIMS systems and CAD Platform

Software and Algorithms supporting every stage of DBTL


3 CO2

GLUCOSE

Fatty acids

Ergosterol

Ubiquinone

Dolichol

Membrane

localization

Amino acids

Fatty acids

Cell wall

Histone acetylation

Growth regulation

Amino acids

NAD+

Ubiquinone

Glycolysis

PPP

Solved fundamental challenges in metabolic engineering

FARNESENE

Second generation yeast metabolism for acetyl coA production


Time (h)

No switch

Withswitch

When grown in the OFF state, growth rate (µ) is improved dramatically

Ce

ll G

row

th

v

Solved fundamental synthetic biology challenges

Robust, Industrial Scale Switch

200h


Open strain engineering platform: partnership with Genome Compiler

Drive quality through serving a larger community

Breaking the machineThe next two orders of magnitude


What are the resource drivers for microbial engineering?


Challenges of optimizing 100 molecules concurrently

• Sheer scale of molecule pipeline forces automation– designing strains by hand could easily consume every strain engineer we have

– analyzing those data could easily consume every computing person we have

– (and we still wouldn’t be done)

• Computers are better at crunching complex data– phenotype data are multivariate, multi-dimensional

– new data of gigabyte volumes flowing in constantly

– decisions must be made quickly to inform next cycle of strain development or testing

• Computers excel at combinatorial design & learning without bias– better random number generators

– it’s boring to do by hand => error-prone

– avail of extensive literature on machine learning

– intentionally generate poor designs, fully crossed/nested DOEs for uncovering genotype-phenotype relationships


execution

prefrontal

cortex

memoryperception,

learning

KNOWLEDGE

STORE

facts ~ inferences

targets ~ routes

methods ~ models

genotypes ~ lineages

priorities

hitpick,

retest

prob.

assertions

INITIAL

PATHWAY

DESIGN

“know-

ledge”

pass/fail

data

PROCESS

ANALYZER

probabilistic decision making

design of experiments

control engineering

bandwidth allocation

tactics ~ strategy

DESIGNER

BUILD

ENZYME

IMPRVMT

MUTA-

GENESIS

pipeline selection

DECON-

VOLUTION

RATIONAL

ENGG

DNA

design

routes to

molecule

raw

data

PHENOTYPE

ANALYZER

LIMS

strains

TEST

high

level

designGENOTYPE

GENERATOR

Building an Automated Scientist for closed-loop strain engineering


Where will the next 100-500 compounds come from ?

Image courtesy of James Kennedy (http://tinyurl.com/qhjmen8)

60% of known natural products are Terpenoid


Terpenes have the richest chemical repertoire on the planet

Phytochemisty 70 (2009) 1621-1637


Homologous

recombination

+ PPS

TS

P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

PPS TS P450

b-farnesene

patchoulol

amorphadiene artemisinic acid

santalene

limonene g-terpinene terpinolene

3-carene b-pinene a-pinene

myrcene C10 C15

C30

geraniol

valencene

(+)-d-cadinene 5-Epi-aristolochene

vetispiradiene

Epi-cedrol

taxadien-5a-ol taxadiene

casbene

C20

lanosterol

> 3

0,0

00

ter

pen

oid

sin

nat

ure

µPharmsm

: Applying our platform to drug discovery

Amyris’ multi-loci integration technology allows rapid combinatorial library construction


glucose

TCACycle

Phenylpropenes Stilbenes

Flavonoids

Isoflavonoids

Trp Tyr Phe

Shikimate pathway

Flavonoid pathway

Arg

His

Gln

ProGluGlutamate pathway

Ergosterol

Isoprenoid pathway

C5's

C10's

C15's

Metabolic node

IPP

GPP

FPP

Replicate process for other key metabolic nodes


From concept to industrial production

Thanks!

Backup slides


Architecture for Genome Compiler joint development

customer

Genome Compiler Portal

Order + Design input

AmyrisBioFab

Design specification,Cost feedback

DNA Delivery

T_CYC1 3’ Gene2

P_GAL1

5’ Gene1 Gene2 URA3


BUILD STRAINS

A predictable path from lab to Industrial Manufacturing

TEST STRAINS & PROCESSES

96-wellScreening

Design and Learn: Automation, Computing

Mutagenesis,Meiosis &

AutomatedStrain

Engineering

0.5L & 2LFermentation

300 LFermentation

&Recovery Ops

120,000 strains/mo 60 strains/mo 2 strains/mo 2-4 strains/yr

MANUFACTURE

Millions strains/mo

200,000 LFermentation

&Recovery Ops


Warehouse to integrate data sources

LIMS system

Data drivendecision making

LIMS system LIMS system Large data streams

Primary warehouse

Larg

e sc

ale

par

alle

l co

mp

uti

ng

Process QCETL

Secondary warehouse

Mining, design tools

Quality tagging

Quality tagging

Quality tagging

FermentationHTSGenotypes

Quality Tagging is

critical: we want to

distinguish operational

outliers from scientific

ones.

Tremendousamount of analytic work to summarizea tremendousamount of experimentalwork


Quality: high resolution / throughput DNA QC

SNP

indel


Multivariate regression predicts tank performance better than any

single assay

Tan

kp

erfo

rman

ce

Plate performance (various assays)

0.92


Genome browsing / alternative host support

Courtesy of Amyris, Inc.

H. polymorpha K. marxianus K. lactis A. adeninivorans

© Kable © Scipio © Getty Images

E. coli

© LBL

Y. lipolytica

© Bioline Int.

CAT-1; PE-2(Industrial Yeast)

© GRC© Eye Of Science


The rabit hutch: 120 million base pairs . . .

-

20

40

60

80

100

120

140

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Jan

-10

Ap

r-1

0

Jul-

10

Oct

-10

Jan

-11

Ap

r-1

1

Jul-

11

Oct

-11

Jan

-12

Ap

r-1

2

Jul-

12

Oct

-12

Jan

-13

Ap

r-1

3

Jul-

13

Oct

-13

Bas

e p

airs

(m

illio

ns)

Ban

k si

ze (

bas

e p

airs

)

Date

Bank size(parts)

Human Genomecoding sequences

Yeast Genome


Tracking material flow through complex laboratory processes

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