Non-coding DNA is crucial to biology, yet knowing that it is there hasn't made it any easier to understand what it does. "We fooled ourselves into thinking the genome was going to be a transparent blueprint, but it's not," says Mel Greaves, a cell biologist at the Institute of Cancer Research in Sutton, UK.
Instead, as sequencing and other new technologies spew forth data, the complexity of biology has seemed to grow by orders of magnitude.
Complexity growing by orders of magnitude? This is precisely what was not expected.
"It seems like we're climbing a mountain that keeps getting higher and higher," says Jennifer Doudna, a biochemist at the University of California, Berkeley. "The more we know, the more we realize there is to know."
Biologists have seen promises of simplicity before. The regulation of gene expression, for example, seemed more or less solved 50 years ago. In 1961, French biologists François Jacob and Jacques Monod proposed the idea that 'regulator' proteins bind to DNA to control the expression of genes. Five years later, American biochemist Walter Gilbert confirmed this model by discovering the lac repressor protein, which binds to DNA to control lactose metabolism in Escherichia coli bacteria. For the rest of the twentieth century, scientists expanded on the details of the model, but they were confident that they understood the basics. "The crux of regulation," says the 1997 genetics textbook Genes VI (Oxford Univ. Press), "is that a regulator gene codes for a regulator protein that controls transcription by binding to particular site(s) on DNA."
Indeed, for evolutionists all of biology is a fluke. It just happened to arise, so isn’t it simple?
Just one decade of post-genome biology has exploded that view. Biology's new glimpse at a universe of non-coding DNA — what used to be called 'junk' DNA — has been fascinating and befuddling. Researchers from an international collaborative project called the Encyclopedia of DNA Elements (ENCODE) showed that in a selected portion of the genome containing just a few per cent of protein-coding sequence, between 74% and 93% of DNA was transcribed into RNA.
Much non-coding DNA has a regulatory role; small RNAs of different varieties seem to control gene expression at the level of both DNA and RNA transcripts in ways that are still only beginning to become clear. "Just the sheer existence of these exotic regulators suggests that our understanding about the most basic things — such as how a cell turns on and off — is incredibly naive," says Joshua Plotkin, a mathematical biologist at the University of Pennsylvania in Philadelphia.
But wait, did not the non-coding DNA, like everything else, spontaneously arise by itself? How could we be so “incredibly naïve” about all of this?
Even for a single molecule, vast swathes of messy complexity arise. The protein p53, for example, was first discovered in 1979, and despite initially being misjudged as a cancer promoter, it soon gained notoriety as a tumour suppressor — a 'guardian of the genome' that stifles cancer growth by condemning genetically damaged cells to death. Few proteins have been studied more than p53, and it even commands its own meetings. Yet the p53 story has turned out to be immensely more complex than it seemed at first.
In 1990, several labs found that p53 binds directly to DNA to control transcription, supporting the traditional Jacob–Monod model of gene regulation. But as researchers broadened their understanding of gene regulation, they found more facets to p53. Just last year, Japanese researchers reported that p53 helps to process several varieties of small RNA that keep cell growth in check, revealing a mechanism by which the protein exerts its tumour-suppressing power.
Even before that, it was clear that p53 sat at the centre of a dynamic network of protein, chemical and genetic interactions. Researchers now know that p53 binds to thousands of sites in DNA, and some of these sites are thousands of base pairs away from any genes. It influences cell growth, death and structure and DNA repair. It also binds to numerous other proteins, which can modify its activity, and these protein–protein interactions can be tuned by the addition of chemical modifiers, such as phosphates and methyl groups. Through a process known as alternative splicing, p53 can take nine different forms, each of which has its own activities and chemical modifiers. Biologists are now realizing that p53 is also involved in processes beyond cancer, such as fertility and very early embryonic development. In fact, it seems wilfully ignorant to try to understand p53 on its own. Instead, biologists have shifted to studying the p53 network, as depicted in cartoons containing boxes, circles and arrows meant to symbolize its maze of interactions.
Did all of this spontanously arise via random biological change, such as caused by mutations?
"When we started out, the idea was that signalling pathways were fairly simple and linear," says Tony Pawson, a cell biologist at the University of Toronto in Ontario. "Now, we appreciate that the signalling information in cells is organized through networks of information rather than simple discrete pathways. It's infinitely more complex."
Evolution’s predictions routinely turn out to be false. Evolutionists are constantly surprised because their theory is always pointing in the wrong direction.
"In many cases you've got high-throughput projects going on, but much of the biology is still occurring on a small scale," says James Collins, a bioengineer at Boston University in Massachusetts. "We've made the mistake of equating the gathering of information with a corresponding increase in insight and understanding."
A new discipline — systems biology — was supposed to help scientists make sense of the complexity. The hope was that by cataloguing all the interactions in the p53 network, or in a cell, or between a group of cells, then plugging them into a computational model, biologists would glean insights about how biological systems behaved.
In the heady post-genome years, systems biologists started a long list of projects built on this strategy, attempting to model pieces of biology such as the yeast cell, E. coli, the liver and even the 'virtual human'. So far, all these attempts have run up against the same roadblock: there is no way to gather all the relevant data about each interaction included in the model.
In many cases, the models themselves quickly become so complex that they are unlikely to reveal insights about the system, degenerating instead into mazes of interactions that are simply exercises in cataloguing.
No way to gather all the relevant data about each interaction? But according to evolution the creative force behind biology is, well, nothing. So how could there be such mazes of interactions?
In retrospect, it was probably unrealistic to expect that charting out the biological interactions at a systems level would reveal systems-level properties, when many of the mechanisms and principles governing inter-and intracellular behaviour are still a mystery, says Leonid Kruglyak, a geneticist at Princeton University in New Jersey. He draws a comparison to physics: imagine building a particle accelerator such as the Large Hadron Collider without knowing anything about the underlying theories of quantum mechanics, quantum chromodynamics or relativity. "You would have all this stuff in your detector, and you would have no idea how to think about it, because it would involve processes that you didn't understand at all," says Kruglyak. "There is a certain amount of naivety to the idea that for any process — be it biology or weather prediction or anything else — you can simply take very large amounts of data and run a data-mining program and understand what is going on in a generic way."
From where did that “naivety” come, and will they learn from their mistakes? It seems not:
For example, transcription factors encoded in the urchin embryo's genome are first activated by maternal proteins. These embryonic factors, which are active for only a short time, trigger downstream transcription factors that interact in a positive feedback circuit to switch each other on permanently. Like the sea urchin, other organisms from fruitflies to humans organize development into 'modules' of genes, the interactions of which are largely isolated from one another, allowing evolution to tweak each module without compromising the integrity of the whole process. Development, in other words, follows similar rules in different species.
"The fundamental idea that the genomic regulatory system underlies all the events of development of the body plan, and that changes in it probably underlie the evolution of body plans, is a basic principle of biology that we didn't have before," says Davidson. That's a big step forwards from 1963, when Davidson started his first lab. Back then, he says, most theories of development were "manifestly useless".
In other words, the massive biological change change evolution brought about was based on the genomic regulatory system which, of course, evolution had just happened to have already created. That was fortunate.
This article is worth revisiting because the problem with all of this is not merely that mistakes were made. Nor is there anything wrong with a theory that doesn’t work out. This happens all the time in science. The problem is that evolution is driven by theological and philosophical convictions that won’t be overturned by scientific evidence. Religion drives science, and it matters.