A computer tool based on synthetic biology

approach to design microorganisms and

bio-based processes

São Paulo, October 26, 2010.

Prof. Dr. Rubens Maciel Filho

State of University of Campinas- UnicampSchool of Chemical Engineering

Laboratory of Optimization, Design and Advanced Process Control

An Integrated Process for Total Bioethanol Production and Zero CO2 Emission

Real Challenge

Integration of scientific basis with engineering concepts through sophisticated

mathematical and computer tools and experimental data collecting

for all the needed steps

From microorganism model to large scale plant operation, with proposition

of integrated alternative routes and alternative processes for fermentation

and bioethanol separation high efficient plants

COMPUTATIONAL TOOLS

Advances in computational tools and numerical analysis provide

opportunities to develop larger and more realistic models with a wider range

of modeling capabilities.

A guide line to take decisions when multivariability and complex variable

interaction there exist, as the case of metabolic pathways in microorganisms.

Use of Computer Aided and Process Engineering (CAPE) procedures to

have a systematic approach to define the main phenomena taking place in

the microorganism when the whole process is considered

Rational solution among possible options (if that is the case) for the

definition of operational conditions

Achieve a working, in the engineering basis,

microorganism in terms of performance and stability

COMPUTATIONAL TOOLS

•Mathematical models serve as a process knowledge management

• allows to investigate interactions that can be difficult if not

impossible, to view and/or provide only with the experiments,

especially when many factors vary simultaneously.

•Modeling of fermentation processes: mathematical equations -

mass/energy balances for each component in the bioreactor,

coupled with complex biochemical transformations taking into

account the rate with which these transformations take place.

COMPUTATIONAL TOOLS

To simulate a process is necessary:

•A mathematical model that can reproduce, as closely as

possible, the process behavior under various conditions

•To identify and to represent the main phenomena that occur in

the process.

•Reasonable process data

•To have an understanding of the interplay between the

numerous and interconnected reactions responsible for making

it possible for cells to survive and reproduce i.e. the physiology

of the microorganism.

ANHYDRIDE OF THE BIOETHANOL

PRODUCTION PROCESS FROM SUGARCANE

Fermentation

Mass balance equations and reaction rate of the model

eglufeed

egluSSDXRR

t

Scos71

cos

adeacetaldehy

edeacetaldehyiglu

eglu

ea

heglu

eglu

ha

leglu

eglu

l XsKsKs

skX

Ks

skX

Ks

skR

11

cos

1

1cos

cos

1

1cos

cos

111

a

eglu

egluX

Ks

skR

7cos

cos

77

pyruvate

pyruvateSDXRRR

t

S321978.0

a

egluipyruvate

pyruvateX

sKKs

skR

1

1

cos22

22

a

pyruvate

pyruvateX

Ks

skR

3

4

4

33

REQUIREMENTS FOR REAL WORLD APPLICATIONS

xy velocity

field

Concerns to be considered:

Energy spend to stirr

Microorganis maintenance

Well distributed flow (perfect

mixed system)

Conversion achieved and

Concentration, rate of reaction

How much will cost to

separate - To be price

competitive 0,05 US$ /Kg

Costs to sterilize the leaving

streams

Existing GMO based

processes are not competitive

Better design of the GMO

microorganisms are required

Real world – Fermentors 1.500.000 liters (12) 43 thousands ton sugar cane/day

Without a

rational

procedure

Decisions may

be difficult, time

consuming and

too late when

alternative but not

necessarily better

solution is

implemented

is the focus

It is possible to

propose and

evaluate

systematic

procedures to

attack the problem

General objective

of this work

Near Brussels

•Problem identification (where is the bottle neck/restriction)

•Rational Solution among possible options (if that is the case)

•Data collection for computer tools use

•Appropriated Computer Aided Design Tool

•Computer Aided Design of the microorganism taking into account

the physiology to built-up the model (virtual microorganism)

•Evaluation of the model / functional model

•Development of the guidelines either for genetic modified

microorganisms or to stimulate genetic changes naturally

•Optimize the process to achieve high operational performance –

high conversion, higher concentration and large throughput

Proposed Pathways

Brazilian Production Season 2008/2009

569.062.629 ton of sugarcane

27.512.962 thousand liters of ethanol (larger world

exporter)

9.336.343 thousand liters of anhydrous ethanol

18.176.619 thousand liters of hydrous ethanol

31.049.206 ton of sugar (larger world producer)

SUGARCANE – As Raw Material

17

Sugarcane Expansion Potential

2.66Sugarcane for ethanol

0.15Each million kl of ethanol needs

Sugarcane uses less than 0.32% of arable land

90.0Available area for crop

expansion

5.34Sugarcane

60.40all crops

Cultivated land:

320Total arable land

850Brazil

Million ha

18

Sugar

Glycose

Sacarose

FE

RM

EN

TA

TIO

N

Ethanol

Acetic acid

Lactic Acid

butanol Acetone ethanol

CH

EM

ICA

L S

YN

TH

ES

IS

Acetaldhyde

Anhydride acetic

Ethyl Acetate

Vinyl Acetate

Crotonaldhyde

Paraldhyde

Butanol

Buthyl Acetate

Piridine

Nicotinamide

Glycol

Butadiene

Glyoxalate

Succinic acid

Biorefinery Concept: Some Chemical Products via fermentation

Learning curve already competitive

fermentação

19

Etilene

Ethanol

Acetaldehyde

Acetic acid

Propene

Propylene

___Acrylic Acid

Glycerol

Lactic acid

Butadiene

Butanodiol

Succinic acid

BIOMASSH

YD

RO

LY

SIS

Sugar

Glycose

Sacarose

Xylose

Arabynose

FE

RM

EN

TA

TIO

N

Other Products to be obtained from biomass

Learning curve costs have to be reduced

20

Alchoholchemistry Products

Ethanol

Propylene

Acetaldehyde

Ethylene

Acetic Acid

Ethylene-Dichloride

Styrene

Vinyl Acetate

Ethylenediamine

Acetic Anhydride

MonochloroaceticAcid

Ethyl + Other Acetates

2-Ethylhexanol

N-Butanol

Ethylene Oxide/Glycol

Polyethylene

Butadiene

Polyvinil Acetate

Polyvinyl Chloride

Polystyrene

Crotonaldehyde N-Butyral-Dehyde

Ketene

Vinyl Chloride

COMPUTATIONAL TOOLS-

Possible approach

The fermentation models can be divided in four groups depending on

the level of detail in the model: unstructured model; segregated and

non segregated population; structured model.

Unstructured Structured

No

n-S

egre

gat

ed

Cell population is assumed to

be completely homogeneous

Cell multicomponent

description

Seg

reg

ated

Single component

Heterogeneous single cells

Multicomponent

Description of the heterogeneity

of cell by cell

Balanced growth

Cell average

approximation

Balanced growth

Cell average

approximation

METABOLISM OF SACCHAROMYCES

CEREVISIAE

Metabolism biochemical assimilation (anabolic pathways)

and dissimilation (catabolic pathways) of nutrients by a cell.

Anabolism supply and support all the essential components

and compartments, enabling the survival and growth of the

microorganism in the surrounding environment.

Catabolism provide metabolic energy and precursors for the

anabolism.

Sugar catabolism in yeast glycolysis pathway.

It is used to describe the main pathways dissimilation glucose 6-

phosphate Embden-Meyerhoff-Parnas (EMP) and Pentose

Phosphate (PP) pathways.

EMP is considered the primary route for dissimilation of glucose to

pyruvate during aerobic growth on a glucose-limited substrate.

PP pathway has been recognized to serve both an oxidative as well

as an anaplerotic function.

GLYCOLYSIS

HNADHATPpyruvateNADPADPeglu i 2 2 2 2 2 2 2 cos

HNADPHCONADPphosphateeglu 21 12 6 21 6 cos 2

HNADPHCO-phosphateriboseNADPphosphateeglu 2 2 5 2 6 cos 2

METABOLISM OF SACCHAROMYCES

CEREVISIAE

Membranes – Which are the limitations ?

Glucose

glucose-6-phosphate

fructose-6-

phosphate

fructose-1,6-diphosphate

1,3-

diphosphoglycerate

2 pyruvate

ADP

dihydroxyacetone-

phosphate

3-phosphoglycerate

2-phosphoenolpyruvate

ATP

ADPATP

2-phosphoglycerate

2ATP2ADP

2ATP2ADP

2NAD+

2NADH+2H+

acetyl-phosphate

Acetyl-CoA

Acetaldehyde

NAD+

NAD+

Glucose

glucose-6-phosphate

6-phosphogluconate

ribulose-5-phosphate

ADPATP

ADPATP

pentoses

xylulose-5-phosphate

glyceraldehyde-3-

phosphate

TCA cycleLactic acid

NADH

NADH

Ethanol

Acetic acid

CO2

(EMP)

(PP)

Ethanol fermentation

Glycolitic route and Krebs Cycle

Aerobic oxidation

ENERGY BALANCE OF SACCHAROMYCES CEREVISIAE

ATPOHCOOHHCADPPOHC i 2 2 2 2 22 22526126

3ATP O4H 2 O2

1 3 3 22 NADPADPHNADH i

2 FADHEFumarateFADESuccinate

Phosphorilation

Pyruvate

OHADPPPyruvate i 222 17 15ATP CO3 15 15 .5O2

OHATPC 2i6126 2 2 2H 2NADH 2Pyruvate 2NAD 2ADP P2 OH

OHATPCOADPPOPyruvate i 222 34 30 6 30 30 5 2

OHATPCOADPPOC i 2226126 42 36 2 36 36 6 OH

NUTRIENTES FOR GROWTH OF YEAST

Substrate Intermediates Enzymes Products

Sucrose Invertase Glucose + Fructose

Maltose Maltase Glucose

Melibiose Melibiase Glucose + Galactose

Glucose Glucose products

Ethanol

Acetaldehyde →

Acetyl-CoA →

Oxaloacetate

Alcohol

dehydrogenase

Glucose by

gluconeogenesis

LactatePyruvate

Lactate

dehydrogenase

Glucose by

gluconeogenesis

Glycerol

Glycerol-3-phosphate

→

Dihydroxyaceton-

phosphate

Glucose by

gluconeogenesis

In addition to hexose sugars, yeasts can utilize a number of

non-conventional carbon sources, such as biopolymers,

pentoses, alcohols, hydrocarbons, fatty acids and organic acids.

Pentose sugars can be fermented to ethanol by very few

species, although many yeasts can grow aerobically on pentoses.

Saccharomyces cerevisiae can fermenter xylose by introducing

genes for xylose reductase and xylitol dehydrogenase from xylose

-fermenting species by recombinant DNA technology.

NUTRIENTES FOR GROWTH OF YEAST

IMPACT ON THE MODEL DEVELOPMENT

Bacteria Fungi

D-xylose D-xylose

xylitol

D-xylulose

xylose isomerase

D-xylulose-5-P

NAD(P)H

NAD(P)+

Pentose Phosphate Pathway

xyl. reductase

xyl.

dehydrogenase

xylulokinase

NAD+

NADH

ATP

ADP

Saccharomyces cerevisiae engineered to

metabolize xylose

Ho et al. cloned genes to xylose

reductase (XR), xylose dehydrogenase

(XDH) of Pistichia stipitis, and

introduced in S. cerevisiae

XK TKL

TAL

Xylose Xylitol

Xylitol

XK

XR

XDH

MATHEMATICAL MODELING TO

ETHANOL PRODUCTION

Structured model with free cells

Continuous stirred tank bioreactor (CSTR)

Anaerobic fermentative process

Ethanol, acetaldehyde, pyruvate and biomass production

METABOLISM OF SACCHAROMYCES

CEREVISIAE

STOICHIOMETRIC REACTIONS

NADHpyruvatee glu 33.0 cos

NADHpyruvate .671 CO 2

20.33CO dehyde0.67acetal pyruvate

NADH.acetatedeacetaldehy 50

NADHCOacetate 2 2

ethanolNADH.yde acetaldeh 50

NADHCOeglu 0.119 087.0 .913X0 cos 2a

ADHCOacetate 0.401N 222.0 .778X0 2a

Acdha X X

ndegradatio aX ndegradatio AcdhX

Lei et al., 2001.

a

aeglu

eglu

aa

eglu

egluX

KS

SkX

KS

Skr

1cos

cos

1

1cos

cos

11

a

egluipyruvate

pyruvateX

SKKS

Skr

cos22

221

1

Acdha

deacetaldehy

deacetaldehyXX

KS

Skr

4

44

a

egluiaacetate

acetateaa

acetate

acetate XSKKS

SkX

KS

Skr

cos55

5

5

551

1

a

ethanoledeacetaldehy

ethanoledeacetaldehyX

SKKS

SkSkr

66

6

66

a

egluiaacetate

acetate XSKKS

Skr

cos55

881

1

a

eglu

egluX

KS

Skr

7cos

cos

77

KINETIC RATES

DSSxrrdt

dSegluineglu

eglu

cos cos71

cos

DSxrrrdt

dSpyruvate

pyruvate

321978.0

DSxrdt

dSethanol

ethanol6045.1

DSxrrrdt

dSacetate

acetate854363.1

aa Xrrrrrr

dt

dX8710987 619.0732.0619.0732.0

DSxrrrdt

dSdeacetaldehy

deacetaldehy

6435.0

MASS BALANCE OF THE MODEL

X biomass; Xa active cell material;

XAcdh Acetaldehyde dehydrogenase; D dilution rate;

Ki rate constant; Ki affinity constant;

Kji inhibition constant

• Mass balance equations 8

• Kinetic parameter 37

• Parameter adjust Genetic Algorithm

MODEL SOLUTION

CSTR simulations

TRS Total Reductor Sugars

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

0

2

4

6

8

10

12

14G

luco

se c

on

cen

trati

on

(g

/L)

Time (h)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

0

5

10

15

20

Time (h)

Py

ruv

ate

con

cen

trat

ion

(g

/L)

0

1

2

3

4

5

6 Acetald

ehy

de co

ncen

tration

(g/L

)

CSTR

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

0

10

20

30

40

50

60

Time (h)

Eth

an

ol

co

ncen

trati

on

(g

/L)

0

5

10

15

20

25

Bio

mass c

on

cen

tratio

n (g

/L)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

0,20

0,25

0,30

0,35

0,40

0,45

0,50

Xa

Time (h)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

0,010

0,015

0,020

0,025

0,030

XA

cd

h

Time (h)

Time (h)

Pyru

vate

concentr

ati

on (

g/L

)

Time (h)

Glu

cose

con

cen

trat

ion

(g/L

)BATCH

Time (h)

Ace

tate

con

cen

trat

ion

(g/L

)

Time (h)

Eth

anolc

on

cen

trat

ion

(g/L

)

Bio

mas

sco

ncen

trat

ion

(g/L

)

Time (h)

Biomass

Concentration g/L

Effect of temperature

Kinetic parameters optimized by

Quasi-Newton algorithm at 30, 31.2,

34, 36.8 and 38 C.

Experimental and Model Predictions for different temperatures

Experimental and simulated data

(QN ―) from 30 C to 38 C.

Experimental data are for

concentration of substrate , S( );

cell mass X( ) and ethanol P( ).

MATHEMATICAL MODELING TO

ETHANOL PRODUCTION

Structured model with immobilized cells

Plug flow bioreactor (PFR)

Anaerobic fermentative process

Ethanol, acetaldehyde, pyruvate and biomass production

MASS BALANCE TO FLUID PHASE

Glucose

Pyruvate

Acetaldehyde

Ethanol

Xerrz

Su

z

SD

dt

SethanoleSKpyruvatepyruvate

az

pyruvate

312

2

978.01

Xerrrz

Su

z

SD

dt

SethanoleSKdeacetaldehydeacetaldehy

az

deacetaldehy

6432

2

5.01

Xerz

Su

z

SD

dt

SethanoleSKethanolethanol

azethanol

62

2

045.11

Xerrz

Su

z

SD

dt

SethanoleSKeglueglu

az

eglu

71

cos

2

cos

2

cos 1

Xerrr

Sr

rrR

D

t

SethanoleSKASeglu

71

2

22

cos 1

Xerrrr

Pr

rrR

D

t

SethanoleSKAPpyruvate

321

2

22978.0

1

Xerrrr

Lr

rrR

D

t

SethanoleSKALdeacetaldehy

643

2

225.0

1

Xerr

AAr

rrR

D

t

SethanoleSKAAAethanol

6

2

22945.1

1

XkeX

XXrr

t

Xd

SKe

sat

ethanol`

87 1619,0732.0

aa Xrrrrrr

t

X8710987 619.0732.0619.0732.0

AcdhAcdh Xrrrrt

X87119 619.0732.0

MASS BALANCE TO SOLID PHASE

Glucose

Pyruvate

Acetaldehyde

Ethanol

Cells

Active cells

Acetaldehyde dehydrogenase enzyme

0,0 0,2 0,4 0,6 0,8 1,0

0

20

40

60

80

100

120

140

Simulation - Glucose

Experimental- Glucose

Simulation - Ethanol

Experimental- Ethanol

Co

ncen

trati

on

(g

/L)

Axial position

0 20 40 60 80 100 120 140 160

0

20

40

60

80

100

120

140

160

180

Simulation - glucose

Experimental- glucose

Simulation - ethanol

Experimental- ethanolC

on

cen

trati

on

(g/L

)

Time(h)

Model Validation

Petrochemical resources

Acetaldehyde (CH3CHO)

Lactonitrile (CH3CHOHCN

Only racemic DL-lactic acid

Addition of HCN

and catalyst

Hydrolysis by

H2SO4

Renewable resources

Fermentable carbohydrates

Fermented broth

Optically pure L(+) or D(-) lactic acid

Chemical synthesis

Microbial fermentation

SS

F

Desirable due toi) Recent environmental issuesii) Limited nature of petrochemical resourcesiii) Flexible in terms of required properties

LACTIC ACID

Microorganism: Lactobacillus plantarum (lactic acid isomers L and D

production )isolated from sample of industrial ethanol fermentation.

Carbohydrate source: sucrose obtained from sugarcane .

Fermentation: 5 L New Brunswick

Scientific BioFlo III bioreactor (4.8 L

working volume) equipped with a

control system on-line to monitor the

fermentation parameters, such as

temperature, CO2 and turbidity.

LACTIC ACID - PRODUCTION

POLY LACTIC ACID

Just like its monomer, poly lactic

acid can be found as isomers:

• poly(L-lactic acid) (PLLA),

• poly(D-lactic acid) (PDLA) and

• poly(D, L- lactic acid) (PDLLA).

Depending on the monomer used, the poly lactic acid can be obtained

in different forms, all of them with different properties.

Due to the chirality of the molecule, the properties of polymers of lactic acid

vary greatly depending on the relation and distribution of the two isomers.

Valuable product over US 140/g (application into medical area)

XP

P

SK

S

SK

S

dt

dX

iS max

max 1

XP

P

SK

S

SK

S

Ydt

dS

iSS

X max

max 11

XXP

P

SK

S

SK

S

dt

dP

iS max

max 1

Biomass

Lactic Acid

Sucrose

MATHEMATICAL MODELING TO

LACTIC ACID PRODUCTION

UNSTRUCTURED MODEL – BATCH BIOREACTOR

SIMULATION

Kinetic parameters Value

µmax (h-1) 0.530

KS (g/L) 5.472

α 8.698

β 0.272

YX/S (g/g) 0.0947

Pmax (g/L) 40.808

KINETIC PARAMETERS OF THE PROCESS

MATHEMATICAL MODELING TO

LACTIC ACID PRODUCTION

Use of the model to develop operational conditions to

stimulate the desired genetic modifications naturally – D and L

in any desired proportion.

Lactobacillus pentosus T

DSM 20314 (D79211)

CPQBA 183-10 DRM

Lactobacillus plantarum T

NRRL B14768 (AJ965482)

Lactobacillus brevisT DSM 20054 ( M58810)

Lactobacillus fermentumT

DSM 20052 (X61142)t

Lactobacillus reuteriT

DSM 20016 (L23507)

Lactobacillus panisT

DSM 6035 (X94230)

Lactobacillus fornicalisT DSM 13171 (Y18654)

Lactobacillus acidipiscisT

HSCC 1411 (AB023836)

Pseudomonas aeruginosaT

DSM50071 (X06684)

96

100

79

72

33

42

0.02 RNA ribossomal 16S

(Lb. plantarum e Lb. pentosus) Pentoses fermentation (Whitman

et al., 2009).

LACTIC ACID - RESULTS

0 8 16 24 32 40 48 56 64 72

0

5

10

15

20

25

30

Lactic Acid

Time (h)

Co

nce

ntr

atio

n (

g/L

)

Sucrose

0

100

200

300

400

500

600

700

800

900

2º pulse

1º pulse

NaO

H so

lutio

n (m

L)

NaOH

MATHEMATICAL MODELING TO

LACTIC ACID PRODUCTION

BATCH – SEMI BATCH BIOREACTOR

Use of the model to develop operational

conditions to stimulate the desired genetic

modifications naturally – D and L in any

desired proportion

SIMULATION OF THE BIOETHANOL

PRODUCTION PROCESS FROM SUGARCANE

SIMULATION OF AN INTEGRATED PROCESS FOR

LACTIC ACID, ETHYL LACTATE AND ETHYL

ACRYLATE PRODUCTION

Representative Metabolic Route for the Process of Acrylic Acid

Production

Existing Process are based on petrochemical route

Biological Route – very low concentration with the existing

Genetic Modified Organism (around 2% conversion, very diluted

system

There exist large market for the green acrylic acid

It appears that suitable biological route maybe successfully

developed

Representative metabolic route involved in the process of acrylic

acid production

STOICHIOMETRIC REACTIONS

2 cos 1Rpyruvatee glu

a

R lucose 2 Xg

lactate 3Rpyruvate

OH acid acrylic lactate 2

R4

a

R actate 5 Xl

Ladh

R

a X 6X

ndegradatio 7R

aX

KINETIC RATES

a

a

aa XKS

SkX

KS

SkR

1

1

1

11

a

i

X

K

LKS

SkR

2

2

22

1

1aX

KP

PkR

3

33

aXKL

LkR

4

44a

i

XSKKL

LkR

55

551

1

a

ia

XAAKKL

L

KS

SkR

666

661

1

_

a

a

aa XKAA

AAkX

KS

SkR

7

7

7

77

0 10 20 30 40 50

0

10

20

30

40

50

60

70

80 Glucose

Pyruvate

Lactate

Acrylic acid

Biomass

Co

ncen

trati

on

(g

/L)

Time (h)

0 10 20 30 40 50

0

10

20

30

40

50

60

70 Glucose

Pyruvate

Lactate

Acrylic acid

Biomass

Co

ncen

trati

on

(g

/L)

Time (h)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 300

10

20

30

40

50

60

70

80

D(h-1)

0.05

0.1

0.2

0.3

0.4

0.5

Glu

cose

con

cent

rati

on (

g/L

)

Time (h)

Fermentation

Lactic Acid EsterificationE

than

ol

Distillation

Wat

er

Ethyl Lactate

Catalyst

Ethyl Acrylate

WaterDehydration

Eth

ano

l

Substrate

Ino

culu

m

CELL ON CHIP

There exist mass and heat transfer

limitations to improve the microorganism

performance.

Cell membrane has to engineered to

overcome transfer problems, but this has

not been considered suitably.

Cell on chip could be a possible

alternative.

Infra-structure to cell on chip – LOPCA/UNICAMP: Rapid Prototyping

3DPrinter (ZCorporation) FDM (Stratasys)

CELL ON CHIP AND MESH CONSTRUCTION

SIMULATIONS

A B

A

Density = 1.3229 g/cm3

Viscosity = 0.01190 kg/m.s

B

Density = 0.9128g/cm3

Viscosity = 0.00113 kg/m.s

SIMULATIONS

A

Density = 1.3229 g/cm3

Viscosity = 0.01190 kg/m.s

B

Density = 0.9128g/cm3

Viscosity = 0.00113 kg/m.s

A BA B

Practical Case Study:

Biodiesel Production

Experimental Set-up

Cell on chip

“Micro-plants

Reduce heat and mass transfer limitationsCheaper production with high performance operation

CONCLUSIONS

Simulation tools on the level of microorganisms

metabolic pathways is helpful:

To take decisions on the definitions of optimal

operational conditions

Serve as guideline to genetic modified

microorganism in a rational basis taking into

account the process requirements and real world

conditions

Work as tool to the development of operational

conditions to stimulate the desired genetic

modifications naturally

Prototipagem Rápida: Microreatores