From Newton to Hawking, scientists love wagers. Now Lewis Wolpert has bet Rupert Sheldrake a case of fine port that: "By 1 May 2029, given the genome of a fertilised egg of an animal or plant, we will be able to predict in at least one case all the details of the organism that develops from it, including any abnormalities." If the outcome isn't obvious, then the Royal Society will be asked to adjudicate.

    Lewis Wolpert

    I HAVE entered into this wager with Rupert Sheldrake because of my interest in the details of how embryos develop, and how our understanding of this process will progress. In my latest book, How We Live and Why We Die, I suggest that it will one day be possible to predict from an embryo's genome how it will develop, and I believe it is possible for this to happen in the next 20 years.

    I am, in fact, being a little over-keen because 40 years is a more likely time frame for such a breakthrough. Cells and embryos are extremely complicated: for their size, embryonic cells are the most complex structures in the universe.

    Animals develop from a single cell, a fertilised egg, which divides to produce cells that will form the embryo. How that egg develops into an embryo and newborn animal is controlled by genes in the chromosomes. These genes are passive: they do nothing, just provide the code for proteins. It is proteins that determine how cells behave. While the DNA in every cell contains the code for all the proteins in all the cells, it is the particular proteins produced in particular cells that determine how those cells behave.

    Every cell of the embryo contains many copies of several thousand different proteins. These proteins have a plethora of functions: acting as enzymes to break down and build other molecules, providing structures for the cell, interacting with each other, and many more. The complexity of the interactions between millions of molecules is amazing.

    As the proteins determine how the cells behave, it is their activity that causes the embryo to develop. Underlying this process, though, are the genes, as they control which proteins are made - including some proteins that activate specific genes. It is essential that there is this control over which cells continue to divide, and of mechanisms to pattern the embryo so that different cells develop into different structures, such as the brain or limbs.

    There is a huge incentive to understand these processes and so be able to work out the development of an embryo given only its genome. This ability could pave the way for regenerative medicine by allowing scientists to program stem cells to become structures that could replace damaged parts of the body.

    To win the bet, we will have to be able to predict the behaviour of almost all the cells in the embryo. In a small worm, say the nematode Caenorhabditis elegans, there are 959 cells, making it the ideal model to solve this problem. It is a major challenge, but advances in cell biology, systems biology and computing will take us there.

    Rupert Sheldrake

    LEWIS WOLPERT's faith in the predictive power of the genome is misplaced. Genes enable organisms to make proteins, but do not contain programs or blueprints, or explain the development of embryos.

    The problems begin with proteins. Genes code for the linear sequences of amino acids in proteins, which then fold up into complex three-dimensional forms. Wolpert's wager presupposes that the folding of proteins can be computed from first principles, given the sequence of amino acids specified by the genes. So far, this has proved impossible. As in all bottom-up calculations, there is a combinatorial explosion. For example, by random folding, the amino-acid chain of the enzyme ribonuclease, a small protein, could adopt more than 1040 different shapes, which would take billions of years to explore. In fact, it folds into its habitual form in 2 minutes.

    Even if we could solve protein-folding, the next stage would be to predict the structure of cells on the basis of the interactions of millions of proteins and other molecules. This would unleash a far worse combinatorial explosion, with more possible arrangements than all the atoms in the universe.

    Random molecular permutations simply cannot explain how organisms work. Instead, cells, tissues and organs develop in a modular manner, shaped by morphogenetic fields, first recognised by developmental biologists in the 1920s. Wolpert himself acknowledges the importance of such fields. Among biologists, he is best known for "positional information", by which cells "know" where they are within the field of a developing organ, such as a limb. But he believes morphogenetic fields can be reduced to standard chemistry and physics. I disagree. I believe these fields have organizing abilities, or systems properties, that involve new scientific principles.

    The Human Genome Project has itself set back the hopes it engendered. First, our genome contains only between 20,000 and 25,000 genes, far fewer than the 100,000 expected. In contrast, sea urchins have about 26,000, and rice plants 38,000. Moreover, our genome differs very little from the chimpanzee's genome, the sequencing of which was completed in 2005. As Svante P??bo, director of the Chimpanzee Genome Project, commented: "We cannot see in this why we are so different from chimpanzees."

    Second, in practice, the predictive value of human genomes turns out to be low. Everyone knows tall parents tend to have tall children, and recent studies on the genomes of 30,000 people identified about 50 genes associated with being tall or short. Yet together these genes accounted for only about 5 per cent of the inheritance of height. This is not the only example of "missing heritability". Steve Jones, professor of genetics at University College London says that "hubris has been replaced with concern", and he suggests the present approach is "throwing good money after bad".

    Wolpert is not alone in believing in the predictive value of the genome. Governments, venture capitalists and medical charities have bet and are still betting billions of dollars on it. More than a case of fine port is at stake.

    A brief history of wagers

    Scientific wagers date back to Greece in the 5th or 6th century BC and were often a rhetorical device for thinking about a subject. In their current form, they can also help stimulate fresh thinking.

    One of the famous wagers of the more modern era was announced by Christopher Wren in 1684. He would give a book worth 40 shillings to anyone who could deduce Kepler's laws from the inverse-square law. Isaac Newton took this seriously and his deliberations eventually became his Principia - but too late to claim the prize.

    In 1959, physicist Richard Feynman bet $1000 that it was impossible to build a motor no bigger than 1/64 of an inch on each side. He lost: electrical engineer Bill McLellan succeeded. Feynman was said to be disappointed because he hoped his bet would stimulate new technology, but McLellan's motor used existing techniques.

    從牛頓到霍金，科學(xué)家們都愛(ài)打賭。如今Lewis Wolpert跟Rupert Sheldrake打賭說(shuō)："到2029年5月1日，只需一顆受精卵，無(wú)論動(dòng)物還是植物，我們就能預(yù)測(cè)出至少在一種情況下這顆受精卵成長(zhǎng)過(guò)程的全部細(xì)節(jié)，包括所有異常情況。"如果結(jié)果并不明顯，Lewis Wolpert就會(huì)接受英國(guó)皇家學(xué)會(huì)的審判。

    Lewis Wolpert的獨(dú)白：

    之所以跟Rupert Sheldrake打賭是因?yàn)槲覍?duì)胚胎成長(zhǎng)的過(guò)程很感興趣，并且希望能對(duì)其有更深入的了解。在我最近出版的《How We Live and Why We Die》中，我認(rèn)為總有一天人們能從胚胎的基因中預(yù)測(cè)出它成長(zhǎng)的過(guò)程，我也相信這一設(shè)想會(huì)在未來(lái)的20年內(nèi)實(shí)現(xiàn)。

    實(shí)際上，我可能過(guò)于心急了，40年時(shí)間對(duì)實(shí)現(xiàn)這一突破似乎更有可能。因?yàn)榧?xì)胞和胚胎結(jié)構(gòu)極其復(fù)雜：?jiǎn)螐某叽缟蟻?lái)講，胚胎干細(xì)胞是宇宙中最復(fù)雜的結(jié)構(gòu)。

    動(dòng)物們從一顆受精卵衍化而來(lái)，受精卵產(chǎn)生組成胚胎的細(xì)胞。染色體中的基因控制著卵子變成胚胎和新生動(dòng)物的過(guò)程。但是這些基因十分懶惰：它們什么也不做，只為蛋白質(zhì)提供編碼。因此是蛋白質(zhì)決定了細(xì)胞的行為。而細(xì)胞中的DNA包含所有細(xì)胞蛋白質(zhì)的編碼，只有個(gè)別細(xì)胞產(chǎn)生的特殊蛋白質(zhì)才決定細(xì)胞行為。

    胚胎中的每一個(gè)細(xì)胞都包含上千種不同蛋白質(zhì)的復(fù)制品。這些蛋白質(zhì)功能過(guò)剩：它們會(huì)像酶一樣分解物質(zhì)，或形成其它分子，或?yàn)榧?xì)胞賦予結(jié)構(gòu)，有些還會(huì)與其它蛋白質(zhì)進(jìn)行互動(dòng)等等。數(shù)百萬(wàn)蛋白質(zhì)分子同時(shí)進(jìn)行活動(dòng)的復(fù)雜狀態(tài)令人吃驚。

    蛋白質(zhì)決定細(xì)胞行為，蛋白質(zhì)的活動(dòng)促使胚胎發(fā)展。但是這一過(guò)程的始作俑者是基因，包括某些需要蛋白質(zhì)激活的基因，因?yàn)樗鼈兛刂频鞍踪|(zhì)的形成�；�因的控制必不可少，只有它們決定哪些細(xì)胞繼續(xù)，這樣不同的細(xì)胞才會(huì)成長(zhǎng)為不同的結(jié)構(gòu)，如大腦和四肢。

    只有了解基因，才能從一顆受精卵中判斷胚胎的發(fā)展�？茖W(xué)家們還可以將研究結(jié)果應(yīng)用到再生醫(yī)學(xué)上去，用干細(xì)胞培育器官來(lái)替換身體內(nèi)的壞死部分。

    要想贏得這場(chǎng)戰(zhàn)斗的勝利，我們必須能夠預(yù)測(cè)胚胎中所有細(xì)胞的行為。以某種小型土壤線蟲(chóng)為例，它有959個(gè)細(xì)胞，是解決這一問(wèn)題的理想模型。很顯然，這是一項(xiàng)巨大的挑戰(zhàn)，但是細(xì)胞生物學(xué)，系統(tǒng)生物學(xué)和計(jì)算機(jī)技術(shù)的發(fā)展會(huì)幫助我們將夢(mèng)想變成現(xiàn)實(shí)。

    Rupert Sheldrake的獨(dú)白：

    Lewis Wolpert竟然寄希望于基因真是異想天開(kāi)。基因促使組織制造蛋白質(zhì)這的確沒(méi)錯(cuò)，但是它們既沒(méi)有計(jì)劃，也不能解釋胚胎們的發(fā)展。

    一切問(wèn)題的根源在于蛋白質(zhì)�；�因控制蛋白質(zhì)中線性氨基酸類的編碼，這些氨基酸再折疊形成復(fù)雜的立體結(jié)構(gòu)。Wolpert認(rèn)為只需特定基因的氨基酸就能判斷蛋白質(zhì)折疊的結(jié)果。迄今為止，這是根本不可能的。因?yàn)榈鞍踪|(zhì)折疊的可能性數(shù)不勝數(shù)。例如，通過(guò)隨機(jī)折疊，核糖核酸酶（一種小型蛋白質(zhì)）的氨基酸鏈能形成超過(guò)1040種不同的結(jié)構(gòu)，單這一種蛋白質(zhì)就需要數(shù)億年的時(shí)間來(lái)探索。而實(shí)際上，氨基酸鏈折疊的過(guò)程只需兩分鐘。

    即使我們能抓住蛋白質(zhì)折疊的規(guī)律，下一步就是通過(guò)分析數(shù)百萬(wàn)蛋白質(zhì)和其它分子之間的相互作用，來(lái)預(yù)測(cè)細(xì)胞的結(jié)構(gòu)。這勢(shì)必會(huì)引發(fā)另一次更大規(guī)模的信息爆炸，因?yàn)檫@一過(guò)程產(chǎn)生的可能性比宇宙中所有的原子數(shù)量還要多。

    僅憑分析隨機(jī)分子排列的規(guī)律不可能解釋器官的形成。相反，早在20世紀(jì)20年代發(fā)育學(xué)家們就認(rèn)識(shí)到細(xì)胞，組織和器官是按照特定的模式而生長(zhǎng)，這種模式是由形態(tài)發(fā)生場(chǎng)所而決定的。Wolpert知道這些場(chǎng)所的重要性。在生物學(xué)家之中，他以知曉"位置信息"而聞名，位置信息就是細(xì)胞"知道"其在生長(zhǎng)器官中的位置，比如四肢。但是他認(rèn)為形態(tài)發(fā)生場(chǎng)所會(huì)被周圍的化學(xué)或物理作用而削弱。這一點(diǎn)我不認(rèn)同。我相信形態(tài)發(fā)生場(chǎng)所具有組織能力或者系統(tǒng)功能，對(duì)其的研究將會(huì)發(fā)現(xiàn)新的科學(xué)原理。

    人類基因組計(jì)劃就證明了這一預(yù)測(cè)很不現(xiàn)實(shí)。首先，我們的基因組只包含2萬(wàn)至2萬(wàn)5千個(gè)基因，與預(yù)期的10萬(wàn)相去甚遠(yuǎn)。相比較，海膽有2萬(wàn)6千個(gè)基因，而谷類植物的基因有3萬(wàn)8千個(gè)。此外，據(jù)2005年的研究顯示人類與黑猩猩基因差別很小。黑猩猩基因組計(jì)劃的負(fù)責(zé)人Svante P??bo曾說(shuō)過(guò)："我們不能從黑猩猩的基因組中判斷出為什么我們與黑猩猩不一樣。"

    其次，實(shí)際上，人類基因組的預(yù)測(cè)價(jià)值很低。每個(gè)人都知道高個(gè)家長(zhǎng)容易有高個(gè)孩子，而最近對(duì)3萬(wàn)人的基因組進(jìn)行鑒定后發(fā)現(xiàn)只有50個(gè)基因與人的高矮有關(guān)。這些基因加在一起只對(duì)身高遺傳起到5%的作用。這并不是"失傳現(xiàn)象"的唯一例證。倫敦大學(xué)學(xué)院的遺傳學(xué)教授Steve Jones說(shuō)過(guò)："驕傲已蒙蔽了憂慮的雙眼。"他認(rèn)為目前的研究方向是"賠了夫人又折兵".

    Wolpert并不是唯一一個(gè)堅(jiān)信人類基因組預(yù)測(cè)價(jià)值的人。政府部門，資本家們以及慈善機(jī)構(gòu)都在上面下注，一擲千金。這樣做的結(jié)果很危險(xiǎn)。

    科學(xué)家打賭簡(jiǎn)史

    科學(xué)家打賭的歷史可以追溯到公元前5、6世紀(jì)的希臘，那時(shí)打賭是一種用來(lái)刺激人們思考的手段。就現(xiàn)在來(lái)看，打賭仍舊可以激發(fā)人們的靈感。

    現(xiàn)代最著名的打賭發(fā)生在1684年。Christopher Wren打賭如果有人能用平方反比定律推論開(kāi)普勒定律，他就會(huì)將一本價(jià)值40先令的書(shū)送給這個(gè)人。Isaac Newton經(jīng)過(guò)深思熟慮最終形成了他的著名理論，但對(duì)于領(lǐng)取獎(jiǎng)賞為時(shí)已晚。

    1959年，物理學(xué)家Richard Feynman打賭1000美元，預(yù)言不可能有人制造出邊長(zhǎng)不超過(guò)六十四分之一英尺的馬達(dá)。最終電機(jī)工程師Bill McLellan抱得美元?dú)w。Feynman稱這一結(jié)果令他很失望，他本希望這次能刺激人們進(jìn)行技術(shù)創(chuàng)新，但是McLellan制造的馬達(dá)仍使用現(xiàn)有技術(shù)。

    1975年，Stephen Hawking與同伴宇宙學(xué)家Kip Thorne曾打賭天鵝座X-1是否含有黑洞，賭注是輸家為贏家訂閱雜志。結(jié)果Hawking認(rèn)輸，也恰好從這時(shí)起Hawking開(kāi)始花費(fèi)大量時(shí)間研究黑洞。