Why the Human Genome’s Tangled Physicality May Confound AI

Samuel Velasco and Hannah Waters/ Quanta Magazine

塞缪尔·贝拉斯科 (Samuel Velasco) 和汉娜·沃特斯 (Hannah Waters)/Quanta 杂志

Since its molecular structure was deduced in the 1950s, DNA has been hailed by many biologists as the secret of life.

自从20世纪50年代其分子结构被推导以来，DNA就被许多生物学家誉为生命的秘密。

They’ve read and studied the information stored in the DNA found in the cells of living organisms, known as their genomes, and claimed that this genetic database must be some kind of blueprint, code script, or computer.

他们阅读并研究了生物体细胞中 DNA 中存储的信息，即基因组，并声称这个遗传数据库一定是某种蓝图、代码脚本或计算机。

But if DNA really does harbor some greater secret about how life works, biologists have yet to find it.

但如果 DNA 确实隐藏着关于生命如何运作的更大秘密，生物学家还没有找到它。

In fact, the human genome is less a script than a puzzle that gets harder the closer they look.

事实上，人类基因组与其说是一个脚本，不如说是一个谜题，越仔细观察就越难。

Knowing the entire sequence — the order of all 3 billion or so of our DNA’s chemical building blocks, nearly fully deduced by the international Human Genome Project between 1990 and 2003 — hasn’t helped much.

了解整个序列——大约 30 亿个 DNA 化学构件的顺序，国际人类基因组计划在 1990 年至 2003 年间几乎完全推导出来——并没有多大帮助。

That investigation showed that barely 2% of the human genome consists of actual genes, the information-coding sequences of DNA.

该调查表明，人类基因组中只有 2% 是由实际基因（即 DNA 信息编码序列）组成的。

It’s now clear that understanding the human genome is no longer a matter of figuring out what each gene does.

现在很明显，了解人类基因组不再是弄清楚每个基因的作用。

The deeper and much harder question is how those genes are used, or regulated, a question that seems to involve some and perhaps much of the rest of the genome.

更深入、更困难的问题是如何使用或调节这些基因，这个问题似乎涉及基因组的一些甚至大部分其余部分。

By switching suites of genes on and off, the many different cell types in our bodies can all be created from the same material.

通过打开和关闭基因组，我们体内的许多不同细胞类型都可以由相同的材料产生。

Cells also regulate their genes from moment to moment in response to a constant inflow of signals from their neighbors and surroundings.

细胞还时时刻刻调节它们的基因，以响应来自邻居和周围环境的不断流入的信号。

But the processes that govern gene regulation are proving so complex that some biologists wonder whether a full understanding of it — of how the genome really works — will ever be within the grasp of our puny minds.

但事实证明，控制基因调控的过程是如此复杂，以至于一些生物学家怀疑我们是否能够完全理解基因组的真正运作方式。

Some are counting on outsourcing the analysis to artificial intelligence.

一些人指望将分析工作外包给人工智能。

Genomic “foundation models” such as Evo 2, Genos, and Google DeepMind’s AlphaGenome are trained on vast quantities of genomic data , which biologists use to make predictions about how differences in DNA sequence affect biological processes and ultimately the traits (including disease risk) of a whole organism.

Evo 2、Genos 和 Google DeepMind 的 AlphaGenome 等基因组“基础模型”经过大量基因组数据的训练，生物学家利用这些数据来预测 DNA 序列的差异如何影响生物过程，并最终影响整个生物体的特征（包括疾病风险）。

These algorithms don’t worry about the complicated regulatory stuff going on; all of that is supposedly subsumed by the algorithm’s “training,” through which it deduces correlations from cases we already know about.

这些算法不担心正在发生的复杂的监管问题；据推测，所有这些都包含在算法的“训练”中，通过它从我们已经知道的案例中推断出相关性。

This approach is likely to be useful, but for those who crave real understanding of how the genome, and ultimately life itself, works, a computational black box will never suffice.

这种方法可能有用，但对于那些渴望真正了解基因组以及最终生命本身如何运作的人来说，计算黑匣子永远不够。

And perhaps more to the point, the genome might not submit to the kind of straightforward input-output approach that such AI models ultimately assume.

也许更重要的是，基因组可能不会服从此类人工智能模型最终假设的那种简单的输入输出方法。

That’s because the genome is no blueprint or algorithm.

那是因为基因组不是蓝图或算法。

It is something else.

这是另外一回事。

Given that it’s the product of around 4 billion years of evolution, perhaps it’s not surprising that our genome is complicated.

鉴于它是大约 40 亿年进化的产物，我们的基因组如此复杂也许并不奇怪。

The surprise has been what those complications are.

令人惊讶的是这些并发症是什么。

“Our genome is not what we might make it if we sat down at the drawing board,” said the biologist Karen Adelman , who studies gene regulation at Harvard Medical School.

哈佛医学院研究基因调控的生物学家凯伦·阿德尔曼 (Karen Adelman) 表示：“如果我们坐在绘图板上，我们的基因组并不是我们所能创造的。”

We’ve stopped thinking about the genome as a linear piece of DNA code.

我们已经不再将基因组视为一段线性 DNA 代码。

Wendy Bickmore, University of Edinburgh

温迪·比克莫尔，爱丁堡大学

The traditional view posits that a small proportion of our DNA holds the code for making the protein molecules that orchestrate our cells’ chemistry.

传统观点认为，我们 DNA 的一小部分保存着制造蛋白质分子的代码，这些蛋白质分子协调着我们细胞的化学反应。

Each instruction for a protein is held in a corresponding gene — we have around 20,000 of these — and gene sequences can range in length from a couple of dozen to almost 3 million DNA “letters” (representing molecules called nucleotides).

蛋白质的每条指令都保存在相应的基因中——我们大约有 20,000 个这样的基因——基因序列的长度可以从几十个到近 300 万个 DNA“字母”（代表称为核苷酸的分子）不等。

Making a protein from its gene is a two-stage affair.

从基因中制造蛋白质需要两个阶段。

First the DNA is read, letter by letter, by an enzyme called a polymerase, which creates a copy of that code in a related molecule called messenger RNA (mRNA).

首先，一种称为聚合酶的酶逐个字母地读取 DNA，聚合酶会在称为信使 RNA (mRNA) 的相关分子中创建该代码的副本。

This is called transcription.

这称为转录。

The mRNA is then read by a piece of molecular machinery called the ribosome, which constructs the protein — a process called translation.

然后，mRNA 被称为核糖体的分子机器读取，并构建蛋白质，这一过程称为翻译。

The proteins made by the ribosome then go off to do their jobs in making and sustaining the organism.

然后，核糖体产生的蛋白质就会开始发挥其制造和维持生物体的作用。

This picture is still more or less correct.

这张图或多或少还是正确的。

But it turns out that “the genes are probably not the most interesting part of the genome,” Adelman said.

但事实证明，“基因可能不是基因组中最有趣的部分，”阿德尔曼说。

What matters more is how our genes, many of which we share with simpler organisms, are regulated: turned on and off.

更重要的是我们的基因（其中许多基因与更简单的生物体共有）是如何调节的：打开和关闭。

Which proteins a cell needs changes over time and according to cell type: muscle, brain, skin, and so on.

细胞需要哪些蛋白质随着时间的推移并根据细胞类型而变化：肌肉、大脑、皮肤等。

How the genes that encode those proteins are regulated depends on some of the genome that doesn’t code for proteins.

编码这些蛋白质的基因如何受到调节取决于一些不编码蛋白质的基因组。

Biologists have known about gene regulation, and the involvement of “noncoding” DNA, since the 1960s.

自 20 世纪 60 年代以来，生物学家就了解基因调控以及“非编码”DNA 的参与。

But for many years, most of what they understood about this came from studies of simple organisms like bacteria, where the principles are generally straightforward.

但多年来，他们对此的理解大部分来自对细菌等简单生物体的研究，其中的原理通常很简单。

It has gradually become clear, though, that in complex eukaryotic organisms like us, gene regulation is far more complicated, involving overlapping systems of oversight and control, each with its own intricacies.

然而，人们逐渐清楚，在像我们这样的复杂真核生物中，基因调控要复杂得多，涉及重叠的监督和控制系统，每个系统都有其复杂性。

Transcription gets started by proteins called transcription factors, which are like the operations managers of gene regulation.

转录由称为转录因子的蛋白质启动，它们就像基因调控的操作管理者。

These proteins stick to sections of DNA (typically close to the target gene) and recruit the polymerase enzyme to make an mRNA copy.

这些蛋白质粘附在 DNA 片段上（通常靠近目标基因）并招募聚合酶来制作 mRNA 副本。

In bacteria, transcription factors are rather like keys that fit the locks of unique binding sites on DNA.

在细菌中，转录因子就像一把钥匙，可以锁住 DNA 上独特结合位点的锁。

But that’s not how they work in complex organisms.

但这并不是它们在复杂生物体中的工作方式。

In us, the logic of transcription factors is more difficult to parse.

在我们看来，转录因子的逻辑更难解析。

For one thing, our transcription factors don’t show strong preferences for particular DNA binding sites.

一方面，我们的转录因子对特定的 DNA 结合位点没有表现出强烈的偏好。

What’s more, they tend to work in pairs or groups.

更重要的是，他们倾向于结对或小组工作。

And a given transcription factor might have different effects in different contexts, such as activating gene transcription in one cell type but suppressing it in another, depending on which other transcription factors are around.

给定的转录因子在不同的环境中可能会产生不同的效果，例如在一种细胞类型中激活基因转录，但在另一种细胞类型中抑制基因转录，具体取决于周围有哪些其他转录因子。

In bacteria, regulation tends to have an “OR” logic, Adelman said, whereby a particular signal turns a gene on or off: It’s either this or that.

阿德尔曼说，在细菌中，调节往往具有“或”逻辑，即特定信号打开或关闭基因：要么是这个，要么是那个。

But in the human genome the logic is more like what computer scientists designate “AND.” Many signals are integrated to reach a regulatory decision: this and that and also that other thing.

但在人类基因组中，逻辑更像是计算机科学家所说的“AND”。许多信号被整合以达成监管决策：这个、那个以及其他的事情。

In this case, regulation can be more responsive to nuances of context, and the regulatory knobs are tunable rather than being just on/off.

在这种情况下，监管可以更好地响应环境的细微差别，并且监管旋钮是可调的，而不仅仅是开/关。

“This is part of the beauty” of our regulatory complexity, Adelman said.

阿德尔曼说，“这是我们监管复杂性的一部分”。

When they interact with the genome, transcription factors bind to pieces of DNA called enhancers — which present a puzzle of their own.

当转录因子与基因组相互作用时，它们会与称为增强子的 DNA 片段结合，这本身就是一个难题。

Enhancers are gathering points for transcription factors, and they are thought to be the decisive influence on transcription: They deliver the “go” signal for a waiting polymerase to make an mRNA version of the DNA sequence.

增强子是转录因子的聚集点，它们被认为对转录具有决定性影响：它们为等待的聚合酶传递“开始”信号，以生成 DNA 序列的 mRNA 版本。

Seems simple enough, but mapping enhancers to their respective genes is far from straightforward.

看起来很简单，但将增强子映射到各自的基因却远非如此简单。

Our genome has hundreds of thousands, perhaps millions, of enhancers.

我们的基因组有数十万甚至数百万个增强子。

That means we have many more of them than we have genes.

这意味着我们拥有的这些基因比我们拥有的基因还要多。

Each gene might be influenced by many enhancers, and each enhancer might influence multiple genes.

每个基因可能受到许多增强子的影响，每个增强子可能影响多个基因。

The genome isn’t static — it’s living.

基因组不是静态的——它是活的。

Karen Adelman, Harvard Medical School

凯伦·阿德尔曼，哈佛医学院

“It’s embarrassing that 25 years after the Human Genome Project, we don’t know where all the enhancers are in the genome, let alone what they do when they act and which genes they control,” said Wendy Bickmore , a genome biologist at the University of Edinburgh.

“令人尴尬的是，人类基因组计划 25 年后，我们不知道所有增强子在基因组中的位置，更不用说它们在发挥作用时做什么以及它们控制哪些基因，”爱丁堡大学基因组生物学家 Wendy Bickmore 说。

Biologists do know that most enhancers won’t respond to a single transcription factor.

生物学家确实知道大多数增强子不会对单个转录因子产生反应。

Their activation “requires a cocktail,” Bickmore said.

比克莫尔说，它们的激活“需要一杯鸡尾酒”。

“That’s what gives [an enhancer] that exquisite specificity — because it’s only in a particular cell at a particular time that you have the right combination of factors to bind and activate that enhancer.”

“这就是赋予[增强子]如此精致的特异性的原因——因为只有在特定时间的特定细胞中，你才有正确的因子组合来结合和激活该增强子。”

Some enhancers are, as you’d expect, close to the genes they regulate, or even sit on DNA inside a gene.

正如您所料，一些增强子靠近它们所调节的基因，甚至位于基因内的 DNA 上。

But others sit far away from the gene — perhaps millions of nucleotides away, with more genes in between.

但其他基因则距离基因很远——也许有数百万个核苷酸，中间有更多的基因。

The existence of such so-called “distal” enhancers “seems bonkers,” Bickmore said.

比克莫尔说，这种所谓的“远端”增强子的存在“似乎很疯狂”。

“How do you get that information from over there to over here, to the gene that needs to be activated?

“你如何将信息从那边传送到这里，传送到需要激活的基因？

That’s a largely unanswered question.”

这是一个基本上没有答案的问题。”

One of the answers comes in the form of a loop.

答案之一以循环的形式出现。

Distal enhancers are brought to the gene they regulate on great loops of DNA or, more strictly, of chromatin, the combination of DNA and its packaging proteins that are unraveled as if from a ball of wool.

远端增强子被带到它们在DNA大环上调节的基因，或者更严格地说，在染色质大环上，DNA及其包装蛋白的组合，就像从羊毛球中解开一样。

The loops are created by a protein motor called cohesin, which runs up and down the DNA strand and extrudes it as needed.

这些环是由一种称为粘连蛋白的蛋白质马达产生的，它在 DNA 链上上下运行，并根据需要将其挤出。

Once cohesin has formed a loop to bring elements together, what then?

一旦粘连蛋白形成一个环将元素聚集在一起，然后呢？

It was once thought that they then stick together or assemble into a molecular machine, but they don’t.

人们曾经认为它们会粘在一起或组装成分子机器，但事实并非如此。

Rather, the components appear to form a loose but dense blob in which they interact rather weakly, fleetingly, and indiscriminately — a sort of committee, sometimes called a condensate.

相反，这些成分似乎形成了一个松散但致密的斑点，它们在其中相互作用相当微弱、短暂且不加区别——一种委员会，有时称为凝聚体。

These transcription hubs are extremely fluid and differ from one cell to another.

这些转录中心非常不稳定，并且每个细胞之间都存在差异。

“There’ll be a bit of loop extrusion going on over here, in the next cell it might be over here, and the whole thing is turning over incredibly fast,” Bickmore said.

比克莫尔说：“这里会发生一些循环挤压，在下一个单元中它可能会在这里，整个事情的翻转速度非常快。”

Even if the cells are notionally identical — both skin cells, say — exactly what the gene-regulatory machinery is up to at any moment is never quite the same in any two of them.

即使这些细胞在概念上是相同的——比如都是皮肤细胞——但它们中的任何两个细胞的基因调节机制在任何时刻的作用都永远不会完全相同。

Chromatin loops are just one reason why a gene’s transcription depends on the shape and structure of the chromatin around it.

染色质环只是基因转录取决于其周围染色质的形状和结构的原因之一。

The textbook image of a chromosome — one of the 46 units into which our genomes are divided — is of a compact, X-shaped cluster of chromatin .

染色体（我们的基因组划分为 46 个单位之一）的教科书图像是一个紧凑的 X 形染色质簇。

But any time a cell is not actively dividing, its chromatin is unwound into what looks like a tangled mess.

但只要细胞不活跃分裂，它的染色质就会解开，形成看起来像一团乱七八糟的东西。

There is order to the chaos, however.

然而，混乱中也有秩序。

Some parts of chromatin are densely packed into a form called heterochromatin.

染色质的某些部分密集地堆积成一种称为异染色质的形式。

The compacted DNA there is relatively inaccessible to transcription factors; the genes it contains are typically silenced.

转录因子相对难以接近那里压缩的 DNA；它所包含的基因通常是沉默的。

Meanwhile, other parts are relatively loose, open, and accessible: This is called euchromatin.

与此同时，其他部分相对宽松、开放且易于接近：这被称为常染色质。

There are special enzymes involved in packaging and repackaging chromatin, thereby controlling transcription.

有一些特殊的酶参与包装和重新包装染色质，从而控制转录。

In other words, what matters is not just the encoded information in the DNA but also how it exists physically and dynamically in space.

换句话说，重要的不仅仅是 DNA 中的编码信息，还有它在空间中物理和动态存在的方式。

“We’ve stopped thinking about the genome as a linear piece of DNA code,” Bickmore said.

“我们已经不再将基因组视为一段线性 DNA 代码，”比克莫尔说。

“Thinking about this incredibly dynamic three-dimensional folding as absolutely inherent to regulation is a very exciting change.”

“将这种令人难以置信的动态三维折叠视为监管所固有的，是一个非常令人兴奋的变化。”

One aspect of this 3D organization is the clustering of segments of chromatin into compartments called topologically associating domains (TADs).

这种 3D 组织的一个方面是将染色质片段聚集到称为拓扑关联域 (TAD) 的区室中。

Within a TAD, the genes seem to be coregulated: switched on or off in groups.

在 TAD 中，基因似乎是共同调控的：成组地打开或关闭。

Such groups keep suites of genes active or silent together to form and provide function in different cell types.

这些群体将活性或沉默的基因组保持在一起，以在不同的细胞类型中形成并提供功能。

Cohesin is also involved in the shuffling of chromatin to construct TADs — a dynamic process in which the chromatin is constantly rearranged in our cells.

粘连蛋白还参与染色质的改组以构建 TAD，这是一个染色质在细胞中不断重新排列的动态过程。

Chromatin shape can also be influenced by chemical modifications called epigenetic marks: small molecules attached to DNA packaging proteins called histones or stuck directly to DNA.

染色质形状也会受到称为表观遗传标记的化学修饰的影响：附着在 DNA 包装蛋白（称为组蛋白）上或直接粘附在 DNA 上的小分子。

Some of these epigenetic modifications can alter the electrical charges on histones, which changes how the proteins attract or repel one another and so rejigs the chromatin packing.

其中一些表观遗传修饰可以改变组蛋白上的电荷，从而改变蛋白质相互吸引或排斥的方式，从而重新调整染色质堆积。

Epigenetic modifications to chromatin are like annotations of the DNA script that change its meaning in a given context.

染色质的表观遗传修饰就像 DNA 脚本的注释，会改变其在给定上下文中的含义。

When cells divide, the epigenetic annotations are copied, too.

当细胞分裂时，表观遗传注释也会被复制。

How and when the marks get added and changed, and what each type of mark means for gene activity, are complex questions with no simple answers.

如何以及何时添加和更改标记，以及每种类型的标记对基因活性意味着什么，都是复杂的问题，没有简单的答案。

Some researchers talk of an “epigenetic code” governing this aspect of gene regulation, but it’s far from clear if anything so systematic really exists.

一些研究人员谈到了控制基因调控这一方面的“表观遗传密码”，但尚不清楚是否真的存在如此系统的东西。

All of these processes and others can determine whether a gene gets transcribed into mRNA.

所有这些过程和其他过程都可以决定基因是否被转录成 mRNA。

But there are further layers of regulation that determine whether the mRNA is then translated into a corresponding protein — and which protein arises.

但还有更深层的调控决定了 mRNA 是否会被翻译成相应的蛋白质，以及会产生哪种蛋白质。

This post-transcriptional regulation is often controlled by RNA molecules that are said to be noncoding.

这种转录后调节通常由非编码 RNA 分子控制。

These short-lived molecules aren’t templates for proteins, as mRNA is, but have other jobs of their own.

这些短命分子不像 mRNA 那样是蛋白质的模板，但它们还有其他作用。

While mRNA is produced from the protein-coding areas of DNA (so-called “coding genes”), noncoding RNAs are transcribed from other DNA regions now generally described as noncoding genes.

虽然 mRNA 是从 DNA 的蛋白质编码区域（所谓的“编码基因”）产生的，但非编码 RNA 是从现在通常称为非编码基因的其他 DNA 区域转录的。

These noncoding RNAs are versatile , taking on varied roles in a cell.

这些非编码 RNA 用途广泛，在细胞中发挥着不同的作用。

Researchers are learning more about what they can do every day, and many if not most of them seem to be involved in gene regulation.

研究人员正在更多地了解他们每天可以做什么，而且许多（如果不是大多数）似乎都参与了基因调控。

Small noncoding RNAs called microRNAs, for example, can silence mRNAs before they can be translated into proteins.

例如，称为 microRNA 的小型非编码 RNA 可以在 mRNA 翻译成蛋白质之前使它们沉默。

They do this by guiding special enzymes to a particular mRNA to degrade or chemically modify it.

他们通过引导特殊的酶作用于特定的 mRNA 来降解或化学修饰它来实现这一点。

The microRNAs don’t do this job alone but, not unlike transcription factors, act combinatorially, in groups, and in a rather promiscuous manner: A given microRNA might regulate many mRNAs, and a given mRNA might be regulated by many microRNAs.

microRNA 不能单独完成这项工作，但与转录因子不同，它们以相当混杂的方式组合、成组地发挥作用：给定的 microRNA 可能调节许多 mRNA，而给定的 mRNA 可能受到许多 microRNA 的调节。

Why make an mRNA only to stop it getting translated in a protein?

为什么制造 mRNA 只是为了阻止它被翻译成蛋白质？

This sort of post-transcriptional regulation is like having another checkpoint: Does the cell really need this protein?

这种转录后调节就像有另一个检查点：细胞真的需要这种蛋白质吗？

MicroRNAs can be mobilized to allow cells to adjust gene expression depending on the immediate context .

MicroRNA 可以被调动，使细胞能够根据当前情况调整基因表达。

In this way, the workings of the genome are less like a program’s inevitable progression and more like an adaptive and responsive process.

这样，基因组的运作就不再像程序不可避免的进展，而更像是一个适应性和响应性的过程。

Another post-transcriptional complication is that mRNAs get translated to protein only after they have been reorganized.

另一个转录后并发症是 mRNA 仅在重组后才能翻译为蛋白质。

Fresh from transcription, an mRNA contains sequences that encode bits of protein, called exons, as well as sequences that shouldn’t be translated and need to be snipped out, called introns.

刚完成转录后，mRNA 包含编码蛋白质片段的序列（称为外显子），以及不应该翻译且需要剪掉的序列（称为内含子）。

(Strictly speaking, this pre-edited RNA is called pre-mRNA.) The job of editing introns out and splicing exons together is done by a molecular assembly called the spliceosome, which is made from several proteins together with various noncoding RNAs.

（严格来说，这种预编辑的 RNA 称为 pre-mRNA。）编辑内含子并将外显子剪接在一起的工作是由称为剪接体的分子组装体完成的，该分子组装体由多种蛋白质和各种非编码 RNA 组成。

The spliceosome too can be sensitive to context, so that it might splice the pre-mRNA to encode one protein in one cell type and a slightly different protein in another.

剪接体也可能对环境敏感，因此它可能会剪接前体 mRNA，编码一种细胞类型中的一种蛋白质，而另一种细胞类型中编码略有不同的蛋白质。

Sometimes these different protein “isoforms” can have very different roles.

有时，这些不同的蛋白质“亚型”可以发挥非常不同的作用。

Transcription factors, for example, are often alternatively spliced in this way, and their isoforms can take on different regulatory tasks — some might activate gene expression, for instance, while others repress it.

例如，转录因子通常以这种方式选择性剪接，它们的亚型可以承担不同的调节任务——例如，有些可能激活基因表达，而另一些则抑制基因表达。

All told, these and other regulatory mechanisms show that the genome is far from some automated program running in the background to build us and keep us alive.

总而言之，这些和其他调节机制表明，基因组远非某种在后台运行的、构建我们并维持我们生存的自动化程序。

Our cells are, in effect, making complex decisions about how to use their genes — both the information they contain and the structure they assume.

事实上，我们的细胞正在就如何使用它们的基因——它们所包含的信息和它们所假设的结构——做出复杂的决定。

Thus, cells need to assemble a rather loose and fuzzy committee of components, such as transcription factors and enhancers, to get transcription underway, which also depends on how the chromatin strand is shaped and molded at that moment.

因此，细胞需要组装一个相当松散和模糊的组件委员会，例如转录因子和增强子，才能进行转录，这也取决于当时染色质链的形状和塑造方式。

Then there are further layers of decision-making and action-taking in between mRNA and the final, functional protein.

然后，mRNA 和最终的功能蛋白之间还有进一步的决策和行动层。

Remember, too, that all the players — from transcription factors to noncoding RNAs — are themselves produced from the genome in the same kind of context-dependent process.

还要记住，所有参与者——从转录因子到非编码 RNA——本身都是在相同的上下文依赖过程中从基因组中产生的。

That makes the genome rather like a recursive, self-referential system that the computer scientist Douglas Hofstadter dubbed “ a strange loop .” It acts on itself, mindful of its own history (which determines chromatin conformation and epigenetic markings, say) and heedful of messages from inside and outside the cell.

这使得基因组更像是一个递归的、自我参照的系统，计算机科学家道格拉斯·霍夫施塔特（Douglas Hofstadter）将其称为“奇怪的循环”。它作用于自身，注意自身的历史（例如，它决定染色质构象和表观遗传标记）并留意来自细胞内部和外部的信息。

Not, then, a blueprint.

那么，这并不是一个蓝图。

And for that reason, not at all easy to understand.

因此，一点也不容易理解。

“I wouldn’t have designed it this way if I was God,” Bickmore said.

“如果我是上帝，我就不会这样设计它，”比克莫尔说。

“But here we are!”

“但是我们来了！”

Why is gene regulation in animals like us so darned complicated?

为什么像我们这样的动物的基因调控如此复杂？

One potential answer is that evolution doesn’t have the foresight to design with efficiency and transparent logic, but merely tinkers with what it has already available.

一个可能的答案是，进化没有远见，无法以高效和透明的逻辑进行设计，而只是对现有的东西进行修补。

Maybe so — but eukaryotic gene regulation isn’t just a messy version of what happens in bacteria.

也许是这样——但真核基因调控不仅仅是细菌中发生的事情的混乱版本。

It has different principles, and there’s surely a reason for them.

它有不同的原则，而且肯定有其原因。

Bickmore suspects that the complexity of regulation and of genome organization might have been the only means of generating complexity in the organism.

比克莫尔怀疑，调控和基因组组织的复杂性可能是在生物体中产生复杂性的唯一手段。

For example, organisms with many tissue types and varied lifestyles required more control over which genes were on or off in a given cell.

例如，具有多种组织类型和不同生活方式的生物体需要更多地控制给定细胞中哪些基因打开或关闭。

One thing this demanded was more and more noncoding regulatory sequences in DNA.

这需要的一件事是 DNA 中存在越来越多的非编码调控序列。

But then they couldn’t all fit close to the gene itself.

但它们不可能都与基因本身非常吻合。

“As you get more complexity, you need to add more and more enhancers,” Bickmore said.

“当你变得越来越复杂时，你需要添加越来越多的增强器，”比克莫尔说。

“But where are you going to put them?

“但是你要把它们放在哪里呢？

You start to put them farther and farther away.

你开始把它们放得越来越远。

Once they are [far enough], you start to need TADs and three-dimensional [chromatin] folding to allow those things to work.”

一旦它们[足够远]，你就开始需要TAD和三维[染色质]折叠来让这些东西发挥作用。”

We also need regulatory complexity because, over evolutionary time, the human genome has accumulated DNA from parasitic viruses in the form of jumping genetic material called transposable elements .

我们还需要复杂的监管，因为在进化过程中，人类基因组以称为转座元件的跳跃遗传物质的形式积累了来自寄生病毒的 DNA。

These sequences have inserted themselves all over our chromosomes and are good at replicating themselves.

这些序列已将自身插入到我们的整个染色体中，并且善于自我复制。

To sift the good DNA from the bad, we needed additional layers of regulation to ensure that cells weren’t translating RNAs they don’t really need or that could be actively harmful.

为了从坏的 DNA 中筛选出好的 DNA，我们需要额外的监管层以确保细胞不会翻译它们并不真正需要的或可能有害的 RNA。

With so many context-dependent checks and balances in the workings of our genome, it is evidently not a program or algorithm that predictably generates the same outcome in every situation.

在我们的基因组运作中存在如此多与环境相关的检查和平衡，显然不是一个程序或算法可以在每种情况下都可预测地产生相同的结果。

It’s an open informational system that responds to external inputs and the genome’s dynamic internal conditions.

它是一个开放的信息系统，可以响应外部输入和基因组的动态内部条件。

This poses a challenge if AI relies solely on the genetic sequences within genomes to predict what genomes will do.

如果人工智能仅依赖基因组内的基因序列来预测基因组会做什么，这就构成了挑战。

Researchers developing AI-based genomic foundation models such as AlphaGenome hope that all these layers of regulation — transcription factors, splicing, epigenetic marks, loops, chromatin packing, and so on — will be implicitly included in the correlations that the algorithms learn between genetic sequence and organismal traits.

开发基于人工智能的基因组基础模型（例如 AlphaGenome）的研究人员希望所有这些调控层（转录因子、剪接、表观遗传标记、环、染色质包装等）都将隐式包含在算法学习的基因序列和生物性状之间的相关性中。

They’re content for the complexity described above to be in a black box, so long as the model generates accurate predictions.

只要模型能够生成准确的预测，他们就满足于上述黑匣子中的复杂性。

But will that work?

但这行得通吗？

“I’m sure [AlphaGenome] is going to be useful, but with limitations,” Bickmore said.

“我确信 [AlphaGenome] 会很有用，但有局限性，”比克莫尔说。

“To me the big gap is in the complexity of the human body — in all the cell types and how they change over time in development.

“对我来说，最大的差距在于人体的复杂性——所有细胞类型以及它们在发育过程中如何变化。

And all that data is missing.”

所有这些数据都丢失了。”

Fundamentally, the challenge is that the genome is not a set of static, linear instructions.

从根本上说，挑战在于基因组不是一组静态的线性指令。

It is highly dynamic, and it uses its information contextually, with combinatorial and promiscuous logic.

它是高度动态的，并根据上下文使用其信息，具有组合和混杂的逻辑。

“Whether we’ll ever be able to capture that aspect” in algorithms like AlphaGenome, “I don’t know,” she said.

“我们是否能够在 AlphaGenome 等算法中捕捉到这个方面”，“我不知道，”她说。

Yet the problem goes even deeper because the functioning of specific organisms, including each of us, doesn’t just depend on genomes.

然而，问题更加严重，因为特定有机体（包括我们每个人）的功能不仅仅取决于基因组。

Other factors, such as diet, environment, microbiome and, for us at least, culture, can matter hugely, too — not just in terms of how we act and how healthy we are but also in the state of our genome itself.

其他因素，例如饮食、环境、微生物组，以及至少对我们来说，文化，也可能非常重要——不仅关系到我们的行为方式和健康状况，还关系到我们的基因组本身的状态。

The biologist Adrian Woolfson , co-founder of California-based biotech company Genyro, which aims to use AI systems for so-called “generative biology,” calls this information cloud the “informiome.”

生物学家阿德里安·伍尔夫森 (Adrian Woolfson) 是加州生物技术公司 Genyro 的联合创始人，该公司旨在将人工智能系统用于所谓的“生成生物学”，他将这种信息云称为“信息组”。

“While the human genome forms the foundation of the human informiome, other layers of extra-genetic information are equally important,” Woolfson wrote in his book On the Future of Species , published in April 2026.

“虽然人类基因组构成了人类信息组的基础，但其他层面的遗传外信息也同样重要，”伍尔夫森在其 2026 年 4 月出版的《论物种的未来》一书中写道。

Genomic foundation models won’t even be able to predict all the consequences of genetic mutations, he argued, because the relevant information is not in the genome sequence in the first place.

他认为，基因组基础模型甚至无法预测基因突变的所有后果，因为相关信息一开始就不在基因组序列中。

So how should we think about the genome?

那么我们应该如何看待基因组呢？

Maybe the only metaphors that can capture the way the genome really works must come from biology itself.

也许唯一能够描述基因组真正运作方式的隐喻必须来自生物学本身。

In 2020, the biological historian Evelyn Fox compared the genome to “an exquisitely sensitive reactive system.” Rather than a sequence of genes leading to the formation of traits, she said, it’s more of “a device for regulating the production of specific proteins in response to constantly changing signals it receives from its environment.”

2020 年，生物历史学家伊芙琳·福克斯 (Evelyn Fox) 将基因组比作“一个极其敏感的反应系统”。她说，它不是导致性状形成的基因序列，而是“一种调节特定蛋白质产生的装置，以响应从环境中接收到的不断变化的信号”。

That sounds close to the picture painted by the geneticist Barbara McClintock in the address she delivered upon being awarded the 1983 Nobel Prize in Physiology or Medicine for her discovery of transposons.

这听起来与遗传学家芭芭拉·麦克林托克 (Barbara McClintock) 因发现转座子而获得 1983 年诺贝尔生理学或医学奖时在演讲中所描绘的画面很接近。

The genome, she declared, is “a highly sensitive organ of the cell, monitoring genomic activities and correcting common errors, sensing the unusual and unexpected events and responding to them, often by restructuring the genome.”

她宣称，基因组是“细胞的高度敏感器官，通常通过重组基因组来监测基因组活动并纠正常见错误，感知不寻常和意外事件并对其做出反应。”

Research since that time has fleshed out this image, revealing how the shape of chromatin can matter as much as the information its DNA sequences encode and how an army of molecules collaborates to reorganize it and make collective decisions about how to use its genetic information in context-dependent ways.

自那时以来的研究充实了这一形象，揭示了染色质的形状如何与其DNA序列编码的信息一样重要，以及一群分子如何协作重组它，并就如何以上下文相关的方式使用其遗传信息做出集体决策。

There is no human technology that works this way, so metaphors such as blueprints, programs, or computers will always fall short.

没有任何人类技术能够以这种方式工作，因此诸如蓝图、程序或计算机之类的隐喻总是不够的。

Bickmore is optimistic that the workings of the genome are understandable, despite its complexity.

比克莫尔乐观地认为，尽管基因组很复杂，但它的运作方式是可以理解的。

“We’ve got a handle on it now,” she said.

“我们现在已经掌握了这件事，”她说。

“We might not know the details, but I think the whole field is coalescing now into a framework where we’re thinking along similar lines.” AI can surely help with this sense-making, but in the end, human reasoning will be needed to discern the fundamental principles.

“我们可能不知道细节，但我认为整个领域现在正在合并成一个框架，我们在这个框架中沿着类似的思路思考。”人工智能肯定可以帮助理解这种意义，但最终需要人类推理来辨别基本原理。

“McClintock was far more on point than people realized at the time,” Adelman said.

“麦克林托克的观点比人们当时意识到的要准确得多，”阿德尔曼说。

“What she said was that the genome isn’t static — it’s living.”

“她说的是基因组不是静态的——它是活的。”

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Abusive, profane, self-promotional, misleading, incoherent or off-topic comments will be rejected.

辱骂、亵渎、自我推销、误导、语无伦次或偏离主题的评论将被拒绝。

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版主在正常工作时间（纽约时间）有工作人员，并且只能接受英文评论。