Bioinformatics for Systems Biology - Editor Krawetz, Stephen, 2nd Edition (2009)

With continued informatic and biological cross-fertilization, advancements in Systems Biology will revolutionize personalized medicine answering questions by integrating information in unexpected ways.

Table of Contents

Part I - Life of a Cell and Its Analysis

- Structure and Function of the Nucleus and Cell Organelles
- Transcription and the Control of Gene Expression
- RNA Processing and Translation
- DNA Replication, Recombination, and Repair
- Cell signalling
- Epigenetics of Spermiogenesis -- combining in silico and proteomic approaches in the mouse model
- Genomic Tools for Analyzing Transcriptional Regulatory Networks

Part II - Statistical Tools and Their Application

- Probability and Hypothesis Testing
- Stochastic Models for Biological Patterns
- Population Genetics
- Statistical tools for gene expression analysis and system biology and related web resources

Part III - Transcriptome Analysis

- What goes in is what comes out -- How to design and implement a successful microarray experiment
- Tools and Approaches for an End-to-End Expression Array Analysis
- Analysis of alternative splicing with microarrays

Part IV - Structural and Functional Sequence Analysis

- An Introduction to Multiple Sequence Alignment — and the T-Coffee Shop. Beyond just aligning sequences: how good can you make your alignment, and so what?
- A spectrum of phylogenetic-based approaches for predicting protein functional sites
- The role of Transcription Factor Binding Sites in Promoters and Their In Silico Detection
- In silico Discovery of DNA Regulatory Sites and Modules

Part V - Literature Mining for Association and Meaning

- Mining the Research Literature in Systems Biology

So you've found out that you don't really exist but are rather just some clever piece of artificial intelligence that someone engineered to function within a simulated world. Depressing news indeed but once you pull yourself together, the primary question should be: How do you escape?

I've been pondering this for awhile but today, after joking around about the topic over lunch (despite its serious nature), it dawned on me...

If your simulation programmer was capable of engineering something as complex as you, he most likely has also engineered mind uploading technology. Escaping the simulated world becomes a challenge of convincing your creator that you are indeed worthy of being uploaded into a piece of technology into his universe rather than your own simulated one. Perhaps even just as a toy for his pet or a robot that does his laundry. It's not important what you get uploaded into as once you're out, you can plot your next move, though being uploaded into something like a toaster may make your next move a bit more challenging.

I think people have been trying this as my friend pointed out... "Hello God, are you there?" so coming up with a more effective means beyond groveling may be all that is needed. One convincing argument might be that the two of you can strategize on getting out of his simulated universe...

Due out in November of '09 this is a book to keep an eye out for. Epignenetics of Aging looks to compile a wealth of data as to how epigenetics impacts aging from the molecular level to various diseases of aging.

I couldn't help but chuckle at the description of session 13 of MIT's OpenCourseWare on System's Biology:

I guess one could argue that slowing, halting and reversing the aging process is sufficiently ambitious (ya think?!?).

The synthetic biologists seeks to add to the the litany of cell functionality by creating new DNA sequences and downstream proteins.

So whose challenge is the greater? The synthetic biologist has the advantage of using rapid evolution cycles to test their new creations which are essentially organically driven robots where failure simply means trash what you have and start over. Compare that to the biogerontologist who seeks to engineer therapeutics for functioning organisms that typically prefer that they are not relegated to the trash bin (go figure).

I often hear the term "War on Aging" but has anybody ever actually declared war? Perhaps that is why so few are reluctant to join the fight?!? I seriously doubt that this is a major factor but I'm a big fan of excuse removal so I will formally declare war:

There, that should suffice. Now get to work :)

Crossposted at ImmInst

I've rambled quite a bit over the past years about trying to get more lay or otherwise skilled people contributing to science directly or indirectly. Stanford's F@H has been a great example of getting a large number of people donating their computing cycles to science. Additional incentives to do so would only further the success of this low level biology research that will eventually pay significant dividends towards longevity research. An ImmInst initiated project (grassroots volunteer effort more likely, akin to the F@H prize) could serve this purpose as well as bring more attention (which leads to more credibility, volunteers, funding, etc) to the life extension organizations and meme.

One idea that has been floated by myself and others (both here and elsewhere) has been the creation of games that benefit science. A folding game has been created by David Baker's protein folding group but it's success has been limited thus far. I suspect that this limited success is due mostly to the fact that it's less a game and more or less just a tedious challenge of guesswork, and a rather difficult one at that. Essentially, it just feels like work. My idea is to build something a bit more enjoyable that leverages the existing base of protein folders and see if we can extend through viral means into a larger community of gamers and even the general public.

Last winter, a scheme was hatched on the ImmInst Forums to push the Longevity Meme's Protein Folding team up in the ranks by offering cash and other prizes to top contributors. The result has been a resounding success launching the team over 120 rankings in list of folding teams.

Now the team is poised to crack the top 100 around the new year and forge even further up the rankings. If you're not already, do your part by folding for science. Fold for yours and others' future health by donating your computer's spare computing cycles to the largest super computing effort on the planet.

The Longevity Meme's Team # is 32461 if you would like to help us promote the meme of long and healthy lifespan. If you're a gamer or would like to make a more significant contribution then consider putting your computer's video card(GPU) to use or your PS3 as these are extremely powerful devices for doing the intensive computational biology work at the molecular level.

Onwards to the top 50 or even 25!

Wired has an article that touches upon the grail...

Drugs Reveal Another Possible Cause of Aging

Through the study of the SIRT1 and related pathways, David Sinclair and his group think they've found life's universal Achilles heel in the form of gene dysregulation.

It'll take some time for the mechanisms of how this gene dysregulation occurs but chromatin damage and changes within the epigenetic landscape are likely to play a big part and systems biology strategies will enable us to navigate this complex terrain. RNAi type therapies look to be our best possible strategies for modifications at this layer.

Happy Turkey Day...

Update: I chased down the original source:

Cell: Volume 135, Issue 5, 28 November 2008, Pages 907-918 : SIRT1_Redistribution_on_Chromatin_Promotes_Genomic_Stability_but_Alters_Gene_Expression_During_Aging

Technology Review also has a good overview with comments from Jans Vijg and Leonard Guarante:

Technology Review: How Cells Age - Parallels between mice and yeast uncover a potentially universal aging mechanism.

Building robust intra and intercellular aging models by leveraging and integrating systems biology data sets from other research fields, particularly cancer, is an idea I've been pondering for awhile. The data sets for such models are just starting to emerge and tend to fall into a handful of classifications thus researching some of the statistics and algorithms used for complex models has been my focus as of late. A fellow student in my Systems Biology course introduced me to the Veterbi Algorithm. It's a dynamic programming algorithm used for finding hidden state transitions. Given the complexity of metabolic gene regulation and the numerous unknowns it seems like a good algorithm to explore as a tool for deciphering the development of various aging pathologies. I'll blog in more detail as I spend more time with it.

The second half of my course deals specifically with Dynamic Programming in Systems Biology so I'll be exploring the general concepts further soon.
I also just learned that there is a new detailed Dynamic Programming course being developed by Pedro Mendes which sounds interesting.

Crowdsourcing, Human Computation. Call it what you will, but it's a new emerging force that is destined to play an increasing role in shaping the future of humanity's progress. The concept of millions of minds coming together to solve complex and challenging problems will dwarf the contributions of all the past brilliant minds... combined. Leonardo, Newton, Einstein? Great work guys but there's a new game in town and it's fielded with a team of millions of players who each contributes just a tiny bit.

We are just testing the prototypes of this concept, however are already seeing some incredible potential. There is evidence of it everywhere from online fundraising efforts to help the poor, for example through microlending, to simply donating computing time to efforts such as protein folding.

The one most recent and striking example unfolding before us is the grassroots effort that the American Democrats have organized to raise money for Barack Obama's presidential election campaign. The dollar amounts being raised for his bid for the White House are staggering and will likely change the electoral landscape forever. These individuals see a problem in leadership, band together to solve it through new social networks and communication mediums brought about by the internet and appropriate resources accordingly and in real-time. So why not use this concept to solve other large-scale or difficult problems?

Life Extensionist Ann C hosts the latest
Longevity Blog Carnival. Spread the word.

Adriano Henney and Giulio Superti-Furga suggest that ...with the right plan, systems biology can empower drug discovery. I suggest it will do far more than that. Systems Biology and it's vast warehouse of biological data will eventually be the centerpiece of all future medical intervention IMO.

Nature network has a forum discussion going on the drug discovery aspect.

At some point I'm going to need to put my schooling to work and start building some biological models. One focus area might be on the immune system:

google search: model+"immune+system"+"systems+biology"

Would a systems level understanding of Thymic Involution be a good area to focus research efforts on? I've skimmed over a few papers recently on the topic but it may be too ambitious of an effort given the current data sets available.

Studying aging from a systems perspective is going to require quite a bit of detailed data well beyond what microarrays are currently providing. One of the questions I've had is what level of detail can we capture within the big three -omics networks of genomics, proteomics and metablomics. The one that has concerned me the most due to reaction rates / real-time dynamics is the metabolic cycles. This paper by Sven E. Eklund et. al. discusses capturing cellular metabolic information at a fairly granular level using a biosensor called a multianalyte microphysiometer. Say that 10x fast.

A question to explore at some point is whether this technique would have a good enough signal to noise ratio to distinguish metabolic differences between various age groups for different cell cultures for systems models of aging. I've yet to scan the gerontology literature to see whether this data has been explored much but it's now on the growing to do list. Might as well put this blog to use in as many ways as possible :)

Leroy Hood is tossing around the term P4 Medicine, short for predictive, preventative, personalized and participatory medicine these days as he announces a a new startup venture that focuses on disease biomarker screening. The new company is focused on bringing together high-throughput robotics, microfluidics and antibody replacement techniques to provide better predictive capability within DX testing.

Statistical analysis of proteins and biological networks should eventually yield specific signatures that signal cellular aging in the coming decades. Early detection of aging pathologies is going to be a critical component of managing the aging process in the future thus it's encouraging to see this type research move from the lab to the marketplace.