Sysbio postdoc fellowship: spatio-temporal control of cell-cycle regulation

Funding is available for a 3 year postdoctoral fellowship to study spatio-temporal control in cell-cycle regulation. This is a join project between our group at the EMBL-EBI and the Quantitative Cell Biology group headed by Silvia Santos at the MRC Clinical Sciences Centre in London. More information about the groups interests can be found in the respective webpages.

The main objective of this project will be to study how the spatial and temporal control of key cell-cycle proteins change in different biological contexts. Examples of these different contexts include different differentiation states and/or different species.


We are looking for candidates that are interested in doing both experimental and computational work and previous experience in  cell biology, microscopy, programming, image analysis and/or modelling of dynamical systems are all considered an asset. We will consider candidates that have a stronger expertise on either experimental or computational methods but are interested in learning and using both approaches. Additional information and application link is here with a closing date of 24 November 2013. We are available for further clarification in regards to suitability of background or information about the projects.

Systems Biology versus "real" biology

Scientific American has an article about this years' Lindau meeting of Nobel Laureates. It features an interesting conversations between Tim Hunt, Roland Pache (at the time PhD student) and undergraduate Sophia Hsing-Jung Li.
Here is the video of the conversation:
The discussion centered around systems biology and Hunt was not shy about expressing his skepticism. Since I happen to see great value in both the Omics and the design principles sort of work that characterize systems biology my frustration grew quickly. The whole video can be neatly summarize by Hunt's advice that people working in systems biology should "spend plenty of time talking to real biologists".

Real biologists ? ... I felt like writing a long rant about the findings that were made possible by the sort of work that he his so skeptical about but then I thought about xkcd and relaxed a bit:

Genetic interactions in powers of ten

During my PhD at EMBL I attended a talk by Peer Bork where he said that computational biologists have the luxury of being able to work at any level of biological organization (atoms, cellular interactions, organism, ecosystems, etc) . At the time his lab was starting to work with metagenomics and his talks would cover the whole range of topics from protein domains to ecosystems. This idea of studying biology across this different scales reminded me of a very inspiring short movie entitled "Powers of Ten" (Wikipedia entry). This 1977 short movie was commissioned by IBM and it was written and directed by Ray Eames and Charles Eames. It takes the viewer on a journey in space from the very small atomic resolution to the outer reaches of the universe in incremental steps of powers of ten. Its only about 10 min long and if you haven't already seen here is below the embed version (while it lasts):


With all the different applications of genetic interaction screening going on here in the Krogan lab we though it would be interesting to write an essay that would, in the same spirit of this short movie, take the reader on a journey across different scales of biology. The essay was just made available online and I hope you enjoy the ride :).

We hope it serves as a tutorial for people interested in using genetic interaction data. There is more and more of this sort of data being deposited in databases and only a small fraction is being used to its fullest potential. We tried to show several examples of concrete findings that were first hinted by genetic interaction data.

Additionally we were trying to make the point that developments in high-throughout methods are decreasing the limitations of what can be observed in biological systems using the same methodologies. This is interesting because it challenge us to build models that can explain biological systems across different layers of biological organization. How does a change in DNA propagate across these layers ? Can it change the meaning of a codon, impact on a protein's stability/interactions, affect the action potentials in a neuronal cell and how species interact ? As we increase our capacity to monitor biological systems we should not only be able to tackle specific layers (i.e. understand protein folding) but we will eventually be concerned with coupling this different models to each other.

Drug synergies tend to be context specific

A little over a year ago I mentioned a paper published in MSB on how drug-combinations could be used to study pathways. Recently, some of the same authors have now published a study in Nature Biotech analyzing drug combinations under different contexts (i.e. different tissues, different species, different outputs, etc).

The underlying methodology of the study is essentially the same as in above mentioned paper. The authors try to study the effect of combining drugs on specific phenotypes. One example of a phenotype could be the inhibition of growth of a pathogenic strain. Different concentrations of two drugs are combined in a matrix form as described in figure 1a (reproduced below) and the phenotype is measured for each case. Two drugs are said to be synergistic if the measured impact on the phenotype of the combined drugs is greater than expected by a neutral model.

The authors ask themselves if drug synergy is or not context dependent. This is an important question for combinatorial therapeutics since we would like to have treatments that are context dependent (i.e. specific). The most straightforward example would be drug treatments against pathogens. Ideally, combinations of drugs would act synergistically against the pathogens but not against the host. Another example would be drug combinations targeting the expression of a particular gene (ex. TNF-alpha) without showing synergy at targeting general cell viability.

In order to test this the authors performed simulations of E.coli metabolism growing under different conditions and a astonishing  panel of ~94000 experimental dose matrices covering several different types of therapeutic conditions. In each experiment, two drugs are tested against a control and a test phenotype and the synergy is measured and compared. The results are summarized as the synergy of the two drugs in the test case and the selectivity of this synergy towards the test phenotype. In other words, for each experiment the authors tested if the synergistic drug pairs in the test phenotype (ex inhibition of growth of the pathogen) are also acting in synergy on the control phenotype (ex. inhibition of growth of host cells).

I reproduce above fig 2b with the results from the flux balance simulations of E.coli metabolism. In these simulations "drugs" were implemented as ideal enzyme inhibitors that reduced flux of their targets. Each cross on this figure represents a "drug" pair targeting two enzymes of the E.coli metabolism.  The test and control phenotypes are, in this case, fermentation versus aerobic conditions. In this plot the authors show that synergistic drug pairs under fermentation tend to have a high selectivity for that condition when compared to aerobic conditions.

The authors then went on to show that this was also the case for most of the experimental cases studied. Some of the experimental cases included cell lines derived from different tissues, highlighting the complexity of drug-interactions in multicellular organisms. These results are consistent with the observation that negative genetic interactions are poorly conserved across species (Tischler et al. Nat Genet. 2008, Roguev et al. Science 2008). Although these results are promising, in respect to the usefulness of combinatorial therapeutic strategies, they emphasize the degree of divergence of cellular interaction networks across species and perhaps even tissues. I am obviously biased but I think that fundamental studies of chemogenomics across species will help us to better understand the potential of combinatorial therapeutics.

There are several examples in this paper of specific interesting cases of drug synergies but most of the results are in supplementary materials. Given that most of the authors are affiliated with a company I expect that there will be little real therapeutic value in the data. Still, it looks like an interesting set for anyone interested in studying drug-gene networks.

Lehár, J., Krueger, A., Avery, W., Heilbut, A., Johansen, L., Price, E., Rickles, R., Short III, G., Staunton, J., Jin, X., Lee, M., Zimmermann, G., & Borisy, A. (2009). Synergistic drug combinations tend to improve therapeutically relevant selectivity Nature Biotechnology, 27 (7), 659-666 DOI: 10.1038/nbt.1549

Drug-drug interactions and network connectivity

How does the effect of drug-drug combinations relate to the cellular interactions of their targets ? Last year, Joseph Lehár and colleagues published a paper in MSB looking into this question.

One way to study the effect of drug combinations on growth of a bacteria for example is to measure the inhibition of growth of all possible combinations of serially diluted doses of two combined drugs and plotting dose-matrices like the ones shown in figure 1 of the paper and shown here adapted from the paper. In fig1A the authors show how the combined effect of increasing doses of two drugs inhibit the growth of a methicillin-resistant Staphylococcus aureus strain. Light colors are equivalent to a strong inhibition of drug. One observation from this figure is that the two drugs can inhibit the growth of this strain in an additive fashion. The question the authors tried to address in this paper is how much does this sort of dose-matrix inform us about the possible interactions of the targets. The drugs could be interacting with the same target, different targets in the same pathway/complex, targets in different pathways both required for growth, etc.

In order to study this they first simulated an abstract metabolic network (using ODEs, see model file in Sup) with two different pathways required for growth, with branched and linear blocks and one negative feedback (see Fig3 in the paper). They simulated the effect of increasing drugs in their models by decreasing the enzyme activities of the simulated targets. For each possible drug-drug combination they then calculated the predicted dose-matrix effect on growth (pathway output). The observed that by fitting the obtained dose-matrices to 4 types of classical dose-matrix models (described in Fig2) they could predict where in this network the two targets would more likely be.
As an example , two sequential targets in an unbranched section of the network embedded in an negative feedback produces a dose-matrix that best fits a potentiation model (shown here, adapted from Fig3).

Having established by simulations that there is information on the drug-matrices that relate to the interaction of their targets they then tested the effect of 10 known antifungal drugs on the sterol pathway (also well established) of Candida glabrata. For each drug-drug combination they tried to fit the experimental dose matrices to the same 4 models and compared the best model fit to the expected for the position of the targets in the sterol pathway. For many cases (72%) the best model fit was the same as predicted from the sterol pathway model but only 54% of the best-fit models were unambiguous. There were some cases were drug-with-itself dose matrices (positive control) did not appear additive as expected. The authors mention that this is due to the "instability in the measured potency of a drug" but I am not sure why a drug-with-itself matrix would not be reproducible.

Finally the authors further tested this relation between drug combinations and target interactions by experimentally measuring drug dose-matrices for 94 drug/compounds in human(HCT116) tumor cells (see text for details).

In summary, even if the prediction accuracy is far from perfect, this work shows that it should be possible to either:
1 - use known pathway models plus drug dose-matrices to improve prediction of the most likely targets of the drugs
2 - use known drug-target relationships plus the drug dose-matrices to predict the network connectivity

One obvious complication is the multiple drug targets for the same compound that would reduce the usefulness of the predictions. Some interesting extensions could be to test drug-drug interactions in KO strains or in combinations with RNAi knock-downs
or protein over-expressions.

ICSB 2007

I am attending the eighth International Conference on Systems Biology (ICSB 2007) in Long Beach. I typically prefer smaller conferences but this one is probably the best one to get an overview of the recent progress in systems biology. As expected the program has a broad scope and unlike last year's meeting there are no parallel sessions so I will have a chance to ear more from others fields. Any other bloggers attending ?

First issue of IET Synthetic Biology

The first issue of (yet) another journal related to systems&synthetic biology is now online. IET Synthetic Biology will be freely available during this year. This issue covers several works from iGEM and the editorial is worth a read to have a look at the future direction of the journal.

In addition to conventional research and review articles, we see an important need for practical articles describing technical advances and innovative methods useful in synthetic biology. We will encourage submission of technical articles that might describe novel BioBrick components, construction techniques, characterisation of a new biological circuit, new software or a practical ‘hands-on’ guide to the construction of new instrumentation or a biological device.

In addition to the print journal, we are developing associated web resources. These will include a repository of online video resources, specialised review material and research tools for synthetic biology.

Some journals tracking similar fields:
Molecular Systems Biology
BMC Systems Biology
Systems and Synthetic Biology
HSFP Journal
IET Systems Biology

Structures in Systems Biology (a double bill)

Once in a while I get to write about what I have been working on. The last time it was about the evolution of protein interaction networks. This time it is about two papers that I contributed too. A review about the use of structures in systems biology and an article about structure based prediction of Ras/RBD interactions. I am sorry to say that both require a subscription (pedrobeltrao *at* gmail).

Main conclusions
Structural data can be used to predict Ras/RBD interactions with approximately 80% accuracy
We can and should use structural information to understand the main molecular properties before abstracting away the atomic details. Structural genomics can serve as a bridge between the abstract network view and the atomic detail.

The Making off
Although I am not the first author of the article I think it is safe to say that the main inspiration for the line of work done by Kiel (see also previous publication) is the work by Aloy and Russell where they first showed that it was possible to use a protein complex to predict if similar proteins would be able to interact in a similar way. What Kiel showed is that more accurate predictions can be made by modeling the protein domains under test onto the complex and evaluating the binding energy using a protein design program under development in the lab (FoldX). She used pull-down experiments and available information on Ras/RBD interactions to benchmark the predictions.

The predicted binding energies inform us about the probability that the two protein domains would bind in vitro. Inside the cell there are many other factors contributing to the likelihood of binding (gene expression, localization, complex formation, post-translational modifications, etc). To try to add some of this knowledge to the predictions I contributed with a Naive Bayes predictor that combines information on gene expression, GO functions, conserved physical/genetic interactions in other species and shared binding partners. The likelihood score obtained can be used to further rank the predicted interactions according to the likelihood that these are occurring inside the cell. In supplementary information there are the methods and tables with individual likelihood scores that can be used to reproduce the Naive Bayes predictor.

From atoms to nodes and edges
I think one of the main goals of the the review was to show the current progress that has been made in using structural information to obtain the fundamental properties (binding sites, catalytic sites, protein dynamics, etc) of cellular components that may allow us to create models of cellular functions. There has been some work in approximating the very abstract "nodes and edges" view of cellular interactions to a more traditional pathway model. This has been done typically by searching for modules and particular node roles that depend on the patterns of intra or inter module interactions (see Guimera et al). We should be able to automatically decorate interaction networks (and the pathway modules) with structural data that can further help to computationally generate meaningful models of cellular functions.
The picture was obtained from Beltrao et al , it is Copyright © 2007 Elsevier Ltd and it used here hopefully under fair use.

In the pipeline
There are several important details to iron out before we can just apply this structure based prediction of protein interactions to any protein that we can model onto complexes. We are in the process of testing the approach with other different domain types. Some of if I have been more directly involved and we started now the submission process. I tried to get everyone to agree to submit it to a preprint server but not everyone was comfortable with the idea.

Nature vs. Nurture in personalized medicine

Personalized medicine aims to determine the best therapy for an individual based on personal characteristics. Given that the family history is a risk factor for many diseases there is a strong motivation for the search of inheritable genetic variation that might provide molecular explanations for diseases. In the last couple years, improvements in sequencing technology have helped to scale up these efforts. The HapMap project is an example of these attempts at genome wide characterization of human genetic variation. The project aims to create a haplotype map of the human genome. This map is important because correlating a disease with a haplotype can be used to pin-point the cause of a disease to a genome region. This map based approach is done by first sequencing known sites of polymorphisms, spaced across the genome, in a large population and then associating disease with haplotypes (see a recent example).

Eventually sequencing costs will go down to a point when these map based approaches are replaced by full genome re-sequencing. It looks like there is a consensus that this is just a matter of time. Also, the main sequencing centers seem to be directing more of their efforts to studying variation. If sequencing full genomes is currently too expensive, sequencing coding regions is much more affordable. In two recent papers (Greenman et al. and Sjoblom et al.) researchers have tried to identify somatic mutations in human cancer genomes by sequencing. Greenman and colleagues focused on 518 kinases and searched for mutations in these genes in 210 different human cancers (see post by Keith Robison). Sjoblom and colleagues on the other hand sequenced fewer cancer types (11 breast and 11 colorectal cancers) but did so for 13023 genes. The challenge going forward is to understand what is the impact of these mutations on cellular function.
Instead of sequencing to find new polymorphism is also possible to test the association of previously identified variation with disease by high-throughput profiling. Two recent papers focused on profiling known polymorphisms in cancer tissues using either microarrays or PCR plus mass spec.

Underlying all of these efforts is the idea of genetic determinism. That if I sequence my genome I should know how each variation impacts on my health and what treatment I should use to correct it. It begs the question however of much does it really depend on inherited genetic variation ? The often re-visited Nature vs. Nurture debate. The latests MSB paper highlights the impact of the environment on mammalian metabolic functions. Fracois-Pierre J Martin and colleagues have studied how the microbial gut population affects the mouse metabolism. They have used NMR metabolic profiling in conventional mice, and germ free mice colonized by human baby flora to study this question.

Metabolic analysis of liver, plasma, urine and ileal of both types of mice showed a significant change in metabolites in the different compartments associated with the two microbial populations. This is a very clear example of how the environment must be taken into consideration for future efforts of personalized medical care.

This example also underscores the importance of studying the human microbial associations. As Jonathan Eisen discussed in his blog, maybe we should aim at a human microbiome program.

Nature or Nurture ? In either case, abundant streams of data are forthcoming as the sequencing centers crunch away and new omics tools get directed at studying disease. There will be a lot of work to do in order to understand causal relationships and suggest therapeutic strategies. That might be why Google is taking a look at this. They keep saying they want to organize the worlds information, why not health related data.


The picture was taking from News and View by Ian Wilson:
Top-down versus bottom-up—rediscovering physiology via systems biology? Molecular Systems Biology 3:113

Introducing the Systems Biology department at CRG

I am spending two weeks in Barcelona to help out with a referee report. I can't really say yet what it is about but if everything goes well, maybe I will in a couple of months (hint: evolvability). What I can do is introduce the environment. I am in the 5th floor of the Barcelona Biomedical Research Park. The building is located in front of the sea and it hosts several different institutes. I am staying at the Center for Genomic Regulation (CRG) where my supervisor Luis Serrano is heading the program for Systems Biology. The program is a partnership between CRG and EMBL and it currently is home for four groups (Luis Serrano, James Sharpe, Mark Isalan and Ben Lehner).


The department has a lot of research in development and evolutionary systems biology. I have only been here a week but the environment is great and the beach in the background is a killer plus. Have a look around the webpage for the other programs.

Systems and Synthetic biology RSS pipe

Here is the RSS feed for a Yahoo pipe combining and filtering papers mostly about synthetic and systems biology. There are three systems biology journals directly combined into the feed. Unfortunately I could not find the RSS feeds for IET Systems Biology so it is not included. On top of these are added selected papers from Nature tittles, PLoS titles, Cell, PNAS, Science and Genome Biology. The filtering is done using some typical key words that might be associated to Systems and Synthetic biology. Here is a simple illustration of how it works:
I still have to test the pipe for some time and tweak the filters, but it is enough to get an idea of the things that can be done with these pipes. Like the pipe before you can clone this and change the filters and journals as you like.

in sillico network reconstruction (using expression data)

In my last post I commented on a paper that tried to find the best mathematical model for a cellular pathway. In that paper they used information on known and predicted protein interactions. This time I want to mention a paper, published in Nature Mol. Systems Biology, that attempts to reconstruct gene regulatory networks from gene expression data and Chip-chip data.

The authors were interested in determining how/when transcription factors regulate their target genes over time. One novelty introduced in this work was the focus on bifurcation events in gene expression. They tried to look for cases where a groups of genes clearly bifurcated into two groups at a particular time point. Combining these patterns of bifurcation with experimental binding data for transcription factors they tried to predict what transcription factors regulate these group of genes. There is a simple example shown in figure 1, reproduced below.


In this toy example there is a bifurcation event at 1 h and another at the 2h time point. All of the genes are assigned to a gene expression path. In this case, the red genes are those that are very likely to show a down regulation in between the 1st and 2nd hour and stay at the same level of expression from then on. Once the genes have been assigned it is possible to search for transcription factors that are significantly associate to each gene expression path. For example in this case, TF A is strongly associated to the pink trajectory. This means that many of the genes in the pink group have a known binding site for TF A in their promoter region.


To test their approach, the authors studied the amino-acid starvation in S. cerevisiae. In figure 2 they summarize the reconstructed dynamic map. The result is the association of TFs to groups of genes and the changes in expression of these genes over time during amino acid starvation.

One interesting finding from this map was that Ino4 activates a group of genes related to lipid metabolism starting at the 2h time point. Since Ino4 binding sites had only been profiled by Chip-chip in YPD media and not in a.a. starvation, this is a novel result obtained using their method.

To further test the significance of their observation they performed Chip-chip assays of Ino4 in amino acid starvation. They confirmed that Ino4 binds many more promoters during amino acid starvation as compared to synthetic complete glucose media. Out of 207 genes bound by Ino4 (specifically during AA starvation) 34 were also among the genes assigned to the Ino4 gene path obtained from their approach.

This results confirmed the usefulness of this computational approach to reconstruct gene regulatory networks from gene expression data and TF binding site information.
The authors then go on to study the regulation of other conditions.


For anyone curious enough about the method, this was done using Hidden Markov Models (see here for available primer on HMMs).

In silico network reconstruction

It is day one of Just Science week and I want to tell you about a recent paper that was published in BMC Systems Biology by Rui Alves and Albert Sorribas. It is about a general approach to integrate information to come up with models for cellular pathways. What does this mean and why is this important ?

Increasingly the scientific knowledge is being stored in databases (literature, protein structures, gene expression, protein-protein interactions, protein-DNA interactions, etc). The general idea behind the work described is that we should be able to use the accumulated information about cellular pathways to extract models of how the cell's components interact to preform their functions. By models I mean a formal representation that can tell us how the components' concentrations and activities change with time.

There are several works already dealing with this problem of trying to reconstruct cellular networks from large data sources but I found this article particularly interesting because it uses so many of these methods.

To give you an idea I reproduce below figure 4 of the paper with a diagram of the method (click to zoom in):




The authors have pulled in experimentally known interactions and combined them with putative interactions obtained from docking and phylogenetic based predictions. These predicted networks are then converted to several possible mathematical models that are examined under different parameter conditions and compared with known experimental values.

This method should be particularly suited for a case when some of the genes in the pathway are known and there are experimental measured outputs for the pathway that can be compared with the predictions from the putative pathway models.

Ideally this whole procedure would be fully converted into an automatic pipeline that could be used by people that are not so familiar with the tools.

I will try to stick with the same theme during the week, hopefully covering different methods to achieve the same thing.

System Biology quick links

(via Pierre) BMC System Biology has published their first papers. More or less at the same time the new Systems and Synthetic Biology (published by Springer Netherlands) has started publishing papers. These two journals join IEE Systems Biology and Molecular Systems Biology (Nature/EMBO) as forums to publish works on Systems and Synthetic Biology. All journals (with the exception of IEE Systems Biology) publish in open access or at least (in the case of Systems and Synthetic Biology) offer an open access option.

Some of the talks from the BioSysBio conference are online in Goggle Video.

Here is a nice talk from Alfonso Valencia talking about species co-evolution and a very promising improvement to a sequence based method to predict protein-protein interactions: