Surprise, Human Genome Didn’t Solve All the Mysteries: Life is Complicated and Evolution Fails Yet Again

Here is a Nature News Feature that speaks volumes about the state of evolutionary theory. It explains how the Human Genome project and high throughput technologies have revealed levels of complexity evolutionists hadn’t even dreamed of. It is yet another monumental failure of evolutionary theory, even though we all know evolution is a fact.

Not that long ago, biology was considered by many to be a simple science, a pursuit of expedition, observation and experimentation. At the dawn of the twentieth century, while Albert Einstein and Max Planck were writing mathematical equations that distilled the fundamental physics of the Universe, a biologist was winning the Nobel prize for describing how to make dogs drool on command.

And yet life scientists such as Joseph LeConte were absolutely certain evolution was true. They did not have the foggiest notion of how the biological world could have arisen on its own. In fact they didn’t understand much about the biological world period. But that never got in the way of their certainty.

The molecular revolution that dawned with the discovery of the structure of DNA in 1953 changed all that, making biology more quantitative and respectable, and promising to unravel the mysteries behind everything from evolution to disease origins. The human genome sequence, drafted ten years ago, promised to go even further, helping scientists trace ancestry, decipher the marks of evolution and find the molecular underpinnings of disease, guiding the way to more accurate diagnosis and targeted, personalized treatments. The genome promised to lay bare the blueprint of human biology.

Few predicted, for example, that sequencing the genome would undermine the primacy of genes by unveiling whole new classes of elements — sequences that make RNA or have a regulatory role without coding for proteins. 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.

Translation: Evolution’s simplistic, gene-centric, just-add-water view of biology led researchers to believe that sequencing the entire genome would lead to great breakthroughs. That naïve view became yet another failed evolutionary expectation.

Instead, as sequencing and other new technologies spew forth data, the complexity of biology has seemed to grow by orders of magnitude. Delving into it has been like zooming into a Mandelbrot set — a space that is determined by a simple equation, but that reveals ever more intricate patterns as one peers closer at its boundary.

With the ability to access or assay almost any bit of information, biologists are now struggling with a very big question: can one ever truly know an organism — or even a cell, an organelle or a molecular pathway — down to the finest level of detail?

I once debated an evolutionist who said the purpose of science is to explain, and that evolution explains biology. False. As usual it is the exact opposite. Evolution does not explain biology. Evolution is constantly surprised by scientific findings.

"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."

Isn’t evolution amazing?

"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."

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.

In the past few years the story of regulation has become profoundly more complex than evolutionists ever imagined. Needless to say, there is no credible, scientific, explanation for how it all evolved.

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.

Indeed. It is also “incredibly naïve” to insist we know how such complexity arose.

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 reported3 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.

Of course beyond just-so stories, evolutionists have no idea how such complexity evolved. And yet p53 is just one example of biology’s complexity that defies evolution.

And reading further it is good to see a point of agreement:

The p53 story is just one example of how biologists' understanding has been reshaped, thanks to genomic-era technologies. Knowing the sequence of p53 allows computational biologists to search the genome for sequences where the protein might bind, or to predict positions where other proteins or chemical modifications might attach to the protein. That has expanded the universe of known protein interactions — and has dismantled old ideas about signalling 'pathways', in which proteins such as p53 would trigger a defined set of downstream consequences.

"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."

Indeed, those networks of information are “infinitely more complex” than expectations. That is hardly surprising given that those expectations came from evolutionary theory. What the data are revealing is nothing like what evolution expected.

But even with the deluge of data being provided by high-throughput technologies and the power of super computers will not easily solve biology’s infinite complexity:

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.

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."

A certain amount of naivety? But if all of biology arose from those random mutations and the like, shouldn’t biology be easy to understand?

Some, such as Hiroaki Kitano, a systems biologist at the Systems Biology Institute in Tokyo, point out that systems seem to grow more complex only because we continue to learn about them. "Biology is a defined system," he says, "and in time, we will have a fairly good understanding of what the system is about."

So biology’s complexity is just an illusion of too much knowledge. Once we figure all this out we’ll see how simple it actually is. Sounds like another prediction of evolution. Will it follow the trend of its failed predecessors?

Mina Bissell, a cancer researcher at the Lawrence Berkeley National Laboratory in California, says that during the Human Genome Project, she was driven to despair by predictions that all the mysteries would be solved. "Famous people would get up and say, 'We will understand everything after this'," she says. "Biology is complex, and that is part of its beauty." She need not worry, however; the beautiful patterns of biology's Mandelbrot-like intricacy show few signs of resolving.

Yet another failed prediction.

The notion that all of this evolved into existence is outrageous. It violates both simple common sense and detailed analysis, and makes mockery of science. When will taxpayers stop funding this religious drivel?