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At the dawn of modern genetic research, almost no one anticipated the enormous complexity in the biology of disease.
在現代基因研究的初期，幾乎沒有人預見到疾病生物學的巨大復雜性。
Many researchers thought the genome would be a kind of instruction manual for the body.
很多研究人員認為，基因組應該是人體的一種指導手冊。
Pioneers such as Celera Genomics' Craig Venter and Francis Collins of the National Institutes of Health were celebrated as "gene hunters,"
像塞雷拉基因組公司的克雷格·溫特以及國立衛生研究院的弗朗西斯·柯林斯這樣的先驅被譽為“基因獵手”，
a term that evoked crusaders scouring the globe for that one "silver bullet" gene that would explain—and facilitate a cure for—a given disease.
這個詞讓人聯想到十字軍戰士們在全球尋找一種“銀彈”基因，這種基因能夠解釋——并幫助治愈——某種特定的疾病。
To some extent, these researchers found real treasure. Geneticist Nancy Wexler, for example,
在某種程度上，這些研究人員發現了真正的寶藏。例如，遺傳學者Nancy Wexler
spent years in Venezuela compiling family trees of those affected by Huntington's disease, a rare, inherited condition.
花了多年時間在委內瑞拉匯編亨廷頓氏舞蹈病患者的族譜，這是一種罕見的遺傳病。
Her work led to the discovery of the mutation in a single gene that predicts whether an individual will contract the condition.
她的研究發現了一種單基因的突變，這種突變可以預測一個人是否會患上這種病。
But scientists soon realized that genetic maps were less like an instruction manual and more like the parts catalog you get with Ikea furniture.
但科學家們很快意識到，基因圖譜不太像一本說明書，而更像是宜家家具的零件目錄。
What's more, researchers discovered other catalogs that added complex variables to the relationships between genes and disease—
更重要的是，研究人員還發現了其他目錄，這些目錄為基因和疾病之間的關系增加了復雜的變量——
for example, the proteome, the proteins encoded by DNA, and the transcriptome, all the nucleic acids that convert DNA into proteins.
例如，蛋白質組，DNA編碼的蛋白質，以及轉錄組，所有將DNA轉換成蛋白質的核酸。
The morning-after disappointment has proved wrenching, as researchers learned that complex diseases,
失望是痛苦的，因為研究人員發現，復雜的疾病，
such as cancer and Alzheimer's, didn't yield to a single gene. (Even Huntington's, its gene identified, has remained untreatable.)
如癌癥和阿爾茨海默癥，不會產生單個基因。（即使亨廷頓氏舞蹈癥的基因已被確認，它仍然無法治愈。）
Today, Cohen and others see a link between the obsession with simplicity and a decline in drug discovery.
如今，科恩和其他人看到了對簡單的癡迷和對藥物發現的減少之間的聯系。
That decline shows itself in the 1-in-10 success rate for FDA approval of new therapies; in spiraling costs for drug development
這種減少表現在：FDA批準新療法的成功率為十分之一；藥物研發成本不斷攀升
(what a Tufts study recently identified as "the $2.6 billion pill"); and in the soaring prices of the few treatments that break new ground,
（塔夫斯大學最近的一項研究將其認定為“價值26億美元的藥丸”）；為數不多的幾種有突破的治療方法價格飛漲，
such as the $475,000 cost of a course of treatment with Novartis's leukemia drug Kymriah.
如諾華的抗白血病藥物Kymriah一個療程的費用為47.5萬美元。
More recently, researchers have begun to grapple with biological complexity with the help of the science of networks.
最近，研究人員開始在網絡科學的幫助下設法解決生物學的復雜性。
That science's chief evangelist is Albert-Laszlo Barabasi,
這一科學的首席布道師是艾伯特-拉斯洛·巴拉巴西，
a professor at Northeastern University whose 2014 book Linked popularized the notion that network theory can explain numerous fields,
美國東北大學的一名教授，他在2014年出版的《Linked》一書中普及了這樣一種概念，即網絡理論可以解釋各種領域，
from fashion trends to sexual relations to disease.
如流行趨勢、性關系以及疾病。
Barabasi and others realized that disease is like a bad signal that moves through a network of connections from genes to proteins to cells to tissues,
巴拉巴西和其他人意識到，疾病就像一個不良信號，通過一個連接網絡，在基因、蛋白質、細胞和組織之間移動
until all these "perturbations" manifest as the familiar symptoms of disease.
直到所有這些“混亂”顯現出常見的疾病癥狀。
Complicated diseases are confluences of numerous effects, because pleiotropy means that any given protein can act at different points in the body.
復雜的疾病是多種影響的匯合，因為基因多效性意味著任何給定蛋白質都可以在身體的不同部位起作用。
Startups like Pharnext assume that drugs can also be pleiotropic,
像Pharnext的初創公司認為，藥物也可以是多效性的，
acting on more than one protein and more than one interaction in the body at the same time.
在同一時間作用于不止一種蛋白質，在體內不止一種相互作用。
To find a drug combination capable of tackling complexity, the enormous power of machine learning,
為了尋找能夠解決復雜性的組合藥物，機器學習的巨大能力
with its ability to spot patterns in data, must be wedded to a sense of the structure by which disease operates.
以及其發現數據模式的能力，必須與對疾病運行結構的感知結合起來。
This, in turn, has required an evolution in the relationship between computer scientists and biologists.
這反過來要求計算機科學家和生物學家之間的關系發生進化。
Newer machine-learning approaches ingest vastly more data and can assemble hierarchies of information that let them go beyond correlation.
更新的機器學習方法可以大量吸收更多的數據，并且能夠將信息進行層次組合，使其超越相關性。
Still, harnessing these "deep learning" neural networks into a structure that has any predictive power requires some elegant algorithm-building.
不過，要將這些“深度學習”神經網絡轉化為具有預測能力的結構，需要一些簡煉的算法構建。
Colin Hill, CEO and founder of GNS Healthcare, is one of the builders.
GNS Healthcare的CEO兼創始人Colin Hill就是構建者之一。
His company, based in Cambridge, Mass., has spent 18 years developing a computer system called REFS,
他的公司位于馬薩諸塞州劍橋市，該公司花了18年研發一種電腦系統——REFS，
which stands for "reverse engineering, forward simulation."
全稱為“逆向工程，正演模擬。”
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