How to analyze, integrate, and model large volumes of biological data - some thoughts

This blog post stems from notes I made for a 12 minute talk at the Oregon State Microbiome Initiative, which followed from some previous thinking about data integration on my part -- in particular, Physics ain't biology (and vice versa) and What to do with lots of (sequencing) data.

My talk slides from OSU are here if you're interested.

Thanks to Andy Cameron for his detailed pre-publication peer review - any mistakes remaining are of course mine, not his ;).

Note: During the events below, I was just a graduate student. So my perspective is probably pretty limited. But this is what I saw and remember!

My graduate work was in Eric Davidson's lab, where we studied early development in the sea urchin. Eric had always been very interested in gene expression, and over the preceding decade or two (1980s and onwards) had invested heavily in genomic technologies. This included lots of cDNA macroarrays and BAC libraries, as well as (eventually) the sea urchin genome project.

The sea urchin is a great system for studying early development! You can get literally billions of synchronously developing embryos by fertilizing all the eggs simultaneously; the developing embryo is crystal clear and large enough to be examined using a dissecting scope; sea urchins are available world-wide; early development is mostly invariant with respect to cell lineage (although that comes with a lot of caveats); and sea urchin embryos have been studied since the 1800s, so there was a lot of background literature on the embryology.

The challenge: data integration without guiding theory

What we were faced with in the '90s and '00s was a challenge provided by the scads of new molecular data provided by genomics: that of data integration. We had discovered plenty of genes (all the usual homologs of things known in mice and fruit flies and other animals), we had cell-type specific markers, we could measure individual gene expression fairly easily and accurately with qPCR, we had perturbations working with morpholino oligos, and we had reporter assays working quite well with CAT and GFP. And now, between BAC sequencing and cDNA libraries and (eventually) genome sequencing, we had tons of genomic and transcriptomic data available.

How could we make sense of all of this data? It's hard to convey the confusion and squishiness of a lot of this data to anyone who hasn't done hands-on biology research; I would just say that single experiments or even collections of many experiments rarely provided a definitive answer, and usually just led to new questions. This is not rare in science, of course, but it typically took 2-3 years to figure out what a specific transcription factor might be doing in early development, much less nail down its specific upstream and downstream connections. Scale that to the dozens or 100s of genes involved in early development and, well, it was a lot of people, a lot of confusion, and a lot of discussion.

The GRN wiring diagram

To make a longer story somewhat shorter:

Eric ended up leading an effort (together with Hamid Bolouri, Dave McClay, Andy Cameron, and others in the sea urchin community) to build a gene regulatory network that provided a foundation for data integration and exploration. You can see the result here:

This network at its core is essentially a map of the genomic connections between genes (transcriptional regulation of transcription factors, together with downstream connections mediated by specific binding sites and signaling interactions between cells, as well as whatever other information we had). Eric named this "the view from the genome." On top of this is layered several different "views from the nucleus", which charted the different regulatory states initiated by asymmetries such as the localization of beta cadherin to the vegetal pole of the egg, and the location of sperm entry into the egg.

At least when it started, the network served primarily as a map of the interactions - a somewhat opinionated interpretation of both published and unpublished data. Peter et al., 2012 showed that the network could be used for in silico perturbations, but I don't know how much has been followed up on. During my experiences with it, it mainly served as a communications medium and a point of reference for discussions about future experiments as well as an integrative guide to published work.

What was sort of stunning in hindsight is the extent to which this model became a touchpoint for our lab and (fairly quickly) the community that studied sea urchin early development. Eric presented the network one year at the annual Developmental Biology of the Sea Urchin meeting, and by the next meeting, 18 months later, I remember it showing up in a good portion of talks from other labs. (One of my favorite memories is someone from Dave McClay's lab - I think it was Cyndi Bradham - putting up a view of the GRN inverted to make signaling interactions the core focus, instead of transcriptional regulation; heresy in Eric's lab!)

In essence, the GRN became a community resource fairly quickly. It was provided in both image and interactive form (using BioTapestry), and people felt free to edit and modify the network for their own presentations. It readily enabled in silico thought experiments - "what happens if I knock out this gene? The model predicts this, and this, and this should be downstream, and this other gene should be unaffected" that quickly led to choosing impactful actual experiments. In part because of this, arguments about the effects of specific genes quickly converged to conversation about how to test the arguments (for some definition of "quickly" and "conversation" - sometimes discussions were quite, ahem, robust in Eric's lab and the larger community!)

The GRN also served to highlight the unknowns and the insufficiencies in the model. Eric and others spent a lot of time thinking through questions such as this: "we know that transcription of gene X is repressed by gene Y; but something must still activate gene X. What could it be?" Eventually we did "crazy" things like measure the transcriptional levels and spatial expression patterns of all ~1000 transcription factors found in the sea urchin genome, which could then be directly integrated into the model for further testing.

In short, the GRN was a pretty amazing way for the community of people interested in early development in the sea urchin to communicate about the details. Universal agreement wasn't the major outcome, although I think significant points about early development were settled in part through the model - communication was the outcome.

And, importantly, it served as a central meeting point for data analysis. More on this below.

Missed opportunities?

One of the major missed opportunities (in my view, obviously - feel free to disagree, the comment section is below :) was that we never turned the GRN into a model that was super easy for experimentalists to play with. It would have required significant software development effort to make it possible to do click-able gene knockdown followed by predicted phenotype readout -- but this hasn't been done yet; apparently it has been tough to find funding for this purpose. Had I stayed in the developmental biology game, I like to think I would have invested significant effort in this kind of approach.

I also don't feel like much time was invested in the community annotation and updating aspect of things. The official model was tightly controlled by a few people (in the traditional scientific "experts know best!" approach) and there was no particular attempt to involve the larger community in annotating or updating the model except through 1-1 conversations or formal publications. It's definitely possible that I just missed it, because I was just a graduate student, and by mid-2004 I had also mentally checked out of grad school (it took me a few more years to physically check out ;).

Taking and holding ground

One question that occupies my mind a lot is the question of how we learn, as a community, from the research and data being produced in each lab. With data, one answer is to work to make the data public, annotate it, curate it, make it discoverable - all things that I'm interested in.

With research more broadly, though, it's more challenging. Papers are relatively poor methods for communicating the results of research, especially now that we have the Internet and interactive Web sites. Surely there are better venues (perhaps ones like Distill, the interactive visual journal for machine learning research). Regardless, the vast profusion of papers on any possible topic, combined with the array of interdisciplinary methods needed, means that knowledge integration is slow and knowledge diffusion isn't much faster.

I fear this means that when it comes to specific systems and question, we are potentially forgetting many things that we "know" as people retire or move on to other systems or questions. This is maybe to be expected, but when we confront the level of complexity inherent in biology, with little obvious convergence between systems, it seems problematic to repose our knowledge in dead tree formats.

Mechanistic maps and models for knowledge storage and data integration

So perhaps the solution is maps and models, as I describe above?

In thinking about microbiomes and microbial communities, I'm not sure what form a model would take. At the most concrete and boring level, a directly useful model would be something that took in a bunch of genomic/transcriptomic/proteomic data and evaluated it against everything that we knew, and then sorted it into "expected" and "unexpected". (This is what I discussed a little bit in my talk at OSU.)

The "expected" would be things like the observation of carbon fixation pathways in well-understood autotrophs - "yep, there it is, sort of matches what we already see." The "unexpected" would be things like unannotated or poorly understood genes that were behaving in ways that suggested they were correlated with whatever conditions we were examining. Perhaps we could have multiple bins of unexpected, so that we could separate out things like genes where the genome, transcriptome, and proteome all provided evidence of expression versus situations where we simply saw a transcript with no other kind of data. I don't know.

If I were to indulge in fanciful thinking, I could imagine a sort of Maxwell's Daemon of data integration, sorting data into bins of "boring" and "interesting", churning through data sets looking for a collection of "interesting" that correlated with other data sets produced from the same system. It's likely that such a daemon would have to involve some form of deep correlational analysis and structure identification - deep learning comes to mind. I really don't know.

One interesting question is, how would this interact with experimental biology and experimental biologists? The most immediately useful models might be the ones that worked off of individual genomes, such as flux-balance models; they could be applied to data from new experimental conditions and knockouts, or shifted to apply to strain variants and related species and look for missing genes in known pathways, or new genes that looked potentially interesting.

So I don't know a lot. All I do know is that our current approaches for knowledge integration don't scale to the volume of data we're gathering or (perhaps more importantly) to the scale of the biology we're investigating, and I'm pretty sure computational modeling of some sort has to be brought into the fray in practical ways.

Perhaps one way of thinking about this is to ask what types of computational models would serve as good reference resources, akin to a reference genome. The microbiome world is surprisingly bereft of good reference resources, with the 16s databases and IMG/M serving as two of the big ones; but we clearly need more, along the vein of a community KEGG and other such resources, curated and regularly updated.

Some concluding thoughts

Communication of understanding is key to progress in science; we should work on better ways of doing that. Open science (open data, open source, open access) is one way of better communicating data, computational methods, and results.

One theme that stood out for me from the microbiome workshop at OSU was that of energetics, a point that Stephen Giovanonni made most clearly. To paraphrase, "Microbiome science is limited by the difficulty of assessing the pros and cons of metabolic strategies." The guiding force behind evolution and ecology in the microbial world is energetics, and if we can get a mechanistic handle on energy extraction (autotrophy and heterotrophy) in single genomes and then graduate that to metagenome and community analysis, maybe that will provide a solid stepping stone for progress.

I'm a bit skeptical that the patterns that ecology and evolution can predict will be of immediate use for developing a predictive model. On the other hand, Jesse Zaneweld at the meeting presented on the notion that all happy microbiomes look the same, while all dysfunctional microbiomes are dysfunctional in their own special way; and Jesse pointed towards molecular signatures of dysfunction; so perhaps I'm wrong :).

It may well be that our data is still far too sparse to enable us to build a detailed mechanistic understanding of even simple microbial ecosystems. I wouldn't be surprised by this.

Trent Northern from the JGI concluded in his talk that we need model ecosystems too; absolutely! Perhaps experimental model ecosystems, either natural or fabricated, can serve to identify the computational approaches that will be most useful.

Along this vein, are there a natural set of big questions and core systems for which we could think about models? In the developmental biology world, we have a few big model systems that we focused on (mouse, zebrafish, fruit fly, and worm) - what are the equivalent microbial ecosystems?

All things to think about.


p.s. There are a ton of references and they can be fairly easily found, but a decent starting point might be Davidson et al., 2002, "A genomic regulatory network for development."

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