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A

On an airport shuttle bus to the Kavli Institute for Theoretical Physics in

Santa Barbara, Calif, Chris Wiggins took a colleague's advice and opened a

Microsoft Excel spreadsheet. It had nothing to do with the talk on biopolymer

physics he was invited to give. Rather the columns and rows of numbers that

stared back at him referred to the genetic activity of budding yeast.

Specifically, the numbers represented the amount of messenger RNA (mRNA)

expressed by all 6,200 genes of the yeast over the course of its reproductive

cycle. “It was the first time I ever saw anything like this," Wiggins recalls of

that spring day in 2002. "How to make sense of all these data?"

B

Instead of shirking from this question, the 36-year-old applied mathematician

and physicist at Columbia University embraced it-and now six years later he

thinks he has an answer. By foraying into fields outside his own, Wiggins has

drudged up tools from a branch of artificial intelligence called machine

learning to model the collective protein-making activity of genes from

real-world biological data. Engineers originally designed these tools in the

late 1950s to predict output from input. Wiggins and his colleagues have now

brought machine learning to the natural sciences and tweaked it so that it can

also tell a story—one not only about input and output but also about what

happens inside a model of gene regulation, the black box in between.

C

The impetus for this work began in the late 1990s, when high-throughput

techniques generated more mRNA expression profiles and DNA sequences than ever

before, "opening up a completely different way of thinking about biological

phenomena," Wiggins says. Key among these techniques were DNA microarrays, chips

that provide a panoramic view of the activity of genes and their expression

levels in any cell type, simultaneously and under myriad conditions. As noisy

and incomplete as the data were, biologists could now query which genes turn on

or off in different cells and determine the collection of proteins that give

rise to a cell's characteristic features- healthy or diseased.

D

Yet predicting such gene activity requires uncovering the fundamental rules

that govern it. “Over time, these rules have been locked in by cells,” says

theoretical physicist Harmen Bussemaker, now an associate professor of biology

at Columbia. "Evolution has kept the good stuff." To find these rules,

scientists needed statistics to infer the interaction between genes and the

proteins that regulate them and to then mathematically describe this network's

underlying structure-the dynamic pattern of gene and protein activity over time.

But physicists who did not work with particles (or planets, for that matter)

viewed statistics as nothing short of an anathema. "If your experiment requires

statistics," British physicist Ernest Rutherford once said, "you ought to have

done a better experiment."

E

But in working with microarrays, "the experiment has been done without you,"

Wiggins explains. "And biology doesn't hand you a model to make sense of the

data." Even more challenging, the building blocks that make up DNA, RNA and

proteins are assembled in myriad ways; moreover, subtly different rules of

interaction govern their activity, making it difficult, if not impossible, to

reduce their patterns of interaction to fundamental laws. Some genes and

proteins are not even known. "You are trying to find something compelling about

the natural world in a context where you don't know very much," says William

Bialek, a biophysicist at Princeton University. "You're forced to be agnostic."

Wiggins believes that many machine-learning algorithms perform well under

precisely these conditions. When working with so many unknown variables,

"machine learning lets the data decide what's worth looking at," he says.

F

At the Kavli Institute, Wiggins began building a model of a gene regulatory

network in yeast-the set of rules by which genes and regulators collectively

orchestrate how vigorously DNA is transcribed into mRNA. As he worked with

different algorithms, he started to attend discussions on gene regulation led by

Christina Leslie, who ran the computational biology group at Columbia at the

time. Leslie suggested using a specific machine-learning tool called a

classifier. Say the algorithm must discriminate between pictures that have

bicycles in them and pictures that do not. A classifier sifts through labeled

examples and measures everything it can about them, gradually learning the

decision rules that govern the grouping. From these rules, the algorithm

generates a model that can determine whether or not new pictures have bikes in

them. In gene regulatory networks, the learning task becomes the problem of

predicting whether genes increase or decrease their protein-making activity.

G

The algorithm that Wiggins and Leslie began building in the fall of 2002 was

trained on the DNA sequences and mRNA levels of regulators expressed during a

range of conditions in yeast-when the yeast was cold, hot, starved, and so on.

Specifically, this algorithm-MEDUSA (for motif element discrimination using

sequence agglomeration)—scans every possible pairing between a set of DNA

promoter sequences, called motifs, and regulators. Then, much like a child might

match a list of words with their definitions by drawing a line between the two,

MEDUSA finds the pairing that best improves the fit between the model and the

data it tries to emulate. (Wiggins refers to these pairings as edges.) Each time

MEDUSA finds a pairing, it updates the model by adding a new rule to guide its

search for the next pairing. It then determines the strength of each pairing by

how well the rule improves the existing model. The hierarchy of numbers enables

Wiggins and his colleagues to determine which pairings are more important than

others and how they can collectively influence the activity of each of the

yeast's 6,200 genes. By adding one pairing at a time, MEDUSA can predict which

genes ratchet up their RNA production or clamp that production down, as well as

reveal the collective mechanisms that orchestrate an organism's transcriptional

logic.

Questions 1-6

The reading passage has seven paragraphs, A-G

Choose the correct heading for paragraphs A-G from the list below.

Write the correct number, i-x, in boxes 1-6 on your answer sheet.

List of Headings

i. The search for the better-fit matching between the model and the gained

figures to foresee the activities of the genes

ii. The definition of MEDUSA

iii. A flashback of a commencement for a far-reaching breakthrough

iv. A drawing of the gene map

v. An algorithm used to construct a specific model to discern the appearance

of something new by the joint effort of Wiggins and another scientist

vi. An introduction of a background tracing back to the availability of

mature techniques for detailed research on genes

vii. A way out to face the challenge confronting the scientist on the

deciding of researchable data

viii. A failure to find out some specific genes controlling the production of

certain proteins

ix. The use of a means from another domain for reference

x. A tough hurdle on the way to find the law governing the activities of the

genes

Example: Paragraph A iii

1 Paragraph B

2 Paragraph C

3 Paragraph D

4 Paragraph E

5 Paragraph F

6 Paragraph G

Questions 7-9

Do the following statements agree with the information given in Reading

Passage 1?

In boxes 7-9 on your answer sheet, write

TRUE if the statement is true

FALSE if the statement is false

NOT GIVEN if the information is not given in the passage

7. Wiggins is the first man to use DNA microarrays for the research on

genes.

8. There is almost no possibility for the effort to decrease the patterns of

interaction between DNA, RNA and proteins.

9. Wiggins holds a very positive attitude on the future of genetic

research.

Questions 10-13

Summary

Complete the following summary of the paragraphs of Reading Passage, using No

More than Three words from the Reading Passage for each answer. Write your

answers in boxes 10-13 on your answer sheet.

Wiggins states that the astoundingly rapid development of techniques

concerning the components of genes aroused the researchers to look at 10 from a

totally new way. 11 is the heart and soul of these techniques and no matter what

the 12 were, at the same time they can offer a whole picture of the genes'

activities as well as 13 in all types of cells. With these techniques scientists

could locate the exact gene which was on or off to manipulate the production of

the proteins.

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