内容来源：https://www.wired.com/story/why-ai-isnt-ready-to-tackle-our-convoluted-genomes/

内容总结：

DNA常被生物学家誉为“生命的秘密”，但科学家逐渐发现，人类基因组更像一个越研究越复杂的谜题，而非一份简单的蓝图或代码脚本。自2003年人类基因组计划完成以来，研究人员意识到仅有约2%的基因组由实际编码蛋白质的基因组成，更深层的问题在于这些基因如何被使用和调控。基因调控涉及增强子、转录因子、染色质三维折叠、表观遗传标记以及非编码RNA等多层机制，其逻辑更接近“与”逻辑而非简单的开关，且高度依赖细胞类型、时间和环境背景。爱丁堡大学基因组生物学家Wendy Bickmore指出，人类基因组计划完成25年后，科学界仍不清楚所有增强子的位置及其作用。哈佛医学院生物学家Karen Adelman表示，基因组并非静态，而是“活的”。一些科学家寄望于Evo 2、AlphaGenome等基因组基础模型通过人工智能预测基因序列与生物性状的关系，但批评者认为，这类“黑箱”模型难以真正揭示基因组的运作原理，因为基因组是一个高度动态、递归、自我参照的开放信息系统，其功能不仅取决于序列本身，还受饮食、环境、微生物组等基因组外信息影响。生物学家Barbara McClintock早在1983年诺奖演讲中就提出，基因组是“细胞的高度敏感器官”，能够监测并响应内外信号，甚至重塑自身。尽管调控机制极为复杂，Bickmore仍对理解基因组持乐观态度，认为该领域正在形成共识框架，人工智能可辅助分析，但最终仍需人类推理来揭示其基本原理。

中文翻译：

这个故事的原始版本发表于《量子杂志》。

自20世纪50年代DNA的分子结构被推导出来以来，许多生物学家一直赞誉DNA为生命的秘密。他们读取并研究了存在于生物体细胞中的DNA所储存的信息——即基因组——并声称这个遗传数据库必定是某种蓝图、代码脚本或计算机。但如果DNA真的隐藏着关于生命运作方式的某种更深层的秘密，生物学家至今仍未找到。

事实上，人类基因组与其说是一部脚本，不如说是一个越仔细看就越发复杂的谜题。仅凭知道完整的序列——即我们DNA全部约30亿个化学构建块的排列顺序，这几乎已由1990年至2003年间的国际人类基因组计划基本推导完成——并没有太大帮助。那项研究表明，人类基因组中仅有不到2%由实际基因组成，即DNA中编码信息的序列。

现在已经很清楚，理解人类基因组不再仅仅是弄清楚每个基因做什么的问题。更深层也更困难的问题是这些基因如何被使用、或被调控——这个问题似乎涉及基因组中其余部分的一些乃至大部分。通过成组地开启和关闭基因，我们体内许多不同的细胞类型都可以从同一套材料中产生。细胞还会根据来自邻近细胞和周围环境的持续信号流入，时刻调控自己的基因。但主管基因调控的过程被证明如此复杂，以至于一些生物学家怀疑，对我们有限的大脑而言，是否有可能完全理解它——理解基因组究竟如何运作。

有些人寄希望于将分析外包给人工智能。诸如Evo 2、Genos以及谷歌DeepMind的AlphaGenome等基因组“基础模型”在大量基因组数据上接受训练，生物学家利用这些模型来预测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性状（包括疾病风险）。这些算法并不操心正在发生的复杂调控过程。所有这些据称都被算法的“训练”所涵盖——训练过程中，算法从我们已知的案例中推导出相关性。

这种方法很可能有用，但对于那些渴望真正理解基因组乃至生命本身如何运作的人来说，一个计算黑箱永远不够。也许更重要的是，基因组可能并不服从这类AI模型最终所假设的那种直截了当的输入-输出方式。

这是因为基因组不是蓝图，也不是算法。它是别的东西。

鉴于它是大约40亿年进化的产物，我们的基因组很复杂，这或许并不令人意外。令人意外的是这些复杂性的具体面貌。“我们的基因组并不是我们坐在绘图板前会设计出来的样子，”哈佛医学院研究基因调控的生物学家凯伦·阿德尔曼说。

传统观点认为，我们DNA的一小部分携带着制造蛋白质分子的代码，这些蛋白质分子 orchestrate 我们细胞的化学过程。制造每种蛋白质的指令存在于相应的基因中——我们大约有2万个这样的基因——基因序列的长度从几十个到近300万个DNA“字母”（代表称为核苷酸的分子）不等。

从一个基因制造蛋白质是一个两步过程。首先，DNA被一种称为聚合酶的酶逐字母读取，该酶在一个称为信使RNA（mRNA）的相关分子中创建该代码的副本。这称为转录。然后，mRNA被一种称为核糖体的分子机器读取，核糖体构建蛋白质——这一过程称为翻译。核糖体制造的蛋白质随后去执行它们制造和维持生物体的工作。

这幅图景仍然大致正确。但事实证明，“基因可能并不是基因组中最有趣的部分，”阿德尔曼说。

更重要的是我们的基因——其中许多我们与更简单的生物共享——如何被调控，即如何被开启和关闭。一个细胞需要哪些蛋白质会随时间以及根据细胞类型而变化：肌肉、大脑、皮肤等等。编码这些蛋白质的基因如何被调控，取决于基因组中一些不编码蛋白质的部分。

生物学家自20世纪60年代以来就知道基因调控以及“非编码”DNA的参与。但许多年来，他们对此的大部分理解来自对细菌等简单生物的研究，在这些生物中原理通常比较直接。然而，人们逐渐清楚地认识到，在我们这样的复杂真核生物中，基因调控要复杂得多，涉及相互重叠的监督和控制体系，每个体系都有自己的复杂性。

转录由称为转录因子的蛋白质启动，它们就像是基因调控的运营经理。这些蛋白质附着在DNA的某些片段上（通常靠近目标基因），并招募聚合酶来制造mRNA副本。在细菌中，转录因子颇像钥匙，恰好插入DNA上独特结合位点的锁中。但在复杂生物中并非如此运作。在我们体内，转录因子的逻辑更难解析。

一方面，我们的转录因子对特定的DNA结合位点没有强烈的偏好。此外，它们往往成对或成组地工作。而且，一个给定的转录因子在不同情境下可能产生不同的效果，例如在一种细胞类型中激活基因转录，而在另一种细胞类型中抑制它，这取决于周围有哪些其他转录因子。

阿德尔曼说，在细菌中，调控往往具有“或”逻辑，即某个特定信号开启或关闭一个基因：非此即彼。但在人类基因组中，逻辑更像计算机科学家所说的“与”。许多信号被整合以达成一个调控决策：这个和那个以及那个其他的。在这种情况下，调控可以更好地回应情境的细微差别，调控旋钮是可调的，而不仅仅是开或关。“这正是我们调控复杂性之美的一部分，”阿德尔曼说。

当转录因子与基因组相互作用时，它们结合到称为增强子的DNA片段上——增强子本身就构成了一个谜题。

增强子是转录因子的聚集点，它们被认为是转录的决定性影响因素：它们为等待中的聚合酶发出“开始”信号，使其制造DNA序列的mRNA版本。看起来够简单，但将增强子与其各自的基因对应起来远非直截了当。我们的基因组有数十万、甚至可能数百万个增强子。这意味着我们的增强子数量远远多于基因数量。每个基因可能受到许多增强子的影响，而每个增强子可能影响多个基因。

“令人尴尬的是，人类基因组计划25年后的今天，我们不知道基因组中所有增强子在哪里，更不用说它们发挥作用时做什么以及它们控制哪些基因了，”爱丁堡大学基因组生物学家温迪·比克莫尔说。

生物学家确实知道，大多数增强子不会对单一转录因子作出响应。它们的激活“需要一杯鸡尾酒，”比克莫尔说。“这正是[增强子]具有那种精妙特异性的原因——因为只有在特定时间的特定细胞中，你才拥有正确的因子组合来结合并激活那个增强子。”

有些增强子如你所料，靠近它们调控的基因，甚至位于基因内部的DNA上。但其他的则远离基因——可能相隔数百万个核苷酸，中间还隔着更多基因。

比克莫尔说，这种所谓的“远端”增强子的存在“看起来简直疯了”。“你怎么把信息从那边传到这边，传到需要被激活的基因那里？这基本上是一个未解的问题。”

答案之一以环的形式出现。

远端增强子通过DNA——更严格地说是染色质（DNA与其包装蛋白的组合，像毛线球一样被解开）——的巨大环被带到它们调控的基因附近。这些环由一种称为黏连蛋白的蛋白质马达创建，它沿着DNA链上下移动，根据需要将其挤出成环。

一旦黏连蛋白形成了将各元素拉到一起的环，然后呢？曾经有人认为它们会粘在一起或组装成一台分子机器，但事实并非如此。相反，这些组件似乎形成一个松散但密集的团块，在其中它们以相当微弱、短暂且不加区分的方式相互作用——一种类似于委员会的结构，有时被称为凝聚体。

这些转录枢纽极其流动，因细胞而异。“这里会有一些环挤出正在进行，在下一个细胞中可能在那里，整个东西以令人难以置信的速度在翻转，”比克莫尔说。即使细胞在名义上是相同的——比如都是皮肤细胞——基因调控机器在任何时刻究竟在做什么，在任何两个细胞中都不完全相同。

染色质环只是基因转录取决于其周围染色质形状和结构的原因之一。

染色体的教科书图像——我们的基因组被分为46个单位，每个单位就是一条染色体——是一个紧凑的X形染色质簇。但每当细胞不在活跃分裂时，其染色质就被解开成看起来像一团乱麻的样子。然而，混乱中自有秩序。染色质的某些部分被密集包装成一种称为异染色质的形式。那里压缩的DNA相对难以被转录因子接近；它包含的基因通常被沉默。与此同时，其他部分相对松散、开放且可接近：这被称为真染色质。

有特殊的酶参与染色质的包装和重新包装，从而控制转录。换句话说，重要的不仅是DNA中编码的信息，还有它如何在空间中物理地、动态地存在。“我们已经不再把基因组看作一条线性的DNA代码，”比克莫尔说。“把这种极其动态的三维折叠视为调控的绝对固有属性，是一个非常令人兴奋的转变。”

这种三维组织的一个方面是染色质片段聚集成称为拓扑关联域（TAD）的隔室。在一个TAD内，基因似乎是协同调控的：成组地开启或关闭。这样的组使成套的基因一起保持活跃或沉默，以在不同细胞类型中形成并提供功能。黏连蛋白也参与染色质的重排以构建TAD——这是一个在我们细胞中染色质不断被重新排列的动态过程。

染色质形状还可以受到称为表观遗传标记的化学修饰的影响：附着在称为组蛋白的DNA包装蛋白上的小分子，或直接粘附在DNA上的小分子。其中一些表观遗传修饰可以改变组蛋白上的电荷，从而改变蛋白质之间相互吸引或排斥的方式，进而重新调整染色质包装。染色质的表观遗传修饰就像DNA脚本上的注释，在特定情境中改变其含义。当细胞分裂时，表观遗传注释也会被复制。

标记如何以及何时被添加和改变，每种标记对基因活动意味着什么，这些都是复杂的问题，没有简单的答案。一些研究人员谈到一种“表观遗传密码”主管基因调控的这一方面，但是否真的存在如此系统化的东西，远未明确。

所有这些过程以及其他过程可以决定一个基因是否被转录为mRNA。但还有更多层的调控决定mRNA随后是否被翻译为相应的蛋白质——以及产生哪种蛋白质。

这种转录后调控通常由被称为非编码的RNA分子控制。这些短寿命的分子不像mRNA那样是蛋白质的模板，而是有自己的其他工作。mRNA是从DNA的蛋白质编码区域（所谓的“编码基因”）产生的，而非编码RNA则从其他DNA区域转录而来，这些区域现在一般被描述为非编码基因。这些非编码RNA功能多样，在细胞中承担各种角色。研究人员每天都在更多地了解它们能做什么，其中许多——如果不是大多数的话——似乎都参与基因调控。

例如，称为microRNA的小型非编码RNA可以在mRNA被翻译成蛋白质之前使其沉默。它们通过引导特殊酶到特定的mRNA上使其降解或进行化学修饰来实现这一点。microRNA并非单独完成这项工作，而是与转录因子类似，以组合方式、成组地、以相当混杂的方式行动：一个给定的microRNA可能调控许多mRNA，而一个给定的mRNA可能受到许多microRNA的调控。

为什么要制造一个mRNA却阻止它被翻译成蛋白质？这种转录后调控就像有另一个检查点：细胞真的需要这种蛋白质吗？microRNA可以被调动起来，使细胞能够根据当下情境调整基因表达。这样一来，基因组的运作就不太像程序不可阻挡的推进，而更像一个适应性的、响应性的过程。

另一个转录后的复杂情况是，mRNA只有在被重新组织之后才会被翻译成蛋白质。刚转录出来的mRNA包含编码蛋白质片段的序列（称为外显子），以及不应被翻译且需要被剪除的序列（称为内含子）。（严格来说，这个编辑前的RNA称为前体mRNA。）剪除内含子并将外显子拼接在一起的工作由一个称为剪接体的分子组装体完成，它由几种蛋白质与多种非编码RNA共同组成。

剪接体也可以对情境敏感，因此它可能将前体mRNA剪接成在一种细胞类型中编码一种蛋白质、在另一种细胞类型中编码略有不同的蛋白质。有时这些不同的蛋白质“异构体”可以具有非常不同的角色。例如，转录因子经常以这种方式进行选择性剪接，它们的异构体可以承担不同的调控任务——例如，有些可能激活基因表达，而另一些则抑制它。

总而言之，这些和其他调控机制表明，基因组远不是某种在后台自动运行以构建我们并维持我们生命的程序。我们的细胞实际上在做出复杂的决定，关于如何使用它们的基因——既包括基因所含的信息，也包括基因所呈现的结构。

因此，细胞需要组装一个相当松散且模糊的组件委员会——如转录因子和增强子——来启动转录，而这还取决于染色质链在那一刻是如何被塑造和模制的。然后在mRNA和最终的功能蛋白质之间还有更多层的决策和行动。

还要记住，所有的参与者——从转录因子到非编码RNA——本身也是以同样依赖情境的过程从基因组中产生的。这使得基因组颇像一个递归的、自指的体系，计算机科学家道格拉斯·霍夫施塔特称之为“奇圈”。它作用于自身，留意自身的历史——比如决定染色质构象和表观遗传标记的历史——并关注来自细胞内外部的信息。因此，它不是蓝图。

也正因如此，它一点也不容易理解。“如果我是上帝，我不会这样设计它，”比克莫尔说。“但事已至此！”

为什么像我们这样的动物的基因调控如此复杂？一个可能的答案是，进化没有先见之明来以高效和透明的逻辑进行设计，而只是对已有的东西修修补补。也许如此——但真核生物的基因调控不仅仅是细菌中所发生事情的混乱版本。它有不同的原理，其中必定有其原因。

比克莫尔怀疑，调控和基因组组织的复杂性可能是产生生物体复杂性的唯一手段。例如，具有许多组织类型和多样生活方式的生物需要对特定细胞中哪些基因开启或关闭有更多控制。这要求DNA中有越来越多的非编码调控序列。但这样的话它们不可能都紧挨着基因本身。

“随着复杂性增加，你需要添加越来越多的增强子，”比克莫尔说。“但你打算把它们放在哪里？你开始把它们放得越来越远。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。”

我们还需要调控复杂性，因为在进化时间尺度上，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。这些序列已将自己插入我们的染色体各处，并擅长自我复制。为了从坏DNA中筛选出好DNA，我们需要额外的调控层来确保细胞不会翻译它们并不真正需要的或可能有害的RNA。

在我们的基因组运作中有如此多依赖情境的制衡机制，它显然不是一个能在每种情况下可预测地产生相同结果的程序或算法。它是一个开放的信息系统，对外部输入和基因组动态的内部条件作出响应。如果AI仅依赖基因组内的遗传序列来预测基因组将做什么，这就构成了挑战。

开发基于AI的基因组基础模型（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、染色质包装等等——都将被隐含地包含在算法所学习的遗传序列与生物体性状之间的相关性中。他们满足于上述复杂性被放在黑箱中，只要模型能产生准确的预测。但这能行得通吗？

“我确信[AlphaGenome]会有用，但有局限性，”比克莫尔说。“对我来说，最大的空白在于人体的复杂性——在所有细胞类型以及它们在发育过程中如何随时间变化。而所有这些数据都是缺失的。”

从根本上说，挑战在于基因组不是一组静态的、线性的指令。它是高度动态的，并且以组合和混杂的逻辑在情境中使用其信息。“我们是否能够[在AlphaGenome这样的算法中]捕捉到那个方面，我不知道，”她说。

然而问题还要更深，因为特定生物体——包括我们每一个人——的运作不仅仅取决于基因组。其他因素，如饮食、环境、微生物组，以及至少对我们而言还有文化，也可能至关重要——不仅体现在我们如何行动和有多健康，还体现在我们基因组本身的状态上。生物学家阿德里安·伍尔夫森是加利福尼亚生物技术公司Genyro的联合创始人，该公司旨在利用AI系统进行所谓的“生成生物学”，他称这个信息云为“信息组”。

“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。他论证说，基因组基础模型甚至无法预测基因突变的所有后果，因为相关信息首先就不在基因组序列中。

那么我们应该如何看待基因组？也许唯一能捕捉基因组真正运作方式的隐喻必须来自生物学本身。2020年，生物学史学家伊夫林·福克斯将基因组比作“一个极其灵敏的反应系统”。她说，它不是一系列基因导致性状形成的序列，而更像是“一个根据从环境不断接收的不断变化的信号来调控特定蛋白质产生的装置”。

这听起来接近遗传学家芭芭拉·麦克林托克在1983年因其发现转座子而获得诺贝尔生理学或医学奖时所发表演讲中描绘的图景。她宣称，基因组是“细胞的一个高度敏感的器官，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作出响应，往往通过重组基因组来实现。”

自那时以来的研究充实了这一图景，揭示了染色质的形状如何与其DNA序列所编码的信息同等重要，以及一支分子大军如何协作重组它并集体决定如何以依赖情境的方式使用其遗传信息。没有任何人类技术是这样运作的，因此蓝图、程序或计算机之类的隐喻永远不够用。

比克莫尔乐观地认为，尽管基因组复杂，其运作是可以理解的。“我们现在已经掌握了一些头绪，”她说。“我们可能不知道细节，但我认为整个领域正在凝聚成一个框架，我们在沿着相似的思路思考。”AI当然可以帮助这种意义建构，但最终仍需要人类的推理来辨别基本原理。

“麦克林托克远比当时人们意识到的更为切中要害，”阿德尔曼说。“她所说的是，基因组不是静态的——它是活的。”

原始报道经《量子杂志》许可转载。《量子杂志》是西蒙斯基金会旗下编辑独立的出版物，其使命是通过报道数学、物理和生命科学的研究进展和趋势来增进公众对科学的理解。

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英文来源：

The original version of this story appeared in Quanta Magazine.
Since its molecular structure was deduced in the 1950s, DNA has been hailed by many biologists as the secret of life. 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. But if DNA really does harbor some greater secret about how life works, biologists have yet to find it.
In fact, the human genome is less a script than a puzzle that gets harder the closer they look. 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. That investigation showed that barely 2 percent of the human genome consists of actual genes, the information-coding sequences of DNA.
It’s now clear that understanding the human genome is no longer a matter of figuring out what each gene does. 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. By switching suites of genes on and off, the many different cell types in our bodies can all be created from the same material. Cells also regulate their genes from moment to moment in response to a constant inflow of signals from their neighbors and surroundings. 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.
Some are counting on outsourcing the analysis to artificial intelligence. 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. 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.
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. 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.
That’s because the genome is no blueprint or algorithm. It’s something else.
Given that it’s the product of around 4 billion years of evolution, perhaps it’s not surprising that our genome is complicated. The surprise has been what those complications are. “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.
The traditional view posits that a small proportion of our DNA holds the code for making the protein molecules that orchestrate our cells’ chemistry. 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).
Making a protein from its gene is a two-stage affair. 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). This is called transcription. The mRNA is then read by a piece of molecular machinery called the ribosome, which constructs the protein—a process called translation. The proteins made by the ribosome then go off to do their jobs in making and sustaining the organism.
This picture is still more or less correct. But it turns out that “the genes are probably not the most interesting part of the genome,” Adelman said.
What matters more is how our genes, many of which we share with simpler organisms, are regulated—turned on and off. Which proteins a cell needs changes over time and according to cell type: muscle, brain, skin, and so on. How the genes that encode those proteins are regulated depends on some of the genome that doesn’t code for proteins.
Biologists have known about gene regulation, and the involvement of “noncoding” DNA, since the 1960s. 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. 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.
Transcription gets started by proteins called transcription factors, which are like the operations managers of gene regulation. These proteins stick to sections of DNA (typically close to the target gene) and recruit the polymerase enzyme to make an mRNA copy. In bacteria, transcription factors are rather like keys that fit the locks of unique binding sites on DNA. But that’s not how they work in complex organisms. In us, the logic of transcription factors is more difficult to parse.
For one thing, our transcription factors don’t show strong preferences for particular DNA binding sites. What’s more, they tend to work in pairs or groups. 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.
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. 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. In this case, regulation can be more responsive to nuances of context, and the regulatory knobs are tunable rather than being just on or off. “This is part of the beauty” of our regulatory complexity, Adelman said.
When they interact with the genome, transcription factors bind to pieces of DNA called enhancers—which present a puzzle of their own.
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. Seems simple enough, but mapping enhancers to their respective genes is far from straightforward. Our genome has hundreds of thousands, perhaps millions, of enhancers. That means we have many more of them than we have genes. Each gene might be influenced by many enhancers, and each enhancer might influence multiple genes.
“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.
Biologists do know that most enhancers won’t respond to a single transcription factor. Their activation “requires a cocktail,” Bickmore said. “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.”
Some enhancers are, as you’d expect, close to the genes they regulate, or even sit on DNA inside a gene. But others sit far away from the gene—perhaps millions of nucleotides away, with more genes in between.
The existence of such so-called “distal” enhancers “seems bonkers,” Bickmore said. “How do you get that information from over there to over here, to the gene that needs to be activated? That’s a largely unanswered question.”
One of the answers comes in the form of a loop.
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. The loops are created by a protein motor called cohesin, which runs up and down the DNA strand and extrudes it as needed.
Once cohesin has formed a loop to bring elements together, what then? It was once thought that they then stick together or assemble into a molecular machine, but they don’t. 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.
These transcription hubs are extremely fluid and differ from one cell to another. “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. 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.
Chromatin loops are just one reason why a gene’s transcription depends on the shape and structure of the chromatin around it.
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. But any time a cell is not actively dividing, its chromatin is unwound into what looks like a tangled mess. There is order to the chaos, however. Some parts of chromatin are densely packed into a form called heterochromatin. The compacted DNA there is relatively inaccessible to transcription factors; the genes it contains are typically silenced. Meanwhile, other parts are relatively loose, open, and accessible: This is called euchromatin.
There are special enzymes involved in packaging and repackaging chromatin, thereby controlling transcription. In other words, what matters is not just the encoded information in the DNA but also how it exists physically and dynamically in space. “We’ve stopped thinking about the genome as a linear piece of DNA code,” Bickmore said. “Thinking about this incredibly dynamic three-dimensional folding as absolutely inherent to regulation is a very exciting change.”
One aspect of this 3D organization is the clustering of segments of chromatin into compartments called topologically associating domains (TADs). Within a TAD, the genes seem to be coregulated: switched on or off in groups. Such groups keep suites of genes active or silent together to form and provide function in different cell types. 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.
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. 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. Epigenetic modifications to chromatin are like annotations of the DNA script that change its meaning in a given context. When cells divide, the epigenetic annotations are copied, too.
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. 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.
All of these processes and others can determine whether a gene gets transcribed into mRNA. But there are further layers of regulation that determine whether the mRNA is then translated into a corresponding protein—and which protein arises.
This post-transcriptional regulation is often controlled by RNA molecules that are said to be noncoding. These short-lived molecules aren’t templates for proteins, as mRNA is, but have other jobs of their own. 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. These noncoding RNAs are versatile, taking on varied roles in a cell. 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.
Small noncoding RNAs called microRNAs, for example, can silence mRNAs before they can be translated into proteins. They do this by guiding special enzymes to a particular mRNA to degrade or chemically modify it. 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.
Why make an mRNA only to stop it getting translated in a protein? This sort of post-transcriptional regulation is like having another checkpoint: Does the cell really need this protein? MicroRNAs can be mobilized to allow cells to adjust gene expression depending on the immediate context. In this way, the workings of the genome are less like a program’s inevitable progression and more like an adaptive and responsive process.
Another post-transcriptional complication is that mRNAs get translated to protein only after they have been reorganized. 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. (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.
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. Sometimes these different protein “isoforms” can have very different roles. 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.
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. Our cells are, in effect, making complex decisions about how to use their genes—both the information they contain and the structure they assume.
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. Then there are further layers of decisionmaking and action-taking in between mRNA and the final, functional protein.
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. 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. Not, then, a blueprint.
And for that reason, not at all easy to understand. “I wouldn’t have designed it this way if I was God,” Bickmore said. “But here we are!”
Why is gene regulation in animals like us so darned complicated? 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. Maybe so—but eukaryotic gene regulation isn’t just a messy version of what happens in bacteria. It has different principles, and there’s surely a reason for them.
Bickmore suspects that the complexity of regulation and of genome organization might have been the only means of generating complexity in the organism. For example, organisms with many tissue types and varied lifestyles required more control over which genes were on or off in a given cell. One thing this demanded was more and more noncoding regulatory sequences in DNA. But then they couldn’t all fit close to the gene itself.
“As you get more complexity, you need to add more and more enhancers,” Bickmore said. “But where are you going to put them? You start to put them farther and farther away. Once they are [far enough], you start to need TADs and three-dimensional [chromatin] folding to allow those things to work.”
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. These sequences have inserted themselves all over our chromosomes and are good at replicating themselves. 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.
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. It’s an open informational system that responds to external inputs and the genome’s dynamic internal conditions. This poses a challenge if AI relies solely on the genetic sequences within genomes to predict what genomes will do.
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. They’re content for the complexity described above to be in a black box, so long as the model generates accurate predictions. But will that work?
“I’m sure [AlphaGenome] is going to be useful, but with limitations,” Bickmore said. “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. And all that data is missing.”
Fundamentally, the challenge is that the genome is not a set of static, linear instructions. It is highly dynamic, and it uses its information contextually, with combinatorial and promiscuous logic. “Whether we’ll ever be able to capture that aspect” in algorithms like AlphaGenome, “I don’t know,” she said.
Yet the problem goes even deeper because the functioning of specific organisms, including each of us, doesn’t just depend on genomes. 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. The biologist Adrian Woolfson, cofounder of California-based biotech company Genyro, which aims to use AI systems for so-called “generative biology,” calls this information cloud the “informiome.”
“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. 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.
So how should we think about the genome? Maybe the only metaphors that can capture the way the genome really works must come from biology itself. 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.”
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. 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.”
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. There is no human technology that works this way, so metaphors such as blueprints, programs, or computers will always fall short.
Bickmore is optimistic that the workings of the genome are understandable, despite its complexity. “We’ve got a handle on it now,” she said. “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.
“McClintock was far more on point than people realized at the time,” Adelman said. “What she said was that the genome isn’t static—it’s living.”
Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
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