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 metobolic 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 good news it will happen and that it is already showing very promising results despite the limited datasets and computing resources at our disposal.

Despite the fact that I've sworn to stay focused on my schoolwork rather than blog, this one was just too good to pass up as it's an initiative that I have previously opined would add a significant amount of value to aging research.

Researchers Mark Vidal and Albert-Laszlo Barabasi have published a "diseasome" map that shows the relationships between disease phenotype and genotype thus showing the common genes across the wide array of diseases.

The importance of models such as this in regards to aging is the ability to tease apart the subtle and low level changes that occur over time. If aging can be viewed as a single phenotype where many endpoints are the results of these changes then it's likely that a good portion of the diseasome will be a subset of a map of aging damage throughout the genome/proteome/metabalome. One could simply reorganize the diseasome a bit and draw a circle around the diseases typically characterized as aging related to illustrate how much actually is aging related. My guess is that it would be a significant percentage.

The full text of the PNAS article is available online as is a more fluffy New York Times Article.

Quick Note: The Methuselah Foundation is hosting a two-day conference Understanding Aging: Biomedical and Bioengineering Approaches. The conf is broken down into four sessions:

• Session 1A/1B: DNA damage, telomeres, cancer
• Session 2A/2B: the cell niche
• Session 3A/3B: accumulation of damaged molecules
• Session 4A/4B: tissue engineering and regenerative medicine

In the past, videos from the SENS conferences have been posted online so if you can't attend, keep an eye on the site post conference.

If pathways are the networks of roads that lead to aging then DNA damage are the Roman Forum.

If I recall, think the Roman goddess of fate was Fortuna, as in "as fortune would have it." So, as Fortuna would have it I was assigned to a project group within my bioinformatics course to study DNA damage and repair mechanisms for a good part of the semester. Given my fascination with biological complexity, the grailquest and stubborn insistence that systems biology is the path to practical immortality, I'm sure you can imagine my disappointment. Seriously, if I could have described a more optimal topic to work on I think the only slightly better project would be the more specific "mitochondrial" DNA damage & repair. Only two weeks into the course and I've already gotten to cite Jan Vijg's Aging of the Genome! Woo-f'n-hoo. I'm so thrilled about this I think I'll down a couple of glasses of wine, toast Fortuna and hack up some goblins tonight in celebration rather than diving into the work . What else might a good Roman soldier do given a blade and some goblins :)

The IGF1 Conference, or so I've dubbed it as it seems to be one of the main topics at the Metabolic Pathways of Longevity Conference, is taking place at the end of March of '08. There's lots of other metabolic goodness at IGF1 with talks on growth hormone receptors, TOR pathways, SIRTs, DMP53s, mito function, calorie restriction and my favorite receptor: the death receptor. Any signally pathway that sounds like a Duke Nukem weapon is going to win points with me.

You can tell a lot as to where a field is simply by looking at the conference agendas and googling for the recent papers of the speakers. From the agenda of this conference it's fairly clear that researchers are zeroing in (or at least stumbling around) the various metabolic pathways that regulate the aging process in simpler organisms and probably humans. One particular area that is increasing showing more evidence as a contributor to the aging process is epigenetic changes that modulate gene regulations. David Sinclair has a talk on Evidence that Chromatin Reorganization is a Driver of Genomic Instability and Aging in Mammals which looks interesting for several reasons but particularly due to the fact that his company has raised a large amount of venture cash and has been investing heavily in research of the metabolic pathways. Has Sinclair and other researchers linked the previously mentioned pathways to epigenetic changes? Is this the heart of the aging process? Alternatively stated: Is it possible that downstream aging damage that slowly accumulates over decades and is characterized by dozens if not hundreds of different disease endpoints, has as its primary root cause, slight and subtle regulatory dysfunction that can be modulated by the upstream metabolic pathways that these researchers are dancing around?

So the quest for the grail lead me back to work on an MSc in order to round out my education with some formal training. I just completed my first 15 credit Biology for Computer Scientists module and start a Bioinformatics module tomorrow. The next two modules after that are Systems Biology and Microarrays assuming I survive this next one.

Going back to school was definitely the right decision though I'm not certain that it's helping my goals of slowing the aging process by any means :O. Pulling all nighters for undergrad work was fine 15 years ago but dragging my ass out of bed and slogging code or writing up design docs for a fast-moving software team after entire weekends, late nights and early morning work can wear one down quickly. Staying in shape mentally and physically combined with precise time optimization have been escrucially important to say the least.

The first module was much more challenging that I had anticipated. I had been studying many aspects of biology, biochem and gerontology for the prior 3 years on my own and felt that I had a fairly good grasp of the most of the key concepts but this course threw me in much deeper than simply getting the gist of things. This was a pleasant surprise as I went into this first intro module with fairly low expectations thinking that I would breeze through it.

For those of you who've played Tetris, I needn't explain. For the rest of you, if you play this game long enough, your brain works on tetris subconsiously. You wake up in the middle of the night and SEE BLOCKS FALLING INTO PLACE!!! You drive to work and you start daydreaming about L's and S's fitting neatly into their respective slots. Ahhh, you think you're losing your mind but fortunately you're just running some backround 'cron' jobs trying to imagine how to survive your next encounter with this game in the wild, that is, when you get back to playing it. This is known as, drumroll, the Tetris Effect.

It hasn't quite happened for me yet while playing the protein folding game Fold It, but I'm wondering that if I play long enough, I'll start to see protein conformations in my sleep. Perhaps from their I can look at a series of ATCG's and see it's 3D fold. Maybe not, but who knows, the brain is quite the machine.

Developing...

The Institute for Systems Biology and the University of Washington College of Engineering will be having the 7th annual symposium on April 20-21. There will be 3 sessions:

Session 1: Biological Imaging
Session 2: Single Cell & Single Cell Experimentation
Session 3: Synthetic Biology

There is no mention of aging anywhere but the topics look fairly interesting. One of the key goals here at grailsearch.org are to look for new and innovative ideas across the breadth of scientific disciplines that can significantly accelerate our ability to engineer aging interventions. The underlying concepts of Systems Biology lie at the core of this strategy as we will likely need to bridge many technologies and disciplines to pull off what seems like an impossible engineering task.

At the 2005 Foundations of Systems Biology In Engineering, Leroy Hood posed these questions as the computational/mathematical challenges in systems biology:

1. How to fully decipher the (digital) information content of the genome
2. How to do all-vs-all comparisons of 1000s of genomes
3. How to extract protein and gene regulatory networks from 1 & 2
4. How to integrate multiple high-throughout data types dependably
5. How to visualize & explore large-scale, multi-dimensional data
6. How to convert static network maps into dynamic mathematical models
7. How to predict protein function ab initio
8. How to identify signatures for cellular states (e.g. healthy vs. diseased)
9. How to build hierarchical models across multiple scales of time & space
10. How to reduce complex multi-dimensional models to underlying principles
11. Text searching to integrate data and literature

The golden age of biology is upon us. We have broken through what previously seemed like an impenetrable wall of complexity, size and scale by decoding the human genome and are now building the next generation of tools to tackle the subsequent set of challenges. The most significant of these hurdles is that of aging. It also happens to be humanity's most important problem to tackle as most human suffering stems from this unfortunate and unnecessary process.

The best tool we have for understanding the complex biological networks of aging is computational theory, particularly machine learning and its application to Systems Biology. Computational horsepower via high-performance computing, machine learning algorithms and biological data are all reaching a point where the intersection of these will soon allow us to use computational systems approaches for developing predictive models that precisely illustrate how we can tweak biological networks to best affect the dreaded aging process.

As part of the Pascal Lecture Series, Machine Learning in Systems Biology (MLBS) 2007 was held at the University of Evry in France to promote and discuss machine learning techniques in computational biology. The lectures are available for viewing on the videolectures.net website. They present various techniques as to how machine learning methods can be used to help us understand the various biological networks with limited data sets. The networks discussed are:

• Metabolic
• Protein-Protein
• Interaction
• Genetic Transcriptional/Regulation Networks

As breakthroughs in micro and nanotechnology increasingly swamps us with a deluge of data from these networks the challenge is to develop analytical methods for deciphering this flood of data. These lectures demonstrate the types of techniques we can use in the young but rapidly growing field of Systems Biology.

While working on a bioinformatics project for school I was sifting through databases and papers about databases in the seemingly impossible task of finding a blood disease from a few small peptide fragment when I stumbled across a fun little article that baited me into reading it. Unfortunately this happens all too often which results in my schoolwork taking 2-3x longer than it should! What sparked me into blogging about this prehistoric article, yeah, it was written in 1997 - the dark ages of informatics I'm sure, was this little snippet-o-joy:

In cellular immunology, for instance, there are about 100 billion peptide sequences to which the immune system can respond, each targeted by a small set of white blood cells, or T lymphocytes—as many types of T cells in one human being as there are stars in our Galaxy.

Just a 100 billion eh? These insane types of numbers constantly whack us up-and-coming bioinformaticians in the head like the proverbial cartoon rake-to-forehead smackaroo. You get to the point that you simply shrug your shoulders, tell yourself there will be an indexed database for that dataset someday, and move on.

But will there be a comprehensive set of biological databases in our lifetime? It's great that all these disparate databases are emerging however science and biotech markets are not organized around assembling the big picture such that all these DBs are cross-indexed in any meaningful and comprehensive manner. To really do this requires this being a primary research goal whereas most research efforts are simply focused on one very small task at hand with a limited research budget. The result is thousands of nonstandard databases and datasets being published all over the net with nobody really synthesizing the data.

Epigenomic Stability relies on a complex network of gene expression networks working in tandem. Controlling all of these networks are regulatory genes who are the real wizard behind the curtains. These regulatory genes are playing a complex game of economics following supply and demand rules of engagement. If the steady state concentrations of proteins shift in one direction, the regulators jump in and trigger a cascade that will bring balance to the force by either triggering promoters or ubiquitins to jump into action. One of the more convincing theories of aging (to myself at least) is that when this network of epigenomic stability is disturbed beyond it's ability to restore itself, cells lose their robustness required for homeostasis and start to show signs of aging in a multitude of ways.

Ouroboros covers the recent news where mutations in a particular pathway (kinase SCH9) of yeast, combined with intervention in the RAS and TOR genetic pathways can extend lifespan significantly thus reinforcing the notion that there is an important connection between epigenomic stability and longevity.

If this instability does indeed play a major role in human aging as well, it would be welcome news as the up and coming RNAi/antisense type therapeutics would allow us to specifically target the regulatory genes or their downstream products and modulate them. A much more sophisticated understanding of these regulatory genes and the networks involved in human aging is required before we can have much of an impact here but that's where network modeling, informatics and system's biology can play a key role.

Slashdotters are discussing the Department of Engery's INCITE project where they award mega supercomputing hours to selected projects. This year there are 55 projects selected that will use 265 million computing hours, triple last year's award. Unfortunately only 3 of the 55 Projects help us on our quest for the grail of infinite lifespan. The rest are all subatomic physics, climate simulations and other nonbiological projects. While some of these projects will eventually trickle up to biology, the ones that have the most impact are those excercising biology's moving parts. The life science projects from those awarded are:

• Computational Protein Structure Prediction and Protein Design
• Large-scale simulations of cardiac electrical activity
• Simulation and modeling of synuclein-based 'protofibril structures' as a means of understanding the molecular basis of Parkinson's Disease

All good but it'd be nice to see a larger percentage of biology projects considering how much ill health costs our society in terms of suffering and lost productivity. When you observe the long-fought war on cancer and other aging diseases one has to wonder whether we're really just waiting around for biotechnology and supercomputing to reach an inflection point such that we can brute force our way through the biological complexity. This would seem to be the case as most therapeutic interventions thrown at aging diseases don't make it through trials leading researcher's back to the drawing board more often that not.

There are many genes that change expression levels as we age. Teasing apart the network of these changes and the protein interactions between them can help us identify the underlying genes and pathways will likely yield potential targets for age reversing drug and hormone therapies. The paper A modular network model of aging by Xue et. al doesn't really offer any new groundbreaking conclusions but does offer supporting evidence that :

• Gene regulatory networks are an ugly tangled mess of interactions with a few key hub gene clusters that seem to play a key role in maintaining homeostasis and network stability.
• As the authors suggest, these hubs, and environmental factors that affect them, might help explain the randomness we see in aging.
• The most appropriate explanation of aging is probably more along the lines of a Network Theory of Aging rather than any particular programmed mechanism, specific environmental factor or damage type.

With a few exceptions, these three points seem to be a common theme in many of the systems biology and gene expression network analysis papers I've read recently. Disturbingly, the gene expression and network patterns seen don't seem to carry over from species to species which suggests that untangling a protein interaction network and meta-analysis gene expression studies in model organisms may offer little value for similar studies in humans, other than the obvious ability to leverage the techniques and tools used to perform the studies. Despite this, the paper is still a worthy read as it illustrates some of the analytical tools and methodologies used to tease apart some of the complex data sets emerging from the high-throughput lab tools.

Databases are usually quite dull. They are filled with tedious gobs of data like shoe size, date of birth and other boring tidbits that are meaningless on their own. Having to work with them on a day to day basis can sometimes makes you want to bore a hole in your head with a spoon. The AGEMAP database kicks ass though and I'll tell you why.

In the recent PLoS article entitled AGEMAP: A Gene Expression Database for Aging in Mice by Zahn, Poosala, et. al. the authors describe how they ginsu-ed some mice and built a gene expression database of about 8,900 genes from 16 tissues at four age intervals. In doing so they created one of the most extensive aging gene expression studies to date and probably the most important of aging datasets yet to surface.

While no clear genes resulted as part of a specific aging process, some rather profound data points have emerged from the study:

Kick-Ass Reason #1.
906 gene expressions were shown to change with age across the various mouse tissues. Who cares? You do. It sets the stage for similar human studies and understanding why gene expressions change will lead to therapies that will very likely extend your healthy lifespan in the coming decades. Significantly.

Kick-Ass Reason #2.
Mice and Humans do not appear to have common age-related changes in gene expression, thus reinforcing the hypothesis that the aging process is not a result of evolutionary pressure.

It's Here! Fold It - The Protein Folding Game. At least in beta format. Watch the video here.

Getting going is a bit of a challenge. For puzzle 10 I was able to get my score to 9274 by doing a lot of wiggling and shaking of my backbone but couldn't go any higher. It seems that the fastest way to maximize your score is by getting to a decent protein conformation by dragging the backbone around until you have a decent cluster and then shaking and wiggling your way to a marginally better formation with the shake and wiggle tools. Good thing for these as the manual changes are a bit tedious and to try all the combinations that the wiggle tool does would take months! It can be a bit discouraging when you go to make manual changes as just about every change you try to make lowers your score drastically.

All in all, not bad for a first cut at a beta. Finding ways to make the game entertaining will be the team's biggest challenge as most computer games can lead you through the game. Whereas here, the answer to the best protein conformation is unknown and you'll never know if you're right, even if you have the top score!