Putting the “system” back in Systems Biology

Cold Spring Harbor labs on Long Island has a diverse offering of conferences that attract experts from all areas of biology. For the last six years, there has been a sister group organizing conferences in Suzhou, China. I spent last week at the 2016 CSH Asia conference on Systems Biology.

While I have been to many conferences on aging and a few on evolution, this was my first Systems Biology conference. I looked forward to learning how biologists think about whole-body issues of homeostasis, organization, and (maybe if I was lucky) blood signals that regulate aging.

What I found instead was that researchers in systems biology are doing what other biologists are doing: they are babes in toyland, exploring the potential of a seductive array of new biomolecular tools. They are compiling catalogs and making maps and correlating every chemical they can find with every other chemical, and collaborating with statisticians to look for patterns in the data. If “systems thinking” is from the top down, what I found at this meeting was just the opposite.

A hundred years ago, Lord Rutherford said that “All science is either physics or stamp collecting.” He was mocking the biologists’ program of collecting specimens, classifying and cataloging them. Twentieth century biology turned this around; it was neither physics nor stamp collecting, but model-building. Systems biologists in particular have analyzed living organisms in terms of signals and networks and energy flows, and have generated a great deal of understanding. At its best, biology has forged a new mode of science.

So why is it that 21st Century systems biology is looking once more like stamp collecting? It’s a question I asked one of the conference organizers (in more polite terms), and he responded that “we are working from a reductionist framework. We are trying to build understanding from the bottom up.”

There’s a deeper answer

Why, after the revolutionary successes of late 20th century biology, should bioscience find itself back at square one, trying to build a foundation? The short answer: genetics is simple; epigenetics is complicated.

The heyday of genetics started from Crick’s decoding of the genetic code in 1961. DNA was revealed to be a blueprint for producing proteins that would do the body’s work. The code was just as simple and elegant as it could be, and the machinery to do the translation was segregated in ribosomes, which could be isolated and picked apart. The era of genetics ended in 2003 with the completion of the human genome project. Results were a surprise to everyone, and the message took awhile to filter into conceptual thinking: The genome is 3% genes and 97% gene regulation. All the impressive tasks of development, homeostasis, and metabolism are performed by an exquisitely adapted system that turns genes on and off in the right place at the right time.

2003 ended the era of genetics and began the era of epigenetics. How is gene expression regulated and contolled? DNA methylation was the first mechanism discovered. But as clues appeared and mechanisms were partially elucidated, it has become apparent that epigenetics is as complicated and intractable as it can possibly be. Besides methylation, there are more than 100 modifications of the DNA and its associated proteins (histones) that affect gene expression. There is also the way in which DNA folds around itself, leaving some regions open where they can be transcribed and keeping other regions under tight wraps. Finally, there is a variety of post-translational modifications; even after a stretch of DNA has been transcribed into RNA and translated into a protein, the protein can be turned on or off by adding a phosphate group or a methyl or acetate at any number of receptor sites.

Metabolism is now seen as a dense web of interacting processes, intertwined causes and effects. Gene network maps draw lines between genes that are co-expressed, and can divide the territory into subsets (modules) that are more closely related to each other than to other modules.


But this is a picture that only a computer could love; it contains intricacies on a scale that human consciousness cannot grasp with conceptual understanding.

Contrast this to the naive simplicity of Crick’s Central Dogma of Molecular Biology: Information flows in one direction, from DNA to RNA to proteins. Crick lived to see his insight de-dogmatized by exceptions, but it has been since his death (2004) that the essential, bewildering complexity of biochemical networks has been revealed.

No wonder the community of systems biologists feels that they are starting over again, collecting, classifying and cataloging stamps.

New Tools

Biochemical science this last few years seems to be driven by newly available technologies. These are so powerful and coming so fast that just exploring what they can tell us is occupying the lion’s share of available funding and lab space. I knew about some of these, and several more were new to me last week.

  • CRISPR-Cas9. This is a tool adapted from bacterial defenses against viruses. It has made it easy to delete a particular gene within a living cell culture, and perhaps within a living, breathing animal. It has been adapted to insert an exogenous gene at a particular location on a particular chromosome, and even to modify a particular section of DNA to turn a gene on or off. Cas9 has been limited to small “payloads”, adding short sequences of DNA, but just last year, techniques were reported for splitting a larger payload among multiple Cas9 vectors [Ref1, Ref2] in such a way that they piggyback.
  • Hi-C is a modern, computer-intensive version of a 20-year-old procedure for mapping a coiled and folded chromosome in 3-space. First, the chromosome is frozen by introducing random bonds between nearby strands. Then it is fragmented with a DNA slicing enzyme. Then the pieces are sequenced (this is the new part) with high throughput sequencing that can tell you which genes are physically closer to which other genes in the folded, 3-D configuration.  Finally, the computer can be used to reconstruct a picture of the 3-D configuration.
  • ATAC-Seq. This is a tool for finding the genes that, at any given time, are in open stretches of DNA (euchromatin), available to be transcribed. Chromosomes are peppered with an enzyme that slices up DNA. The fragments are then collected and sequenced, and a computer program matches the fragments to map where they came from. The DNA that was tightly-packed (heterochromatin) contributes few fragments because the enzyme can’t reach it. Thus the genes that are seen are representative of what is available for transcription.
  • Methylation mapping. Methylation of C’s in stretches of C-G-C-G-C-G-C-G-C-G within a chromosome is the best-studied mechanism of epigenetic regulation. Just in the last few years, it is possible to map the methylation state of an entire genome. A chemical transformation transforms only unmethylated C’s to uracil in a strand of DNA (with bisulfite), leaving the methylated C’s unconverted. By sequencing the strand before and after this transformation, and using a computer to map the differences, the places where C’s were methylated can be identified.
  • ChIP-Seq. If you know of a particular transcription factor—a protein that binds to DNA and turns certain genes on or off—then this technique can tell you where on the DNA the protein attaches itself. The technique combines two older technologies: immunoprecipitation, where an antibody is introduced that picks out one particular protein, and high-throughput sequencing, which identifies and locates the patch of DNA that is stuck to the CHIPped protein.

Gene expression maps have been around for a few years. They provide an enormous amount of information about what genes are being expressed where and when, but they are notoriously difficult to make sense of.  More recent are correlation maps, in which every gene is correlated with every other gene in a huge matrix that shows how likely they are to be expressed at the same time. I am intrigued by principal component analysis. You can start with a set of genes and measure all the cross correlations, and the math comes up with a combination of the genes most likely to be expressed together (Principal Component #1).

Sometimes a single gene sticks out, and the authors conclude that this particular cancer can be treated by targeting this particular gene.

More often, the results show a combination of hundreds of genes that tend to be expressed together in a particular proportion, say 1.2% gene #1, 0.04% gene #2, 0.15% gene #3…and so on, with coefficients for hundreds of genes. This is the output of a principal component analysis. If such a profile is identified with a healthy state, or a young state, we do not yet have the capability to shape this profile of gene expression in a cell culture, let alone in a living animal. But it is not inconceivable that we will acquire this ability with advancing biotechnology of the coming decade(s).


The Harvard laboratory of Brenda Andrews is fully automated, with robotic handling of yeast culture plates, robotic data collection, computerized data analysis. All that’s left for the human to do is to write the historical introduction and submit the manuscript. I am a fan of artificial intelligence and computer learning for some applications. Computers have their own ideas of what constitutes a pattern or a trend. They often come up with unexpected solutions to problems, even simple solutions on rare occasions. But AI never produces elegant theories or new ways to look at the big picture. We give that up when we rely on computers to do science for us.

Growth, Development and Aging

Growth and development are programmed, to be sure, but (so far as we know at present) there appears to be no central coordinator of the process. Rather, the tangled web of chemical signals adapts and responds to changes in the body and in itself. The intelligence is not in a central brain, but is distributed through the system itself. There is no one calling the shots. The metabolism behaves intelligently the way a beehive behaves intelligently, though no single bee has a a clue concerning the hive’s plans and strategies.

I have bet my career on the thesis that aging is a metabolic program, a continuation of the process of development into a phase of self-destruction. I used to think that this meant there were genes for aging. I was the most optimistic speaker at the anti-aging meetings. “All we have to do is find the aging genes and turn them off.”

Then I accepted the new picture centered on epigenetics. I thought that chemical signals were arranged in hierarchies, with a few hox genes and transcription factors controlling a much larger number of workhorse proteins that actually get the job done. The job of the anti-aging scientist is to re-balance the transcription factors to create a more youthful profile, and the workhorse proteins would dutifully take care of the rest.

But more recently, I learned that there are thousands of transcription factors, comparable to the number of genes they regulate.  And the lines between promoters, enhancers, transcription factors, and metabolites has been blurred.  A less optimistic scenario is beginning to come into focus for me. I believe that aging is a continuation of the developmental program, but development is inscrutably complex, and it seems to be controlled by a web of interacting molecules that play multiple roles. Each one is a cause and an effect. Many have roles both a regulatory agents and also as workhorses. There’s no one in charge of the factory. The factory is designed so ingeniously that it runs itself.

Study of Development may be a Key to Aging

Much is known about details of development, but there is no systemic understanding of how the process is put together

    • How much is predetermined in cell lineage?
    • How much is self-organization?
    • How much is centrally organized, through internal secretions?
    • How do these three interact?

Of course, the same questions may be asked about aging. It may be more feasible to approach these questions through development than through aging, (1) because development happens on a faster time scale, (2) because aging contains a stochastic element not present in development, and (3) because the phenotypes of development can be observed clearly and locally. I recognize that this suggestion means going back to basic science to make a long-term investment in understanding of aging, but maybe that’s what we need.

28 thoughts on “Putting the “system” back in Systems Biology

  1. Hi Josh,

    totally agree. I had my brief adventure in Systems Biology between 2003-2006 and I left disillusioned. Garbage in garbage out – that was my conclusion.

    I have totally come to the same conclusion as you. We have to understand development right on from the blastocyst. I actually now think strict organismal aging has something to do with the introduction of the 3 germ layer body plan that come with the Cambrian explosion (triploblastic animals).
    There were also some findings that certain growth conditions in epithelial cells start processes similar to epithelial cell migration in gastrulation.
    And also its known fromSteve Horvath research that the epigenetic clock ticks much faster when the animal is young.
    I have collected some thoughts and papers here.
    We should also pay more attention to accelerated aging in cell cultures. I think we could have a Flemming moment just buy studying keratinocyte cultures.

    Also isnt it strange that most cancers are epithelial?
    Seems to me that all epithelial cells just want to become mesemchymal and became cancerous in the process. But mesemchymal cells they stay put most of the time. Fibrosarcoma is extremely rare. Melanoma is much more common.

    I think there is just something mysterious in the transformation from unicellular life to multicellular sheets (epithelial cells), then the transformation of some epithelial sheet cells to the more free mesemchymal form.

    • I think I forgot to cite the missing link about accelerated cellular aging

      I have collected some evidence upon which I speculate that there is accelerated aging in some cultures. Some cultures that are not restricted by telomere length such as mouse cell cultures in general or hTert transfected human cells seem to have another limit on cell duplication that is dependent either on p16Ink4a or p53.
      Researchers attribute it to the mysterious “culture shock” but I am not sure.
      When this was hotly researched around the year 2000 the epigenetic clock was unknown. But there has been one study recently that proved there is accelerated epigenetic aging in one such culture.
      There is another study that links “culture shock” to epithelial migration at gastrulation.
      The world is awaiting a new Alexander Flemming…

      In more details:

  2. Reading your post made me think about my first years in the gerontological field in the early 90s. My director assigned me a pile of papers to read regarding brain development in embryonic and post-natal life to understand why and how brain ages. It seemed to me a very original approach but at the same time too far from our real goal. As a disciplined young researcher I did the task and finally come out with an idea that consisted in the analysis in aged rats of a protein, highly expressed during development, involved in axon sprouting. Many years have gone since that time and I made up my personal idea of aging. I do not think that aging is only related to genes, I think that at a certain time of life, different for every species, cells and organisms cease to fight against external aggressors. This of course has something to do with DNA, evolution and clocks, but since then are the external factors, present and past, that became prevalent. At that time nature is the only and indisputable lord that currently we are not able to counteract.

  3. Hi Josh,
    I’m reading your post’s for a while.
    Most of the time l agree with you.
    But your picture (the young boy);
    I really find it a bit weird.

  4. For me Josh, this is the best column you’ve written. I totally agree with your conclusion that we are dealing with systems, usually called gene-regulatory networks, consisting of transcription factors, their target genes, which may also be other transcription factors or even miRNAs. used to alter the abundance of cellular proteins or even long-non-coding RNAs (lncRNAs) to alter the abundance of miRNAs. Then there are not only hundreds of potential epigenetic codons, but considering the number of alterable histone residues and their combinations, hundreds of millions of such potential codons. Add to that the fact that any single protein can participate in more than one regulatory network, and may have completely different roles (like beta-catenin which can be a transcription co-factor or an intercellular ‘glue’, or metabolic enzymes that become transcription factors when not occupied by substrate). And we find that what we supposed to be absolutely vital molecules may be lost and the networks adjust themselves to working without them.
    What Josh didn’t mention as part of the new system biology tools, are tools for the mind – like Cell Designer (which I’ve begun playing with) which can simulate the reactions occurring in cells and ‘prove’ if you model of a metabolic network is correct (you have to put in all the values of concentration, volumes etc. for different cellular compartments. The metabolic circuit diagrams are becoming too complex for a human mind to deal with; when you have hundreds of proteins interacting, forming positive and negative feed-back loops, we just can’t picture that or know what the results will be by calculating them by hand, we need a computer simulation (now how realistic those simulations are is an important story, but things have already be discovered by their use).
    I do however believe there’s a simple overarching principle that guides aging across the phyla and kingdoms – I promise to tell all soon.

    • I think we need more studies on histone modifications.
      There are whole cell studies on DNA bases, mRNA profiles, DNA methylation, even on non coding RNAs I believe but I think we dont have an whole cell interrogative method on histone modifications yet.
      Also as epigenetics defines cell type and age, the old approach of meshing all cells in a tissue or just taking only peripheral blood cells as this is very convenient – just doesnt work.

  5. The “inscrutably complex” interactions may very well turn out to be simply fine-tuning of programmed aging, not the fundamental process. After all, programmed death would have provided an advantage at a very early stage in evolution and therefore may be enabled (mostly) by a single fundamental and preserved process.

    As to AI, they will rapidly become more sophisticated and fully capable of abstracting theory, so the bottom-up work of Systems Biology may wind up providing invaluable infrastructure and data.

    • AI is good for what we can train it.
      Its easy to train for something that is repeatable for example 10000 photos of cats or dogs.
      But the research process is unrepeatable. Its hard to train AI against the unknown.
      But it can speed up some tenuous repeatable jobs in the research, like searching related papers, etc.

  6. It seems that blood signal molecules should be upstream of the epigenetic complexity, or at least a place in the system where we could excerpt some control.

  7. So absent of any ‘shortcut’ to anti-aging presented by genetics, will you now be throwing your full support behind SENS?

    Also it sounds like you no longer believe in programmed aging, only antagonist pleitropy; a developmental process that continues into adulthood causing harm?

    • Good questions – thanks for the opportunity to clarify.

      I still believe aging is programmed, in the same sense that development is programmed. “Pleiotropy” means a side-effect of natural selection for a different function. I believe that aging is selected for its own sake, primarily as a measure for demographic stabilization.

      I have always had a mixed relationship with SENS. I’m an enthusiastic supporter of some of the SENS research, and there are other areas where I think there are more promising avenues available.

      Despite all the evidence that aging is complicated, there are simple interventions that have shown major effect on life span. Senolytics, telomerase activation, and stem cell therapies are areas where I expect to see major advances in the immediate future.

      I’ve said in this space that the most important and under-studied area is combining treatments that we know have a small effect on lifespan to see if we can find combinations with large effect. People in our movement (including me) take multiple supplements every day, though we know nothing about how they interact.

      • The advantage with SENS is that you are treating an endpoint, I.e. Senolytics removing senescent cells. You are not doing anything to stop more senescent cells accumulating in the future or changing the rate of aging, just resetting the level back to a more youthful state.

        I will be getting your book soon and who knows, I may be convinced that aging is selected for its own sake, but for now I think its more likely to be pleitrophy – as Blagosklonny says, a bath tap that evolutions did not ever bother turning off.

        • Thanks, Mark – I don’t know what you mean by treating the endpoint. SENS anticipates repeated treatments as things continue to “wear out” and the body needs ongoing maintenance. I remain hopeful that the body knows how to fix itself, and that reprogramming will obviate the need for repeated, engineered repairs. (I also think that engineering to replace nature is a formidable task, in most cases; nature designs things differently from the way we design things, and molecular machines can do things that even micro-robots can’t.)

          Blagosklonny’s theory of aging is not so much pleiotropy as a developmental program that never fully turns itself off. I agree with the mechanics, but I ask: why isn’t the body turning it off? The old argument that it just isn’t worth it because so few animals live to old age in the wild is kaput, now that we know there is a substantial cost of aging in the wild. Epigenetics is exquisitely timed and regulated–it’s more and more clear that epigenetics is how the body maintains homeostasis, as well as how it grows and shapes itself. So I just can’t imagine that evolution “forgot to turn off the bath tap”.

          Thanks for an open mind, and I look forward to discussing ideas in my book with you.

          – Josh

          • Sorry Josh, what I meant by ‘endpoint’ was that senescent cells are an end result of ‘aging’, one that could be removed, repeatedly, as you say, without changing the underlying process that produces senescent cells (i.e. aging). It is not overly difficult to do this through an engineering process, as has been shown in mice, but interfering with the underlying process of senescent cell accumulation possibly is very difficult, because of the complexities of metabolism etc.

            Why is the tap not turned off? Probably because it would compromise survival early on – we need to be big and strong, etc. And why would evolution bother turning it off once we have reproduced? Again, I will read your book, and am willing to be persuaded – but what we can agree on now is that whatever the reason, the tap is not being turned off at present!

          • You mean *one* of the endpoints and ‘individually’ so, right? Cause there are more problems to fix AND, on an organismal level they continue to drive aging, exponentially, by raising even more undead cells like them. I do like the SENS approach as well, at least until Josh’s and Harold’s views can be put to good use.

            Not holding my breath tho (as that would be counter-productive :)), hate to be the ‘party pooper’ (believe me, I already get a lot of flak from people who rather shoot the messenger. Amusing though it may be, I just wish they could be shaken faster from their egoticism ) but, I’m afraid we’re in for the aforementioned nightmare scenario. Nature likes robustness, if she cares at all for evolving anything for so long, it will be a network of effects and side-effects. As Josh’s updated views displays nicely on above graphics.

            Nevertheless, there may still be a silver lining to be had. Evolution is rather slow relative to our perception, we can still modulate such effects faster than it can adapt. But researchers need to be more open minded for this to happen.

  8. Yet, despite all the feedback loops and inherent complexity, if you raise interest rates, all things being equal, asset prices tend to drop. Apologies for the economics analogy, but just to illustrate that sometimes a single point of action can have somewhat predictable results in an otherwise very complex system.

    I fully agree that ageing seems part of a developmental program. Because for the most part it is rather similar in most members of the same species. That’s why you can tell most people age with certain accuracy just by looking at them.

    So if it’s a program, what is keeping the time? You have hinted at this many times on your blog. If the clock resides in the whole system then we are in for a long way. But, if telomeres are a bit like interest rates and the economy, we may be in for a winner.

    • I agree. I think that telomeres are one clock, but I’ve calculated that we should only expect a few years’ life extension from telomerase activation. Fossel warns me that that conclusion is based on the presumption that telomere measurements are accurate, but in fact most of the scatter in telomere measurements is in the methodology, not in the telomeres. I’m eager to see what kind of a boost we get from telomerase activation.

      …and yes, the nightmare would be that there is no localized clock, but that the clock is coded in a thousand hormone levels.

      • What about Blagosklonny’s ideas on persistent MTOR activation driving aging? It seems to me that we age at a rate related to the time it takes us to grow to adulthood. The idea is that every time we eat we are activating metabolic pathways that give us energy to keep us alive but also cause damage. Interventions that lower ROS production (CR, for example) that extend life seem to fit in well with this idea.

      • What about Voronoff? Between 1917 and 1926, he carried out over five hundred transplantations on sheep and goats, and also on a bull, grafting testicles from younger animals to older ones. Voronoff’s observations indicated that the transplantations caused the older animals to regain the vigor of younger animals. Voronoff speculates that effects include better memory, the ability to work longer hours, the potential for no longer needing glasses (due to improvement of muscles around the eye), and the prolonging of life.
        Obviously, the introduction to the consensus system of body cells the source of young signals can shift the entire system to the youth (albeit briefly). Let us remember also the effect of the infusion of young blood plasma into the old body.
        It is interesting to note that at the age of 70 Voronoff married Gerti Schwartz (the beauty and illegitimate daughter of King Carol of Romania) who was 49 years his younger!

  9. Different processes give feedback about timing at different timescales. At the sub second scales we have ions and channels. From second to day scale we have hormones. For daily and yearly changes we have the DNA methylation clock.
    I think there are two processes that need to be understood. One is the cellular state defined by the chromatin state (mostly histone modifications). This defines the current type of the cell and also what the next stages can potentially be. This is like finite state machine, like digital logical.
    Then we have the long range timing process – demethylation of methyalated DNA and methylation of unmethylated DNA sites -as time goes by this can change the state of the cell, for example demethylating regulatory elements that kick off transition to a new chromatin state.
    In order to get young again, we need to set back the timing and keep the state intact. This is hard because such program probably does not exist. The only event that turns back the DNA methylation clock is the fertilization. But it also resets the cellular state and restarts the development program.
    This coupling makes aging an especially hard to crack nut.
    About rapamycin, senolytics, CR, DR and the likes. They dont reset the methylation clock and they dont change the cellular state either. They just alleviate the degenerative effects of the methylation clock I belive.
    About parabiosis: I believe that must be true. In order to become a robust process the individual cellular clocks have to be synchronized. I guess mostly via hormones and other chemical signalling. So we can perturb the process, but it is a one way road, hormones and chemicals wont turn it back. Its a maze of chromatin states and methylation levels and the only winner combination leads to the development of a new organism. All other combinations lead to senescence, apoptosis or cancer.
    I wouldnt give it up though. We might be able to traverse chromatin states in a different way that it happens in nature. Also we might be able to run the methylation clock backwards like in the blastocyst. Quick demethylation followed by strong methylation, while keeping the important histone modification sites protected from DNA methylation by PRC binding – this is essentially what happens after fertilization. But we may be able to do it in a differen state, not embryonic stem cell state.

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