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Summary thoughts on the BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

I really enjoyed this workshop – met new people, chatted about systems biology, clinical genetics, surname-DNA associations, The Princess Bride and Spinal Tap. From a combination of presentations and chats, two defining topics of discussion in this workshop emerged:

  • social challenges, or getting the different disciplines within systems biology to understand one another. Alternatively, people also mentioned the challenge in getting different collaborating groups to work together;
  • stable infrastructure funding, or getting money for supporting software and for building and supporting data standards.

In my opinion, the former is much less of a current challenge than the latter. From my personal experiences within CISBAN (which contains a variety of experimental biologists as well as different types of theoretical biologists, mathematicians and statisticians), we have progressed to the point that I really feel that each "group" understands what the others do. In other words, in a local context, I think that social challenges are minimal. Longer-distance social challenges will remain around a little longer, but with the increasing use of online social networking tools (1, 2, 3, 4, 5, 6), I think much of this could be minimized. In contrast, I think that the challenges in getting funding for stable infrastructure (software and data standards) isn't advancing as quickly as it should. The production and maintenance of life-science data standards are vital to more efficient data sharing and collaboration. People should make room in their grants for the development of data standards (e.g. MIBBI guidelines, syntaxes or semantics – see Frank's excellent discussion on the issue) that will benefit them. Core institutes such as the EBI do a lot of this work, but can't get funding for everything.

I started thinking about all this stuff on Wednesday morning, and writing this did somewhat affect the notes I took in some of the talks, and for that I apologise! πŸ™‚

And, in conclusion, some light entertainment. There was a third category of discussion which many will be familiar with:

  • acronyms

I'm as guilty as the rest of them. Here's a small selection of examples of how much us scientists love our acronyms, and those things which are very close to true acronyms: APPLE, BASIS, CRISP, EMMAS, PRESTA, PheroSys, Phyre, PiMS, SToMP, SyMBA (mine), SysMO, SUMO, ROBuST and others. For a guide to how to build acronyms, see the PhD Comic's excellent summary of the topic (and the related FriendFeed discussion).

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CISBAN and telomere maintenance and shortening, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Amanda Greenall: Telomere binding proteins are conserved between yeat and higher eukaryotes. The capping proteins are very important, because they prevent the telomeres from being recognized as double-strand breaks. They work on cdc13, which is the functional homologue of POT1 in humans. A point mutation cdc13-1 allows them to study telomere uncapping. When grown above 27 degrees Celcius, the cdc13-1 protein becomes non-functional, and fall off. This uncapping causes telomere loss and cell-cycle arrest. So, they do further study into the checkpoint response that happens when telomeres are uncapped. Yeast is a good model, as many of the proteins involved in humans have direct analogs in yeast. They did a series of transcriptomics experiments to determine how gene expression is affected when telomeres are uncapped. They did 30 arrays, and the data was analysed using limma. 647 differentially-expressed genes were identified (418 upregulated (carbohydrate metabolism, energy generation, response to OS), and 229 downregulated (amino acid and ribosome biogenesis, RNA metabolism, etc)). The number of differentially-expressed genes increase with time. For example, 259 of the genes were involved in DNA damage response.

They became quite interested in BNA2, which is an enzyme which catalyses de novo NAD+ biosynthesis. Why is it upregulated? It seems over-expression of BNA2 enhances survival of cdc13-1 strains (using spot tests). Nicotinamide biosynthetic genes are altered when telomeres are uncapped in yeast and humans. The second screen was a robotic screen to identify ExoX and/or pathways affecting responses to telomere uncapping. Robots were used to to large-scale screens that can measure systematic cdc13-1 genetic interactions. One of the tests was the up-down assay, which allows them to distinguish Exo1-like and Rad9-like suppressors. Carry on with the spot tests until have worked through the entire library of strains.

Darren Wilkinson: a discrete stochastic kinetic model has been built to model the cellular response to uncapping. (J Royal Soc Interface, 4(12):73-90), and in Biomodels. Encoded in SBML and simulated in BASIS (web-based simulation engine). You can use the microarray data to infer networks of interactions. Such top-down modelling can often be done with Dynamic Bayesian Networks (DBNs) for discretised data and sparse Dynamic Linear Models (DLMs) for (normalized) continuous data. A special case of DLM is the sparse vector auto-regressive model of order 1, known as the sparse VAR(1) model, and this appears to be effective for uncovering dynamic network interactions (see Opgen-Rhein and Strimmer, 2007). They use a simple version of this model. They use a RJ-MCMC algorithm to explore both graphical structure and model parameters. When the RJ-MCMC is performed, it's quite hard to visualize. They do a plot of the marginal probability that an edge exists. This can also be summarised by choosing an arbitrary threshold and then plotting the resulting network. You can change the thickness of the edges so they match the marginal probability associated with each edge. This picture is then easier for biologists to analyse, and allows them to narrow down their search for important genes. He also performed analysis over the robotic genetic screens. There are usually about 1000 images per experiment, each with 384 spots, and therefore image analysis needs to be automated. Want to pick out those strains that are genetically interacting with the query mutation. For interactions to be useful concept in practice, you need the networks to be sparse. With HTP data, we have sufficient data to be able to re-scale the data in order to enforce this sparsity. A scatter-plot of double against single will show them all lying along a straight line (under a model of genetic independence). Points above and below the regression line are phenotypic enhancers and suppressors, respectively.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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Marco Morelli and the micro-evolution of RNA viruses, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

More fully, he's talking about the micro-evolutionary dynamics of RNA viruses. They want to get a full picture of what happens from the infection of a single cell to an entire outbreak, and all the intermediate scales. The levels of granularity he's looking at goes as follows: within cell, within host (not all viral particles in one host are genetically identical), within group (physical proximity of host to others), between groups (long-distance spreading). The data at each stage is different: from molecular data to epidemiological data. They looked at foot-and-mouth disease (FMD) and plum pox virus (PPV, transmitted by vectors), both RNA viruses. 10,000 farms were culled in the 2001 UK FMD outbreak. However, during this time, modellers were consulted. Samples were taken from every infected farm, and are stored at the IAH Purbright. This means that there's lots of data available. Then, he described a genetic tree that was built based on the FM viruses found in farms in Durham county during the outbreak. However, many transmission patterns are compatible with the tree. With some basic parameters, you can estimate how likely it is that one farm infected another. Among the total set of transmission trees (~2000), only 4 matched the values properly, and can therefore choose the most-likely tree (which accounted for about 50% of the likelihood), and therefore the most likely transmission pattern. Some of the movements show very large distances (of about 15 km). Is it a fault of the model, or a signature of some extrinsic event like transmission via car travel (human) or delivery of infected material. They still have more data (e.g. timing of transmissions) that they still have to use.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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Scott Grandison, leaf growth and form, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Talking about the physical changes occuring in leaves, and looking at the different levels of granularity and orders of magnitude you need to think about (e.g. DNA-scale up to the macro, leaf-scale). Multidisciplinary team, and, as with other centres described at this workshop, the lines are beginning to blur. This was a really great talk, but had many videos that just cannot be reproduced here. There was a nice picture of someone viewing, in proper 3-d, bits of a plant, which went with the argument that transposing 3-d objects to 2-d can often cause problems with your visual analysis. They've been able to get parameters for the rate of growth of individual areas of a leaf – many areas, with many rates. They have made the Growth Factor Toolbox (GFtbox). The models they show using the GFtbox are very nice, and show the development of, for example, the specialized leaf of the pitcher plant or the growth of a "standard" leaf shape for Arapbidopsis.

Great talk! πŸ™‚

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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OCISB: From small to large networks and back, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Judy Armitage: Bacterial sensory networks. The e.coli chemotaxis system is probably the best-understood "system" in biology, where biases in swimming direction are provided by regulating motor switching. The chemotaxis pathway is a paradigm for HPK-RR (histidine protein kinase – response regulators) pathways. There can be over 100 HPK pathways in a single species. OCISB projects include: extend E.coli models to species with two or more chemosensory pathways, and extend these to HPKLRR pathways in general to allow prediction of partners. They started with R.sphaeroides, her "favorite" bacterium. This bacterium has 2 targeted pathways preventing crosstalk. They gave the generated data sets to the modelling groups and asked if proteins operating in parallel or linear pathway? The control theory people came up with 4 models that fit the data, but 3 could be excluded based on perturbation tests in vivo. The same data was given to mathematical biologists.

Modelling was with ODEs (temporal dynamics), and partial DE (for spatiotemporal dynamics). Porter et al (2008) PNAS online, showed how histidine kinase CheA3 is also a specific phosphatase for CheY6-P, one of the 6 motor binding proteins – tuning kinas:phosphatase will control motor switching. Further, there must be a link between cytoplasmic cluster and polar kinase. CheB2~P phosphorelay allows response to environment to be tuned to metabolic need. How common is this and how is discrimination achieved? CheA (HPK) CheY/CheB (RR). Modelling MCP Helix mutants with the sidekick tool – a coarse-grain transmembrane (TM) pipeline.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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Michael White and the NF-kappaB signalling system, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Michael White: Dynamics and function of the NF-kappaB signalling system. NF-kappaB controls cell division and cell death in all cells. How can a simple signal carry so much information (the cell cannot afford to make a mistake!)? It is a complex network with multiple feedback loops (high dynamic complexity). People think that the IkappaB holds it in the cytoplasm, but this doesn't look to be correct. Living cell imaging shows that NF-kappaB oscillates asynchronously between the cytoplasm and nucleus in single cells (i.e. doesn't happen at the same time in multiple cells). However, each cell is cycling with the same amplitude etc, so they're doing the same thing, just not at the same time.

Can we synchronise the oscillations? You can do a repeat pulse protocol and then check to see if the synchronisation has happened. When you stimulate at 100-150 minutes then you can synchronise and not get damped oscillations. They have built a stochastic model. There are a nice set of pictures of pathways, but obviously cannot reproduce those here.

Here go the batteries again…(rest of notes from the paper notes I took, which are generally much lower quality)

Some of this work is funded under SABR, where they will focus on dynamic live cell imaging, quantitative proteomics/phosphoproteomics, genomics/bioinformatics, data analysis, deterministic/stochastic modelling and databases. What are the causes of differential expression? Oscillation dynamics is one possibility (and what he describes in this talk) Others could be signal-specific IkappaB processing, differential NF-kappaB dimer formation, differential protein modifications. Is degradation of IkappaBs regulated by Rel protein binding? NF-kappaB could be differentially phosphorylated.

Finally, one last note on outreach: they've had quite the success with biologists interacting with mathematicians in the group. Biologists are now taking weekly math courses, and it was their idea. That's great πŸ™‚

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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Jim Beynon and PRESTA, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

PRESTA stands for Plant Resposes to Environmental STress in Arabidopsis. Even though the environment is changing rapidly, investment in plant research has declined. Abiotic and biotic stresses will function via core response networks embellished with stress-specific pathways. A fundamental component of these responses is transcriptional change. It seems that in many of the components in stress responses, hormones are key: also, everything seems to focus through key pathways. Two approaches are used: top-down modelling via network inference, or bottom-up modelling via already extant knowledge of key genes. This talk focused on the former.

They used high-resolution time-course microarrays which use 31,000 genome sequence tags (you need these to get the information to the modellers). Then, they use a range of different stress response to reveal commonalities (developmental e.g. senesence, pathogens, and abiotic stress). One example: over 48 hours there were 24 time points taken with 4 biololgical and 3 technical replicates. Two-color arrays allow complex loop design. They've been using the MAANOVA program, and even altered it to make it more efficient. You basically end up with an f test that tells you which genes have changed over time. How to select genes for Network Inference Modeling?: GO annotations, genes known to be involved in stress-related processes, trancription factors known to be involved, early response genes and prior knowledge.

There goes the battery again! Grrr…. transcribed paper notes follow, which aren't generally as detailed in my case…

Vairation of network models: 4 out of the 12 prospective genes shown to have altered pathogen growth phenotype. Knockouts in a hub gene showed both up or down-regulation of senesence. They want to add validation to the network model, and have validated various genes via experimental work). Developed APPLE, which is tha tAnalysis of Plant Promoter-Linked Elements. Discovered if overexpress HSF3 the plants are more tolerant to drought and show increased seed yield. HSF3 is part of the stress response but has a wide range of interactions, which is a good thing for building parameterized models. In the future, wants to look at the genetic diversity in the crops, and try to express a more robust response to the environment.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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From genes to jam: modellilng A.thaliana root growth, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Presented by 4 people from CPIB.

Malcolm Bennett: This plant is a good choice because the morpology is simple, the development well-understood, the imaging technology required for the work is available, and multi-scale modelling is possible. Microfibrils are stopping the cells from growing radially. What are the mechanisms of plant cell expansion? Cell walls are made up of 3 components: cellulose microfibril skeleton, hemicellulose and GAX which cross-link the microfibrils, and pectins and RGI/RGII form the cell wall matrix.

Tara Holman: They divide the root into 5 developmental zones: meristem, accelerating elongation, decelerating elongation, mature, rest of root / lateral root emergence zone. The XET/XTR family function in the loosening of cell walls by allowing slippage of hemicellulose relative to cellulose microfibrils. There are two distinct clades of this family that are elongation specific (based on microarray data). They have transcriptomic data on all 5 areas, and are currently analysing it. They can track changes in expression of cell wall-related loci, such as XETs, and have a large amount of molecular-scale data.

Rosemary Dyson: But how do these changes contribute to root growth? Which factors are actually important? mechanics of root cell growth: cell has high turgor pressure, which is regulated very quickly by the osmotic potential. The TP also exerts a tension in teh cell wall, if tension is greater than a certain yield stress the wall will creep and exhibit irreversible growth. The degree of creep is controlled by varying the cell wall properties (e.g. viscosity). Current models are variations on the 1965 Lockhart model. Modelling assumptions are: approximate cell as a pressurized hollow cylinder with rigid end plates; model the cell wall as consisting of fibres embedded within a ground matrix; assume the wall is permanently yielded, therefore a viscous fluid; exploit the geometry – the cell wall is much thinner than the radius of the cell so can employ asymptotic analysis. It's just like glass blowing… πŸ™‚ She wrote everything in terms of a moving curvilinear coordinate system, fixed within the moving sheet. Where the centre surface is and the thickness of the sheet form part of the solution. She can also decompose the total fluid velocity, U, into velocity of the centre-surface v, and the fluid velocity relative to the moving v so that U = v + u. Everything is a function of the length along the cylinder, s, and time, t, only. Initial conditions are height, radius, angle of fibres, and length of fibres. There were many more functions here, which were very nicely described, but which are impossible to reproduce in these notes πŸ™‚

Darren Wells: takes the equation/model produced by Rosemary and validates it and looks for best-fit numbers. One of the variables that can be measured directly is turgor pressure (via a micropipette filed with silicon oil and some fancy shenanigans). Pressure is about 3 – 3.5 bar normally (about like tyre pressure). Experimental evidence shows generally that you can assume a constant turgor pressure across the 5 areas of the root, though there may be some slight variation. Conventional fixation techniques can lead to errors of up to 100% in estimation of cell wall thickness. You can solve that with Freeze Fracture, but there is no spatial localization with that. Instead, use cryo FIB-SEM and high-pressure free substitution (beautiful elecron microscopy image!). Discovered that walls are thinner when next to another cell, and thicker at the corners, so difficult to measure thickness. In terms of the growth rate (relative to tip velocity) parameter,  it requires dynamic measurement at cellular resolution. Can use confocal microscopy and image analysis techniques, which give cell lengths and diameters "for free". Vertical imaging under physiologically relevant conditions. The final parameter is viscosity, where direct measurement would require novel techniques, e.g. the development of the micro-rheometer (haven't made it, but might build it). A couple of indirect estimation techniques are possible, however. Here's where the jam comes in, with mimetic cell walls using pectin.

All 4 presenters were clear and interesting and nicely joined together, however I particularly liked the modelling section (3rd part) of this talk. Nice use of LaTex!

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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Thomas Nowotny and Pheromones in Moths, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Sensitivity, specificity and ration coding: riddles of the pheromone system in moths. PheroSys project – the neurosciences face the same problem as others in biology, which could be solved by systems biology. Their model for pheromone reaction goes from Antenna -> Antennal Lobe -> mushroom body (involved in recognition and loading) -> pre-motor areas. Can we find the optimal coding strategies? Moths have an extreme specificity and sensitivity to these pheromones.

There are three Work packages. WP1 (Antenna inputs into AL) includes: single olfactory receptor neuron (ORN), which models ORN responses based on cellular processes; population of pheromone-responsive ORNs, where the aim is to describe response patterns of ORNs and correlate with PNs (projector neurons, which have access to many, if not all, of the receptor neurons (RNs)); projection of ORNs and macro-glomerular complex (MGC) organizations, where the aim is to describe the structure of the MGC. WP2 (organization and function of one glomerulus) includes: neuron types and their structure-function relationship – describe electrical properties, characterise projectoin neurons and local interneurons. WP3 (MGC network) includes: investigating the role of oscillations and extracellular recordings in Agrotis ipsilon (multi-neuron correlations).

These notes were transcribed from hand-written ones as my battery had died, therefore they aren't as complete as they would otherwise be.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. πŸ™‚

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SyMBA Demo causes pondering: how should a bioinformatician choose their output format(s)?

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

SyMBA Demo. The lunch hour was also the demo hour. People came to visit me at the SyMBA demo desk for the whole hour, and we had some interesting conversations. There is one particular question I would like to relate from that hour: what should a bioinformatician choose as an output/export format for multi-omics data? This post relates my thoughts about this challenge. It's not meant to be comprehensive: just some ramblings.

I solve this challenge in SyMBA by storing everything as FuGE objects, which can be exported to FuGE-ML. FuGE-ML can be converted into ISA-TAB and into an html format that mimics ISA-TAB using an XSLT. Therefore, because of this interlink between FuGE and ISA-TAB, you can leverage two complementary formats.

But to a bioinformatician who has just been tasked with building an application (and generally on a short time-scale), how do they choose what export format to use, e.g. FuGE or ISA-TAB? There are considerations of:

  • scale: lightweight or heavyweight implementation. A lightweight implementation might favor your own version of ISACreator and the use of ISA-TAB, or a FuGE-based archive (but not a full-blown LIMS) like SyMBA. A heavyweight solution might be a full LIMS such as PIMS, or another FuGE implementation in development called SysFusion.
  • intent: what is the purpose of storing this data? Is it for later analysis? For later deposition to a public database, e.g. at the EBI? Is it archiving? Is it a combination of these things? Your intent will shape what type of application you build, and what formats you focus your effort on. If your intent is storage only, choose whatever is most convenient for your users. However, these days there is always some aspect of data sharing or publishing. If you need further analysis of the data, then you probably want to be able to produce a computationally-friendly format such as XML. If your intent is submission to public databases, you need to ensure you export in a format they import.

Unfortunately, what this means is that the decision depends on the circumstances. FuGE and ISA-TAB are linked, and so you really get two for the price of one with those. I see this sort of thing as a positive – you have a choice as to the representation, storage and export of your data – a choice of formats! And many, like FuGE and ISA-TAB, are going to be easily convertable. The choice depends on your needs, but there is one easy choice: use something that's already been developed – don't reinvent the wheel!

Anyone else have any further suggestions?

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