Computational Tools For The Synthetic Design Of Biochemical Pathways

A key promise of synthetic biology is the possibility to customize the metabolic system of microorganisms

for

the

commercial

production

of

a wide

range

of

high-value

biofuels

1,2

or natural products

. Pathways for the production of alcohols, biodiesels, polyketides and terpenoids have successfully been constructed by introducing combinations of parts from various origins

into

a bacterial

host

that

is

easy

to

cultivate

3–5

. Potentially, entire metabolic pathways can be (re)

designed in silico and implemented in specialized host organisms

. Successes obtained in pioneering work on the antimalarial drug artemisinin

11–15

suggest that such approaches can be very fruitful. A biosynthetic pathway towards this compound was successfully engineered

in Saccharomyces

cerevisiae

and

Escherichia

coli

16–18

(BOX 1), and this pathway has the potential to enable much more cost-effective production of this important drug compared to the costly and laborious process of harvesting it from the source plant Artemisia annua.

The experimental work involved in engineering a synthetic pathway is considerable, and even systematically

planned

experiments

are

usually

accompanied

by

much

trial

and

error.

When

conceiving

the

design

of

a

novel

biosynthetic

pathway

(FIG. 1),

the

synthetic

biologist

has

to

find

optimal

solutions

for

selecting

pathways,

enzymes

or

host

organisms

from

an

abundance

of

possibilities.

In

this

Review,

we

explore

how

the

use

of

powerful

computational

tools

(TABLE 1)

can

lead

to

better-informed

and

more

rapid

design

and

implementation

of

novel

pathways,

and

we

propose

ways

in

which

tools

from

different

fields

of

computation

can

be

linked

together

effectively.

We

discuss

the

different

1,6–10

methodologies for identifying all possible metabolic pathways that can lead to the synthesis of a compound of choice, and how to rank these pathways based on various criteria. Subsequently, we consider how flux balance analysis of pathways can be applied to identify the most suitable candidate host organisms. We also examine how to effectively search sequence databases to obtain a list of candidate parts (such as genes and operons) for the execution of each step in the proposed

pathway.

Finally,

we

...