Life Code Unlocked Reading Answers

Feb 3, 2023

Life Code Unlocked Reading Answers is a academic topic in the IELTS Reading section. This has been taken from the book: Cambridge IELTS 3. The IELTS reading section helps candidates increase their reading skills with the help of passages. Candidates need to read the passage and then answer the questions. There are 13 questions in this topic: Life Code Unlocked Reading Answers. The IELTS reading questions are divided into two sections: choose the appropriate heading, true/false/not given and no more than three words. There are more topics like Life Code Unlocked Reading Answers available online. Candidates can practice from IELTS Reading practice papers to help them excel in the IE:LTS exam.

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Section 1

Read the Passage to Answer the Following Questions

Life Code Unlocked Reading Answers

{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 this 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 makeup 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 a yeast-the set of rules by which genes selectively 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.

Section 2

Solution and Explanation
Read the Passage to Answer the Following Questions
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 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

Question 1:- Paragraph B

Answer: ix
Supporting Sentence: 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.
Keywords: foraying, Wiggins, artificial intelligence
Keyword Location: Paragraph B, lines 2-5
Explanation: Wiggins has used techniques from machine learning. It is a branch of artificial intelligence, to analyze real-world biological data and model the protein production of genes in other fields.

Question 2:- Paragraph C

Answer: vi
Supporting Sentence: 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.
Keywords: impetus, high-throughput, mRNA
Keyword Location: Paragraph C, lines 1-3
Explanation: Wiggins began this research in the late 1990s. It was driven by the availability of high-throughput techniques that generated a large amount of mRNA expression profiles and DNA sequences. This new data opened up new possibilities for understanding biological phenomena, according to Wiggins.

Question 3:- Paragraph D

Answer: x
Supporting Sentence: 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.
Keywords: predicting, gene, theoretical
Keyword Location: Paragraph D, lines 1-3
Explanation: Predicting gene activity requires understanding the underlying principles that govern it. According to theoretical physicist Harmen Bussemaker these principles have been established over time through evolution.

Question 4:- Paragraph E

Answer: vii
Supporting Sentence: 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 makeup DNA, RNA, and proteins are assembled in myriad ways.
Keywords: microarrays, biology
Keyword Location: Paragraph E, lines 1-4
Explanation: According to Wiggins, working with microarrays presents a unique challenge as the experiment has already been conducted. And biology does not provide a pre-existing model to interpret the data. Additionally, the elements of DNA, RNA, and proteins are arranged in a complex and varied manner.

Question 5:- Paragraph F

Answer: v
Supporting Sentence: 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.
Keywords: algorithms, gene regulation
Keyword Location: Paragraph F, lines 3-5
Explanation: While working with various algorithms, he began participating in discussions on gene regulation. It was led by Christina Leslie, who was heading the computational biology group at Columbia. Leslie proposed using a specific machine-learning tool, known as a classifier.

Question 6:- Paragraph G

Answer: i
Supporting Sentence: 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.
Keywords: 2002, DNA sequences, mRNA
Keyword Location: Paragraph G, lines 1-3
Explanation: In the fall of 2002, Wiggins and Leslie started building an algorithm. It was trained on the DNA sequences and mRNA levels of regulators expressed under various conditions in yeast, such as when it was cold, hot, or starved.

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

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

Answer: Not given
Explanation: No relevant information associated with the reading passage has been provided in the reading passage.

There is almost no possibility for the effort to decrease the patterns of interaction between DNA, RNA, and proteins.

Answer: True
Supporting Sentence: Moreover, subtly different rules of interaction govern their activity, making it difficult, if not impossible, to reduce their patterns of interaction to fundamental laws.
Keywords: subtly, interaction, fundamental
Keyword Location: Paragraph E, lines 4-5
Explanation: Additionally, the rules governing their activity differ slightly. Further, making it challenging, if not impossible, to simplify their patterns of interaction to fundamental principles.

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

Answer: Not given
Explanation: No relevant information associated with the reading passage has been provided in the reading passage.

Questions 10-13:
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.

Question 10.

Answer: Biological phenomena
Supporting Sentence: 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.
Keywords: impetus, high-throughput, mRNA
Keyword Location: Paragraph C, lines 1-3
Explanation: Wiggins began this research in the late 1990s, driven by the availability of high-throughput techniques. This generated a large amount of mRNA expression profiles and DNA sequences. This new data opened up new possibilities for understanding biological phenomena, according to Wiggins.

Question 11.

Answer: DNA Microarrays
Supporting Sentence: 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.
Keywords: DNA microarrays, panoramic
Keyword Location: Paragraph C, lines 3-5
Explanation: One of the key techniques used were DNA microarrays. This offered a comprehensive view of the activity. Further, expression levels of genes in any type of cell, under various conditions, at the same time.

Question 12.

Answer: Myriad Conditions
Supporting Sentence: 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.
Keywords: DNA microarrays, panoramic
Keyword Location: Paragraph C, lines 3-5
Explanation: One of the key techniques used were DNA microarrays. This offered a comprehensive view of the activity. Further, expression levels of genes in any type of cell, under various conditions, at the same time.

Question 13.

Answer: their expression levels
Supporting Sentence: 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.
Keywords: DNA microarrays, panoramic
Keyword Location: Paragraph C, lines 3-5
Explanation: One of the key techniques used were DNA microarrays. This offered a comprehensive view of the activity. Further, expression levels of genes in any type of cell, under various conditions, at the same time.

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