The only path to significant longevity is through the disciplined approach of untangling biology's complexity and leveraging the knowledge that lies within in order to design optimized gene expression and molecular alterations. No other short-term hack or even a lengthy list of band-aid interventions is going to do much beyond adding a few years or decades to ponder our demise. A few more years would be nice but why settle :)

There are numerous references to "hacking the genome" in the pop press and blogs however the term "hack" often indicates a short-term patch to get around a temporal issue whereas a real hacker gets inside the machinery and learns it from the inside out. He learns the inner workings and finds what threads can be pulled in order to make adjustments. These threads lie within biology's hierarchical network modules and understanding these will be key to building the future simulations that will drive personalized medicine.

A good introduction article to biological networks can be found in R. Albert, Z. Oltvai, A.-L. Barabási's
nature article. Other works from the author can be found at their website.

I'll be taking a Systems Biology course this fall and working my way through several books and papers on the topic in the coming year. The hope is to build some prototype applications along the way as putting these networks into motion is at the heart of the quest for the grail. All hope springs eternal from these simulations. So if the blog posts seem sparse its because I have my head buried in one of the following books or various papers I've accumulated on systems biology over the past year.

Aging and Cancer are intricately linked. I'm tired and probably will not be able to summarize this in any meaningful manner so I figured I'd post a summary of the chapter from Jörg Tost's Epigenetics for now...

Aging is the main risk factor associated with cancer development. The accumulation of molecular lesions in cells from mature organisms during the aging proccess is perhaps the fact that drives cells to transformation. Molecular lessions can be of genetic or epigenetic nature. Cell epigenetics, in particular DNA methylation and histone modification, becomes altered in aging and cancer. Global hypomethylation and CpG island hypermethylation occur progressively during aging and lead to cell transformation. In particular, the Werner syndrome gene (WRN) promoter and lamin A/C promoter become hypermethylated during the human neoplastic process shedding light on the tight connection between aging and cancer with epigenetics as a link. Cellular modifications that control the length of telomeres and enzymes of the NAD+ dependant deacetylase family (sirtuins) show a progression in cellular aging that is totally reverted during cellular transformation. Here we explore the physiological significance of epigenetic modifications during cellular aging and transformation.

So this is where the grail search has lead me. The bad news is that there is so little known about the regulation of the epigenetic landscape. The good news is that several billions of research dollars for cancer are increasingly targeting this space. Perhaps this research can also be leveraged for aging too.

The RIH for Aging suggests that chromosomal instability produces disturbances in gene expression with age by altering the relationship between genes and regulatory elements and by disrupting the chromatin structure (Vijg, 2007). The best example of this is the many cancers that are increasingly being attributed epigenetic disturbances. In fact, the refinements in the definition of cancer is migrating directly towards being a result of damage to or dysregulation of the epigenetic landscape(source needed). For aging in general though, finding telltale gene expression changes as a smoking gun has been fairly elusive. If stochastic disruption is occurring within the regulatory machinery shouldn't this lead to myriad of changes in gene expression profiles between young and old organisms? A handful of studies have attempted to discern whether this is the case. A study involving cardiomyocytes showed a distinct disruption in gene expression levels with age (Bahar et al., 2006) whereas little or no gene expression changes were detected in stem cells, granulocytes, and naïve B and T cells in a more recent study (Warren, et al., 2007). This, as the authors in the second study suggest, potentially demonstrates that the RIH is not necessarily universal across all cell types. Perhaps some cell types have negligible senescence while others do not?

More gene expression studies across the 200 or so cell types are needed to flush this question out as it's the key sticking point in determining whether we can intervene in the rate of aging within the epigenetic regulatory mechanisms, thus prior to accumulation of damage to downstream cellular functions.

Complexity is our biggest challenge in longevity research. Today we lack the tools to predict much at the scales and speeds that biology operates in any appreciable fashion. The slow loss of cellular fidelity that occurs with age is a deeply rooted in the DNA, RNA and regulatory processes that govern subcellular life and it's unlikely that any current technological interventions based on our current biological knowledge are going to have much impact in slowing or reversing this collapsing machinery. The notion that there might be a silver bullet or two and that we might stumble upon these anytime soon is looking fairly dismal.

When you hit a wall, you engineer new tools to scale it. We now have the means to build the computational tools to overcome biology's complexity. The chart below attempts to demonstrate how an emphasis on the tools of biotechnology and bioinformatics might yield significant returns in longevity research and why it should be at the top of the list for long-term strategic initiatives in fighting off the diseases of aging.

The concept is pretty straight forward and summarized in 3 exponential curves that would probably occur a 2-3 decades after one another.

First Curve - Technological Development

New hardware and software tools designed specifically for engineering and building predictive models at femptosecond and nano scales will unleash a wealth of working knowledge as to how biology makes decisions within its complex network of regulatory networks. While no human will necessarily be able to comprehend this information, genetic algorithms trained on these data sets will start yielding exponential growth in our second curve.

Here is the in-memory source code we must reverse engineer in order to significantly upregulate lifespan:

• Epigenetic Code

Also referred to as:

• The Histone Code

...though histones are just a subset of the epigenetic landscape.

I'm hella tired from reading this book on Epigenetics for 14 hours straight. Aging, cancer, RNA, transcription, regulation. It all makes sense now but now I must sleep...

Note to self: Ponder whether the plateau in mortality curves is the afterglow of RNA's clever strategy of hiding information from entropy.

After mentioning that I had my genome screened through 23andMe to a friend last night a lengthy discussion ensued about WTF I would spend so much money on something like this which in turn lead to discussion about my obsession with researching technology x information x biology x longevity. The tribunal eventually lead to genuine interest and rather than the fireworks booming in the sky I saw things popping my friend's head. At one point he asked me what I thought the next century's biggest technological breakthrough would be for medicine, particularly longevity.

Lots of ideas swirled through my head such as stem cell and genetic therapy, artificial organs, high-throughput lab devices such as DNA sequencing microarrays, ultra-fine resolution imaging, proton beams for cancer treatment, nanotechnology, surgical instruments, swallowable cameras, implantable biosensors, etc. I babbled on about some of these briefly and where they currently stood but he wanted me to be more specific. What would be a killer product? The question was also reposed as "If you had $5 billion to invest in R&D of a single device, what you set out to build?" I pondered this for awhile because there are so many medical devices and dx tools that would add value to medicine. I mostly thought about specific rejuvenational interventions but it was difficult to coalesce my imagination around a single device per se as there are so many challenges that need to be dealt with in regards to aging that none seemed worthy of such a lofty title as being "the greatest."

Interesting.

The Elsevier Grand Challenge: Knowledge Enhancement in the Life Sciences is a contest created to improve the way scientific information is communicated and used. The contest invites members of the scientific community to describe and prototype a tool to improve the interpretation and identification of meaning in (online) journals and text databases relating to the life sciences. Specifically we are looking for new ways to:

• 1. improve the process/methods/results of creating, reviewing and editing scientific content
• 2. interpret, visualize or connect the knowledge more effectively, and/or
• 3. provide tools/ideas for measuring the impact of these improvements.

Abstracts are now invited. Submissions will close on July 15th, 2008.

Why do I find out about these things too late. Abstract done on the 14th! My research paper is due on the 14th. And the aformentioned systems biology conference is on that day too. Are all of these really doomesday signs for July 14th mysteriously channeling themselves through me in the oddest of ways? Me going after this is probably not going to happen but I could use the $35k to fund the grailsearch so perhaps it's worth an all-nighter to hack an abstract together and come what may.

Just what I need though, another summer project :)

Mark your calendar for July 14th, 2008. Bioclues and Bioinformatics.org is hosting an online conference for systems biology for bioinformatics. This is the second conference. The last was held in November '07.

California recently sent cease and desist letters to 13 companies that do personal genome screenings. Are they concerned about people or are they being pressured by doctors and the pharma lobby?

Read the top ten reasons they should not interfere with this nascent market here

Gene expression data are the biomarkers of aging. A nice database of gene expression and regulation data by tissue type was just put online by the Bioinformatics Lab at Wilmer Eye Institute of John Hopkins University. The DB can be found at the TiGER website.

Lolcat says...

Boo

You don't have to be a disciple of Euclid to know that the quickest route between two points is a straight line. Thus is the case with radical life extension. Unfortunately, that straight line runs directly through a tangle of biological complexity which scares a lot of people. When I say scare, I don't mean "Ooh, yikes, a spider," I mean horrifying night terror type scare where you wake up and can't even scream.
It's disappointing but even bright scientists who spend their careers trying to bring sane order to a chaotic system will usually yield to biology's seemingly unending network of baffling mazes. Intractable, impossible, never; these are terms used to describe the notion that we will one day gain mastery over biological function, particularly in humans. Who can blame them though; they have the data on their side after all. For centuries we've been poking and prodding life forms and performing all sorts of alchemy on these poor furry critters with pithy results. We have found that it's quite easy to shorten an organism’s lifespan but until only very recently, have we actually been capable of extending one and only modestly so.
For this reason few will even consider looking at whether reverse engineering the biological aging process is feasible thus it's high time to start dispelling the myth that it's too complex.

Breaking it Down

I won't go into systems theory and argue that biology is actually a highly ordered and well organized rules-based system with segregated functional domains that can be modeled, but will rather frame the debate from the perspective of the ongoing revolution within the field of genomics.

John Lennon's lyrics have always been an inspiration to me but what if we changed that one line. What if, instead we sacrificed today for the chance at two tomorrows?

Imagine: A network of engineers, scientists, philanthropists and volunteers directing their time, money and research at aging therapies in the most creative means possible. A powerful volunteer multi-disciplined network leveraging all related fields and their advancements to the fullest extent.

The major challenge with building this type of network is that few, if any, particularly those with the skills to lead and engineer such feats, are willing to make the personal sacrifice to dedicate their spare time to an effort that most would agree, has very little, if any chance of succeeding, at least in our lifetime. Who is willing to forgo career advancement, children, a spouse, hobbies and enriched lifestyle that can be gleaned from "the machine" for a very small shot at longer lifespans? Interestingly, the more people that make the decision to make the sacrifices, the higher the probability of it's success. But it's not a prospect that is going to have mass appeal in the nearterm so pop that bubble and forget about it. So what is Plan B then?

...Know when to Fold It. *sigh*, you know the creative juices just aren't flowin' when you're quoting Kenny Roger's music from several decades past.

The protein folding game is going public though and that's great news. The computer game Fold It, where you muck around with strands of amino acids in an attempt to find the best possible protein conformation, is getting more coverage around the net. A few weeks ago it was featured on Slashdot and now there's an article over at Technology Review. A snippet...

Luis von Ahn, a computer scientist at Carnegie Mellon University, agrees that humans bring problem-solving skills to the protein-folding game that computers can't match. "The computer does a brute-force search, where we may know the shortcut," he says. "We live in a 3-D world; we know how to navigate space." Von Ahn has designed games that get people to help label images for Google and digitize books. Computers are bad at some tasks that are trivial for humans, such as recognizing a dog in a photograph or reading a blurry word.

While Fold It might be a bit too challenging to reach escape velocity, the concept of human-machine combined computation for problem solving is quite spectacular. If we can merge the power of silicon and grid computing with the power of millions of human imaginations, we might just be able to have a supercomputer of future centuries in this century, perhaps in the next decade or so granted we can get our shite together and coordinate such an effort.

The question is, what are the grand challenges we need to apply this to? Is it predicting protein structure? Curating the sequence and protein databases? Synthetic molecular engineering? Untangling the proteins networks? Trial and error designs of molecular therapies?

If only... :)

image courtesy bifsniff.com

Leroy Hood explains how systems biology will impact medicine and some of the challenges of taking SB from single cell to multi-cellular organisms in MIT's Technology Review article Biology's Next Breakthroughs.

Leroy states:

One of the fundamental questions in doing single-cell studies is whether each cell is utterly individually unique--whether, whatever measurements you take, each will be uniquely different from one another. Or, in fact, whether the cells do fall into discrete populations, discrete states. My own firm conviction is, when we learn how to do these studies properly, there will be discrete states we can look at. Knowing those states, and then reconstituting them to see how populations work--that's going to give us deep insights into developmental, physiologic, and disease mechanisms. If, on the other hand, there aren't discrete states, if there is a continuous distribution of variability, that will represent an interesting challenge.

I tend to agree with him. All of the organ systems, cells and subcellular components are a network of interconnected state machines working in harmony and generally having similar states for populations of like cells. These state machines can be computationally simulated without having to reproduce every protein or atomic interaction. Between the trend towards affordable cluster-centric supercomputing and the rapid growth of knowledge in the genome and metabolic pathway space, the two are on a collision course where computational biology will emerge as driving force not only in longevity research but all medical research. The bad news is that this is a slow and methodical puzzle building process that will take decades. The best news for future generations and perhaps even some of us is that predictive and personalized medicine will reduce suffering and extend lives with extreme efficacy.