{"id":646230,"date":"2026-06-18T23:43:16","date_gmt":"2026-06-18T23:43:16","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/646230\/"},"modified":"2026-06-18T23:43:16","modified_gmt":"2026-06-18T23:43:16","slug":"why-the-human-genomes-tangled-physicality-may-confound-ai","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/646230\/","title":{"rendered":"Why the Human Genome\u2019s Tangled Physicality May Confound AI"},"content":{"rendered":"

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. \u201cWe\u2019ve stopped thinking about the genome as a linear piece of DNA code,\u201d Bickmore said. \u201cThinking about this incredibly dynamic three-dimensional folding as absolutely inherent to regulation is a very exciting change.\u201d<\/p>\n

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 \u2014 a dynamic process in which the chromatin is constantly rearranged in our cells.<\/p>\n

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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.<\/p>\n

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 \u201cepigenetic code\u201d governing this aspect of gene regulation, but it\u2019s far from clear if anything so systematic really exists.<\/p>\n

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 \u2014 and which protein arises.<\/p>\n

RNA Interventions<\/p>\n

This post-transcriptional regulation is often controlled by RNA molecules that are said to be noncoding. These short-lived molecules aren\u2019t 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 \u201ccoding genes\u201d), noncoding RNAs are transcribed from other DNA regions now generally described as noncoding genes. These noncoding RNAs are versatile<\/a>, 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.<\/p>\n

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\u2019t 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.<\/p>\n

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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<\/a>. In this way, the workings of the genome are less like a program\u2019s inevitable progression and more like an adaptive and responsive process.<\/p>\n

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\u2019t 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.<\/p>\n

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 \u201cisoforms\u201d 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<\/a> \u2014 some might activate gene expression, for instance, while others repress it.<\/p>\n

Checks and Balances<\/p>\n

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 \u2014 both the information they contain and the structure they assume.<\/p>\n

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 decision-making and action-taking in between mRNA and the final, functional protein.<\/p>\n

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Remember, too, that all the players \u2014 from transcription factors to noncoding RNAs \u2014 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 \u201ca strange loop<\/a>.\u201d 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.<\/p>\n

And for that reason, not at all easy to understand. \u201cI wouldn\u2019t have designed it this way if I was God,\u201d Bickmore said. \u201cBut here we are!\u201d<\/p>\n

Why is gene regulation in animals like us so darned complicated? One potential answer is that evolution doesn\u2019t have the foresight to design with efficiency and transparent logic, but merely tinkers with what it has already available. Maybe so \u2014 but eukaryotic gene regulation isn\u2019t just a messy version of what happens in bacteria. It has different principles, and there\u2019s surely a reason for them.<\/p>\n

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\u2019t all fit close to the gene itself.<\/p>\n

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\u201cAs you get more complexity, you need to add more and more enhancers,\u201d Bickmore said. \u201cBut 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.\u201d<\/p>\n

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<\/a>. 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\u2019t translating RNAs they don\u2019t really need or that could be actively harmful.<\/p>\n

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\u2019s an open informational system that responds to external inputs and the genome\u2019s dynamic internal conditions. This poses a challenge if AI relies solely on the genetic sequences within genomes to predict what genomes will do.<\/p>\n

\u201cA Highly Sensitive Organ\u201d<\/p>\n

Researchers developing AI-based genomic foundation models such as AlphaGenome hope that all these layers of regulation \u2014 transcription factors, splicing, epigenetic marks, loops, chromatin packing, and so on \u2014 will be implicitly included in the correlations that the algorithms learn between genetic sequence and organismal traits. They\u2019re content for the complexity described above to be in a black box, so long as the model generates accurate predictions. But will that work?<\/p>\n

\u201cI\u2019m sure [AlphaGenome] is going to be useful, but with limitations,\u201d Bickmore said. \u201cTo me the big gap is in the complexity of the human body \u2014 in all the cell types and how they change over time in development. And all that data is missing.\u201d<\/p>\n

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. \u201cWhether we\u2019ll ever be able to capture that aspect\u201d in algorithms like AlphaGenome, \u201cI don\u2019t know,\u201d she said.<\/p>\n

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Yet the problem goes even deeper because the functioning of specific organisms, including each of us, doesn\u2019t just depend on genomes. Other factors, such as diet, environment, microbiome and, for us at least, culture, can matter hugely, too \u2014 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<\/a>, co-founder of California-based biotech company Genyro, which aims to use AI systems for so-called \u201cgenerative biology,\u201d calls this information cloud the \u201cinformiome.\u201d<\/p>\n

\u201cWhile the human genome forms the foundation of the human informiome, other layers of extra-genetic information are equally important,\u201d Woolfson wrote in his book On the Future of Species<\/a>, published in April 2026. Genomic foundation models won\u2019t 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.<\/p>\n

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<\/a> compared the genome to \u201can exquisitely sensitive reactive system.\u201d Rather than a sequence of genes leading to the formation of traits, she said, it\u2019s more of \u201ca device for regulating the production of specific proteins in response to constantly changing signals it receives from its environment.\u201d<\/p>\n

That sounds close to the picture painted by the geneticist Barbara McClintock in the address she delivered<\/a> upon being awarded the 1983 Nobel Prize in Physiology or Medicine for her discovery of transposons. The genome, she declared, is \u201ca 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.\u201d<\/p>\n

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.<\/p>\n

Bickmore is optimistic that the workings of the genome are understandable, despite its complexity. \u201cWe\u2019ve got a handle on it now,\u201d she said. \u201cWe might not know the details, but I think the whole field is coalescing now into a framework where we\u2019re thinking along similar lines.\u201d AI can surely help with this sense-making, but in the end, human reasoning will be needed to discern the fundamental principles.<\/p>\n

\u201cMcClintock was far more on point than people realized at the time,\u201d Adelman said. \u201cWhat she said was that the genome isn\u2019t static \u2014 it\u2019s living.\u201d<\/p>\n

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