University of Cambridge
Keynote Talk 1
It is not unexpected that Steve Oliver chose to talk about yeast, and calls it the perfect eukaryotic model organism. You can look at it in a coarse-grained, top-down approach. This approach includes research using Metabolic Control Analysis (MCA), which he calls a "shortcut to modelling metabolism". The central device of MCA is the Flux Control coefficient, which is a measure of the degree of control an enzyme has on a pathway flux.
You can change the concentration of gene products and measure the impact on flux. With this in mind, they have in the past created a deletion mutant for each of the protein coding genes in yeast. After insertion of the disruption cassette, you can sporulate and then perform tetrad dissection: if it is an essential gene, then there will not be any growth. If there is growth, it is non-essential and you have a colony containing the knockout.
You can look at the changing proportion of different mutant types in a population. They looked at competition between heterozygous and hemizygous mutants, and looked at the differences in growth rate. They found that, in pairwise comparison between the different condition, if they were haplosufficient in one condition they were sufficient in the others. The grape juice condition looked like nothing else: at the top of the list of haploinsufficient list had to do with the transport of pyrimidines. Haploproficiency (HP) is much more context/condition-dependent. Virtually every gene coding for the 26S proteasome were in the list for N-limited haploproficiency.
Haploinsufficient genes were vastly overrepresented in chromosome III irrespective of the type of limitation (except for one). It's the chromosome that determines the sex. The sexes are "a" and "alpha". There are three types: amphimixis (maximises chances of heterozygosity), haplo-selfing, and automixis/intratetrad mating. Haploinsufficient (HI) genes are more similar to their pre-duplication ancestors than their "ohnologs". The haploinsufficient gene is probably the ancestral version of the pair. K.lactis MAT chromosome III is also enriched for HI orthologs.
You can also do experiments which change the flux and measure products of gene action. This work was done in what his lab called "The Big Experiment", and makes use of a chemostat, from which we can get information about the transcriptome, metabolome, and proteome. These measure transcription changes that are wholly due to the change in flux. 493 genes were upregulated with increasing growth rates, and 398 genes were downregulated with increasing growth rates. Looking at the GO terms for those genes were were upregulated with growth rate include ribosome biogenesis and protein biosysthesis. Only 47 of the HI genes have their transcript levels under growth control, and only 26 of the HP types.
Is this a universal law, or context-dependent? It was found to be entirely context-dependent. Decided to repeat the composition experiments in a completely different environment: a turbidostat rather than a chemostat. In this case, there was no nutrient limitation. Looking at the HI genes in a turbidostat, they have the very functions that are to do with growth rate (note: I missed part of that point).
While the laws determining which genes control growth rate may change according to the selective conditions… (sorry!). The discovery of HP in nutrient-unconstrained conditions suggests that yeast has sacrificed short-term gain in favor of long-term survival.
Have worked with Ross King and the robot scientist work going on there. They've also started on a logical cell model encoded in Prolog. This is essentially a directed graph, with metabolites as nodes and enzymes as arcs. If a path can be found from the cell inputs to all the cell outputs, then the cell can grow.
They've created an experimental cycle that can be navigated by the robot, which can do some work on hypothesis inference. They removed the labels on the graph, and then used Abductive Logic Programming (ALP) to infer those missing arcs/labels in the metabolic graph. Abduction example follows. Rule: If a cell grows, then it can synthesise tryptophan. Fact: cell cannot grow. Therefore, the cell cannot synthesis tryp.
They tried a number of different experimental strategies (e.g. ALP versus naive versus random). Found that ALP was as good as graduate students who were presented with the same problem. They "closed the loop" wrt hypothesis formation, testing and validation. Original work was proof-of-principle as they got the robot to re-discover existing knowledge. They're now working on the discovering new knowledge.
To do this, they expanded the background knowledge of the robot, improved the efficiency of hypothesis generation, and extended the original qualitative methodology to allow for quantitative measurements. Basic premise was that growth should occur iff there was a path from growth medium to defined end end-points. The robot would then try to fill in the gaps in the model, where there must be enzymes etc to carry out specific steps. One strategy is to find yeast homologs of genes coding for proteins with appropriate EC numbers.
They have some new hardware to be their robot scientist, called Adam. It has the capacity to perform over 1000 experiments per day. You find that most of the genes that encode the enzyymes are often telomere-associated and have paralogs elsewhere in the genome. It's a very convoluted situation that was "unlikely to be solved by classical genetics." Keep an eye out for the upcoming Science paper called "The Automation of Science": the paper should appear in a couple of weeks.
Personal Comments: A great speaker and an interesting talk. Unfortunately wasn't able to capture absolutely everything he said…!
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. Please let me know of any errors, and I'll fix them!
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