The Seven Stones

Impact Factors 2008

2009-06-24T18:19:26Z

The new Impact Factors 2008 were just released by Thomson Reuters (2008 Journal Citation Reports). We are delighted to announce that Molecular Systems Biology continues its progression, with an Impact Factor 2008 of 12.243.

We address a warm thank you to all our authors and reviewers for this wonderful success, which reflects the current extraordinary dynamism and enthusiasm in the fields of systems biology, synthetic biology and systems medicine!

The limitations of the Impact Factors (IF) have been largely discussed. In particular, it might be questionable to use IFs to rank journals with highly variable scopes, audiences and citation patterns. Moreover, article-centered metrics (such as individual citations, number of download, highlights in N&V, etc...) might be more appropriate to evaluate the contributions of individual researchers, rather than solely relying on the proxy provided by journal-based citation indexes. Nevertheless, when considering the variation of IF over time for a given journal, the impact of some of the confounding factors mentioned above might be reduced, at least to some extent. To facilitate exploration of the progression of IFs over the last five years, I include at the end of this post a Google Motion Chart to visualize IFs of a (rather subjective) selection of journals related to the fields of molecular and cell biology.

One observation that becomes apparent when toying around with this visualization, is that relatively few journals–in this selection!–see their IF raising over a period of 5 years, whereas many seem to be subject to a progressive erosion. This is also visible if one clusters the normalized time profiles, showing that the downward profile (in red) is frequent, at least within the selection used for the Motion Chart below (each curve is the cluster's center with a thickness proportional to the number of journals in this cluster):

Why is that? It is hard to know. Perhaps, it might reflect some global effects affecting many journals at the same time: proliferation of new journals, changes in the pattern of citations directed to reviews rather than primary research, shift to citations of medically and clinically-oriented journals to highlight the medical relevance of the citing paper, etc... On the more positive side, those journals with upward progression (green curve above) may provide pointers to particularly dynamic fields.

In any case, given the above global trends, we are even more happy to open a bottle of Champagne to celebrate and enjoy the moment... :-)

For an easy start with the exploration of the data, select 'Impact Factor' for the Y axis, 'Time' for the X axis, color by 'up vs down', 'same size' in the 'size' menu, check a few of your favorite journals (don't forget to click on Mol Syst Biol!) and check the 'Trail' box. Press the 'play' button to start the animation. Interesting visualizations are also possible with the bar chart option (Click on second tab on top). See also instructions on the relevant Google Docs help page. Have fun!
Legend:

'IF': impact factor
'IF-IF2004': the Impact Factor 2004 (or the first available) was subtracted from all the other, to facilitate visualization of the progression
'up vs do': +1 if IF2008>IF2004, -1 otherwise
'cluster #' & 'profile type': 0=undefined because missing values, 1=profiles goes up then down, 2=down then up, 3=down, 4=up

The end of news, the end of reason

2009-03-20T09:36:00Z

Guest post by Holger Breithaupt, Science & Society Editor, EMBO reports, Heidelberg

Aside from what Waldorf & Statler make of the internet, it is the greatest source of information humanity has ever created; larger than the Vatican Archives, the Library of Congress and all public and university libraries combined. And it’s fast. I don’t have to wait for the news on TV or the daily newspaper to tell me about the US government’s latest reaction to AIG’s bonus payments: the internet, in particular the blogosphere or that latest spawn of it, twittering, gives me real-time news, 24 hours a day. Why then, would we still need news on paper, on TV or on the radio?

Given the power of the internet, there are actually not a few who think that it heralds the demise of the newspaper (Newspapers and Thinking the Unthinkable, Shirky, 2009) and even of journalism (Filling the Void, Nature editorial, 2009). Sure, why bother trying to unfold the New York Times during rush hour in the subway to read a 3500-word feature, if I can download 140-character information tidbits on my iPhone? I don’t even have to buy a newspaper or wait for the 8 pm news in the first place: RSS feeds, search engines, ToC alerts or whatever technology spoon-feed me the newsbits that I’m interested in from the sources that I like.

And that’s exactly the problem. As Nicholas Kristof pointed out, we mainly use the internet to reinforce our prejudices and opinions while it makes it easier for us to ignore contradictory arguments (The Daily Me, Kristof, 2009). I myself plead guilty of such behaviour: while I read and enjoy Frank Rich’s column each week, I shunned William Kristol. On the other hand, while I was reading the newspaper the other day, I stumbled upon an article that explained why paying big bonuses to AIG managers who helped run the company aground is not such a bad idea (The Case for Paying Out Bonuses at A.I.G., Sorkin, 2009); I still disagree, but at least I feel I have a better understanding of the issue.

What is at stake here is our ability to reason, which, as I understand it, means forming your own opinion on a given topic–or maybe even changing it–after listening to the diverse pros and cons. Instead, as Kristof noted, the way we use the internet largely serves to harden our pre-formed beliefs unless we deliberately make the effort of searching and reading the arguments we don’t like to hear. Newspapers, TV and radio and good journalism are the antidote: they provide–if they live up to the task–an oversight of arguments and they expose us to topics and opinions that we would just ignore or not even become aware of and thus broaden our horizon. Claiming that they are no longer needed in the brave new world of blogs, social networks and twittering means that we give up an important opportunity to make up our mind.

Keystone Symposium - Omics Meets Cell Biology (II)

2009-02-02T16:28:00Z

Before I carry on with a summary of the second part of the Keystone Symposium 'Omics Meets Cell Biology', I should clarify that this post and the previous one dedicated to this conference are not intended to provide an comprehensive account of all the talks but rather to communicate some general (and subjective) impressions of the meeting. To keep these posts reasonably short (and sometimes due to a lack of memory...), I had to omit several of the excellent presentations given at this meeting. The full program and complete list of speakers is available at the Keystone Symposium website.

Many of the presentations given during the second part of the meeting reported findings derived from cell-based high- or medium-throughput functional screens, most of them relying on RNAi-mediated knock-down. Here is an overview of the screens presented during this meeting, illustrating by their diversity in scope and scale the versatility of this method:

Focus	# genes tested	Type	Speaker
autophagy	21'000?	RNAi	M Lipinski
sensory organ dev.	20'000	RNAi	J Mummery-Widmer
cell polarity	16'000	RNAi	J Ahringer
imatinib modifiers	9500 (pooled)	RNAi	D Sabatini
viral entry	4000	RNAi	L Pelkmans
cell-cell contacts	2000	RNAi	T Pawson
cell migration	1000	RNAi	J Brugge
centrosome	113	RNAi	L Pelletier
bipolar spindle	45	RNAi	R Medema
DNA repair		RNAi	D Durocher
neuronal differentiation	700	TF overexpression	M Snyder
gene-centered TF location		yeast 1-hybrid library	M Walhout
protein degradation		reporter library	S Elledge

Perhaps not surprisingly, many speakers emphasized that RNAi screens invariably need to be followed up by time-consuming and tedious validations. The off-target problem in mammalian cell-based RNAi screens appears also to be taken very seriously and it was reported that from 4-7 siRNA directed against the same gene were necessary to reach a good level of confidence. In view of the increasing number of RNAi-based functional screens, standards for the description of such experiments (eg. MIARE, MIACA) are likely to become increasingly useful.

In systems biology, network models are often central for the interpretations of omics data related to molecular interactions and they allow to generate biological insights which are different from those derived from the more classical screening-mechanistic-dissection paradigm. In this regard, Uwe Sauer presented exciting work on the relationship between transcriptional regulatory networks, protein expression and the state of the yeast metabolic network. Using a combination of genetic approach and drug perturbations, a series of parallel 'fluxomic' and metabolomic measurements revealed that metabolic fluxes, in contrast to metabolite concentrations, remain robust to perturbations and are apparently affected only by a handful of transcription factors in a given condition at steady state. At the computational level, integration of different types of data represents significant challenges. For example, it is far from trivial to find ways to exploit the information contained in interaction networks and integrate it with other types of large-scale molecular measurements. Trey Ideker exposed an efficient solution to this problem within the context of microarray profiling of breast cancers and showed that expression data can be combined with information on protein physical interactions to define improved and biologically meaningful pathway-based biomarkers for the classification of metastatic vs non-metastatic tumors.

While superposing parallel datasets leads to a 'vertical' integration of networks, Marian Walhout presented an approach to integrate 'horizontally' transcriptional and miRNA-dependent regulatory links and map a composite transcription factor/miRNA regulatory network in Caenorhabditis elegans. In this elegant work, the yeast one-hybrid assay was used as a gene-centric screening method to identify regulatory links between hundreds of transcription factors and promoters of both miRNA genes and genes encoding transcription factors. Closing the loop, the network was completed by computationally predicting the transcription factors potentially targeted by miRNAs. Interestingly, the resulting network showed numerous composite motifs including negative feedback loops (TF --> miR --| TF), which are otherwise under-represented in pure transcriptional regulatory neworks.

Completion of network models may require tedious and repetitive work. To the question "who will fill the gaps?", Steve Oliver replied: "a Robot Scientist". He showed that an actual implementation of such a robot is able to iteratively use a computational model of the yeast metabolic network to automatically design informative experiments, perform them and use the results to extend the model. In an effort to provide a genome-scale overview of the molecular interactions that underly regulation of gene expression, Tim Hughes presented a variety of microarray-based technologies to systematically map transcription factor-DNA, nucleosome-DNA and protein-RNA interactions. The latter results were particularly intriguing given that the high-throughput identification of targets of RNA-binding proteins remains a relatively unexplored route and may reveal novel insights into the complexity of post-transcriptional regulation.

To conclude on a somewhat different note, it was also interesting to observe that an increasing number of studies were accompanied by extensive web resources providing access to the respective datasets:

Resource	Lab
PhophoPep	R Aebersold
Human Protein Atlas	M Uhlen
3Dcomplexes.org	S Teichmann
Nature Cell Migration Gateway	J Brugge
EDGEdb.org	M Walhout
CellCircuits	T Ideker
STRING	C von Mering

This situation underscores the need of a proper infrastructure to host and share (or publish?) large datasets in biology and the central role of web technologies in this regard. In view of the proliferation of biological databases, I wonder whether it might be helpful to have general recommendations on some minimal requirements for this type of databases--eg. type of searching, visualization, data integration functionalities, existence of a (web) APIs, download of datasets, possibility to integrate external datasets, etc...? Or would perhaps something like a 'Minimum Information About a Biological Database' be useful to specify the capabilities of databases? One may also dream that these databases will become progressively interoperable and eventually include web-based APIs facilitating programmatic access to the information stored, ultimately sending Omics in the Cloud...

And, oh yes, the slopes were very nice, even though, I have to admit the air was thin and a little fresh...

Keystone Symposium - Omics Meets Cell Biology (I)

2009-01-30T07:17:25Z

At the Keystone Symposium 'OMICS Meets Cell Biology', held this week in Breckenridge, Colorado, attendees had initially to face two major challenges: the first was to survive the cocktail mixing jet lag and altitude sickness and the second one--oh, it hurts!-- was to resist the temptation to just forget all about science and focus exclusively on the concepts revolving around snow, slopes and fun sports...

In any case, those who survived this harsh test were highly rewarded by attending an extremely exciting meeting, organized by Ruedi Aebersold and Tony Pawson, showcasing the impact of genome-wide and high-throughput technologies, the so-called 'omics', in cell biology.

After the two first days of the meeting, dedicated to 'cell signaling' and 'sub-cellular organization', a series of impressive talks had already delivered a clear and strong message: beyond generating comprehensive 'part lists', omics data lead to important and novel biological insights when integrated with functional and phenotypic data and when applied in experiments addressing well defined aspects of the biology of the system under study. This was particularly well illustrated in the talks dedicated to signaling, which all reported on analyses of well defined systems: ephrin-Eph receptor bidirectional signaling in cell-cell contact (T. Pawson), insulin signaling and growth regulation (E. Hafen), notch signaling and sensory organ development (J. Mummery-Widmer), cytokines and hepatotoxicity (B. Cosgrove), Rho signaling & cell migration (C. Bakal).

I have the feeling that this transition from descriptive catalogs to functional and mechanistc insights can be envisioned as the result, at least in part, of two series of developments:

First, experimental design is evolving and an increasing number of projects combine and integrate functional readouts with genetic approaches and high-throughput molecular measurements. For example, Tony Pawson illustrated how the integration of quantitative (SILAC) proteomics, phenotypic siRNA screens and protein complex identification could shed light on the components and mechanisms involved in ephrin-Eph receptor bidirectional signaling and their impact on cell-cell contacts. A combination of quantitative proteomics and genetic approaches was illustrated by Ruedi Aebersold, whose lab is charting a comprehensive kinase-substrate network in yeast by systematically performing quantitative proteomics on deletion mutants of all kinases and phosphatases. Other experiments link even more intimately, by design, systematical perturbations and molecular measurements to phenotypic outcome. Ben Cosgrove presented such work in the context of the study of drug hepatotoxicity. Systematical measurements of the phophorylation status of 17 signaling proteins and monitoring of cell death rates were performed in HepG2 cells under a variety of cytokine stimulation conditions. Multi-variate statistical analysis enable then to construct correlative models, which have not only predictive power but also reveal key players in the process and provide insight into how signaling components contribute to the phenotypic outcome. The power of data integration was also beautifully demonstrated in the work of Jennifer Mummery-Widmer, who performed genome-wide and tissue specific RNAi screens in Drosophila to identify modifiers of the notch signaling pathway. Integration of the genes identified in the screen with a map of known genetic and physical interactions resulted in a network model whose predictive power was exploited to identify and validate in vivo novel regulators of notch signaling.

Second, the technological platforms are maturing, data quality is increasing and protocols are streamlined, making these technologies progressively more accessible. This might be particularly to relevant for mass spectrometry proteomic approaches, which were omnipresent in the signaling talks. One of the consequences of a relative and progressive 'democratization' of MS proteomics platforms is that their application is not obligatorily restricted anymore to an initial exploratory phase traditionally aimed at providing an unbiased view of a particular system, but can now also be engaged in follow-up, often more focused, investigations to gain deeper mechanistic insights. An example of this was provided by Ernst Hafen who presented his work on growth regulation in Drosophila and showed data on a genome-wide and tissue-specific mutagenesis screen aimed at the identification of modifiers of growth regulation. Selected hits of the screen were then analyzed further in time course experiments upon insulin stimulation and mass spectrometry identification of TAP co-immunoprecipitated protein complexes could reveal the nature and dynamics of signaling complex assembly. One can thus predict that further development of optimized omics technologies for targeted follow-up experimentation will have a profound impact in molecular and cell biology.

Mass spectrometry based proteomics was clearly one of the predominant platforms in many of the studies presented during the sessions devoted to signaling. It was therefore particularly fascinating to listen to Mathias Uhlen's talk, who emphasized the need for complementary approaches based on affinity probes and presented foundational work towards antibody-based proteomics. The scale of the this work is such that it is hardly possible to summarize it in just a few sentences. Fortunately, the resource resulting from this enormous effort can be consulted directly online at the Human Protein Atlas portal. I will only add that Mathias Uhlen estimated that this resource will be able to provide quality controlled antibodies for 50% of human proteins within the coming years and that a first draft of the complete human proteome might be ready around 2014!

Beyond omics based on high-throughput measurements at the molecular level, one very exciting development is the application of imaging techniques for automated measurements of cellular and cytological parameters. Lucas Pelkmans showed that measurements of local cellular features (eg nucleus size, local density, mitotic stage, cell edges etc...) at the single cell level could be correlated to various cellular activities such as viral entry, clathrin distribution etc... He insisted that accounting for such local population parameters may have considerable implications for the interpretation of siRNA screens given the unavoidable heterogeneity of cellular populations. This strategy was then applied in the context of a large-scale siRNA screen for modifiers of viral entry performed on 8 different viruses. Cluster analysis of the resulting hits beautifully reveals a hierarchical 'functional phylogenetic' tree of the various virus strains according to the subset of cellular activities required for their entry. This information could in turn be used for the identification of a novel regulatory mechanism of viral entry essential for most of the viruses tested.

The role of neutral mutations in the evolution of phenotypes

2008-11-25T22:23:16Z

Research highlight by Pedro Beltrao, University of California, San Francisco

In a recent opinion piece, Andreas Wagner tries to reconcile the tension between proponents of neutral evolution and selectionism (Wagner 2008). He argues that “neutral mutations prepare the ground for later evolutionary innovation”. Wagner illustrates this point using a network model of genotype-phenotype relationships (Wagner 2005). In a so-called ‘neutral network’, nodes correspond to distinct genotypes associated with the same phenotype and are connected by an edge if the respective genotypes differ only by a single mutation event (eg point mutation). Examples of neutral networks include different genotypes coding for RNA or protein structures. In this representation, highly connected networks correspond to robust phenotypes that are not very sensitive to changes in genotype. Wagner notes the zinc finger fold as an impressive example of a highly connected neutral network as its structure remains essentially the same even after mutating all but seven of its 26 residues to alanine.

Using this model, Wagner describes how highly robust phenotypes can lead to faster exploration of the genotype space. He further proposes that evolution of innovation occurs via cycles of exploration of nearly neutral spaces (dubbed neutralist regime) followed by a reduction in diversity once a new phenotype of higher fitness is discovered (selectionist regime).

Although these models and ideas were mostly developed using models of sequence to structure relationships, Wagner cites several examples suggesting that these concepts are equally valid for cellular phenotypes that depend on molecular interactions (ex. gene expression patterns).

As Wagner points out, in order to understand the evolution of innovation we must fully understand the mapping between genotypes to phenotypes. This is why it is important to continue to develop richer evolutionary models to link changes at the DNA level with changes in molecular structures, interactions and ultimately phenotypes with a quantifiable impact on fitness. This is an area where systems biology should play an important role.

Models of RNA and protein structure stability upon mutation have existed now for some time (Hofacker et al. 1994, Guerois et al. 2002). More recently the study of large amounts of genomic information and/or systematic interactions studies are providing us with accurate models for different types of molecular interactions (Berger et al. 2008, Burger & van Nimwegen 2008, Chen et al. 2008). In parallel to these, theoretical analysis has been use to aid in the understanding of cellular phenotypes (i.e. cell-cycle, signaling pathways etc) (Tyson et al. 2003). Connecting these different layers of abstraction is an important challenge that will allow us to better understand the origins of biological innovation.

Berger MF et al. (2008). Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences. Cell 133:1266-76

Burger L & van Nimwegen E (2008). Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method. Mol Syst Biol 4:165

Chen JR et al. (2008). Predicting PDZ domain-peptide interactions from primary sequences. Nat Biotechnol 26:1041-5

Guerois R, Nielsen JE & Serrano L (2002). Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320:369-87

Hofacker IL et al. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie / Chemical Monthly 125:167-188

Tyson JJ, Chen KC & Novak B (2003). Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15:221-31

Wagner A (2005). Robustness and Evolvability in Living Systems. Princeton University Press

Wagner A (2008). Neutralism and selectionism: a network-based reconciliation. Nat Rev Genet 9:965-974

RECOMB Systems Biology Conference

2008-10-30T13:49:45Z

The RECOMB Satellite on Regulatory Genomics, RECOMB Satellite on Systems Biology, and DREAM reverse engineering conferences are currently held jointly at the MIT, in Boston.

Some of the talks are currently 'live-blogged' on FriendFeed and can be followed below or in the "Recomb-Sat/DREAM 08" room.

SciFoo: scientific fireworks

2008-08-18T09:30:00Z

In his list of eight 'generative' values (Better Than Free), Kevin Kelly includes 'embodiment'–the actual physical realization of an item or event which could be otherwise freely distributed over the web. While we are all 'hyperlinked' on the Internet, the value of those unique qualities that cannot be generated or "copied" on the web is dramatically increased. The type of intense emulation and shared excitement sparked at the recent Science Foo Camp (SciFoo 2008), organized by Nature, Google and O'Reilly, gave a wonderful example of the unique value of direct human exchange during an exclusive event bringing together roughly 200 top scientists, 'geeks' and other technologists at the Googleplex in Mountain View, California.

SciFoo is a so-called 'unconference': there is no program or more precisely, as Timo Hannay explained during the opening of the conference, the attendees are the 'program'. The actual schedule was defined only on the first evening in a purposefully chaotic process by anyone who wished to organize a session on any topic. For the next two days, in a festival of parallel sessions, astrophysicists, 'googlers', technologists, molecular biologists, taxonomists, game designers, flying car constructors, publishers, thinkers and (some) dreamers discussed and exchanged ideas with great enthusiasm and a rare intensity and openness.

Needless to say that deciding which session to attend was close to impossible... In any case, I ended up following three types of talks: a series on systems biology related topic (data integration, machine learning, personal genomics, baroque structure of the transcribed genome), several (of many) sessions focused on the theme of open data/science and finally some more eclectic sessions (only from my standpoint, of course) on diverse topics such as the foundations of the concept of time in physics, on some demonstration of very simple yet powerful Python scripting exercises to analyze text and the potential of game design to harness our 'cognitive surplus'. I cannot possibly summarize all the talks, interactions and impressions gathered at this meeting, but here are a few subjective excerpts:

There were quite a few sessions on open science and open data. Ernst Hafen made a strong case for the need of a unique AuthorID that would help in tracking the multiple aspects of researchers' scientific activities. With regard to data, Google announced that a new service will soon be launched, Google Research Datasets, offering to host, for free, large datasets of any type. The service will allow inclusion of some minimal meta-data about the submitted datasets and will provide a mechanism to define a delay before the dataset is made publicly visible. This will probably become a very simple and convenient way for storing data (in particular if a useful API is developed), so convenient in fact, that we may have to be a little careful that it will not turn into a temptation to bypass the 'minimal information...' standards usually required by traditional public databases.
George Church provided an overview of the Personal Genome Project (PGP) and described the type of biological data that will be integrated with the genomic and genetic information collected from consenting PGP volunteers: analysis of the transcriptome of pluripotent stem cells derived from the subjects; sequence of the repertoire of recombined V-D-J regions in immune cells ('VDJome') to exploit correlations between given V-D-J sequences and antigen-specific stimulations; characterization of the microbiome used as a tracer of the environmental and physiological conditions; record of phenotypic traits and disease conditions using controlled vocabularies. Finally, George also emphasized the exponentially decreasing cost of sequencing, which will not only make large scale sequencing of full personal genomes feasible but will also potentially open entire new fields of applications based on massive DNA sequencing.
Lee Smolin talked about the nature of the concept of time in physics and investigated the question of whether our perception of time as the 'experience of successive present moments' is 'real' or, alternatively, an emergent property of the laws of physics. I cannot pretend I followed the entire argument, but I learned that the mathematical representation of the physical reality involves the geometrization of time (as one of the state space's dimensions), leading in fact to a representation devoid of temporal flow (somehow the clock has to be outside the system). To this geometrical representation, physical laws are associated and applied to initial conditions. If I did not misunderstand it, it appears that this approach used in physics might have to be considered as approximative because it may only be valid for subsystems of the universe whereas it might not be appropriate for a true cosmological theory of the entire universe, with possibly disturbing consequences on the nature of physical laws...
Believe it or not but music can be 'geekified' as well: Chris diBona, later in the evening, brought his tenori-on for a fun demonstration. I want one of those!

The meeting ended with some final scientific fireworks, when some of the speakers gave a series of brilliant 2 min summary talks, providing a colorful overview of the many sessions we inevitably had missed. I have to admit that I like fireworks and I would certainly have enjoyed having a little more of this final kaleidoscopic view of science. Clearly, the authentic value of this conference lies in the unique and direct human interactions, but I wish there would be nevertheless some way–perhaps by using this last session in some form of outreach action–to disseminate this pure joy of scientific diversity and curiosity to a broader audience.

Credits: illustrations from Bob Lee, Flickr, some rights reserved

Soon Sci Foo!

2008-07-26T00:25:34Z

A last very quick post before going on vacation (Swiss Alps...). In two weeks I will have the great privilege to attend the mythic SciFoo 'un-conference' at the Googleplex in Mountain View, California. Many ideas of exciting sessions are already circulating. I would just like to add my support to Cameron Neylon's proposal for a discussion around the issue of building a 'Science Data Commons'. The availability and 'integrability' of scientific data represent probably some of the major challenges in scientific communication and, obviously, I would be excited to see if, from the discussions at Sci Foo, some ideas will emerge on how scientific journals can take concrete and pragmatic steps to help making scientific data readily available in a useful form.

ISMB 2008: micro-blogging at its best

2008-07-23T20:06:32Z

Probably like many others, I have often been puzzled by the phenomenon of 'micro-blogging', which consists in posting very short messages on the web (typically via sites such as Twitter) with the goal of providing an instantaneous description of the activity, state of mind or thoughts of the writer. The last few days, a small group of bloggers attending the ISMB 2008 Conference in Toronto used a form of collective micro-blogging on FriendFeed in an intensive way to cover many of the talks held at the conference.

Particularly interesting was the coverage of several keynote lectures, often commented simultaneously on a single 'feed' by several bloggers in the audience, providing so to say a real-time example of 'crowdsourcing'. The result is a surprisingly useful set of notes, where the combined attention and complementary knowledge of the participants allow some gaps to be filled, provide additional information (including references or links) and follow the flow of the presentation as it unfolds. I provide below a few picks, relevant to systems biology, while the rest can be consulted (and, importantly, searched!) in the ISMB 2008 Room' on FriendFeed. Good job & many thanks!

The impact of online publishing

2008-07-18T10:13:06Z

"I haven't browsed a table of content in ages; I find all my papers by Pubmed searches anyway". We have probably all heard this remark, which reflects a general trend as how online publishing has changed the way we retrieve scientific publications. In a study published today in Science, Evans ("Electronic Publication and the Narrowing of Science and Scholarship", Evans, 2008) presents data on citations patterns showing that the appearance of electronic publications has been accompanied by a decrease in the number of citations and a progressive restriction of citations to recent papers:

Collectively, the models presented illustrate that as journal archives came online, either through commercial vendors or freely, citation patterns shifted. As deeper backfiles became available, more recent articles were referenced; as more articles became available, fewer were cited and citations became more concentrated within fewer articles.

The interpretation offered is that online availability has driven citations to become more focused while less relevant articles are more easily filtered out. In addition, Evans argues that facile navigation through the network of hyperlinked citations may amplify the tendency to be influenced by other's choice when citing "reference" studies and thus accentuates the dominance of a restricted number of articles:

By enabling scientists to quickly reach and converge with prevailing opinion, electronic journals hasten scientific consensus. But haste may cost more than the subscription to an online archive: Findings and ideas that do not become consensus quickly will be forgotten quickly.

It is probably difficult to be sure that all sources of bias and confounding factors can be eliminated in this type of analysis. For example, on the Friendfeed discussion thread, LJ Jensen asks whether the sheer amount of published research could explain why scientist restrict their citation to the most recent literature. See also some additional discussion in the associated News & Views (Couzin, 2008)

In any case, the study highlights two complementary strategies in information retrieval: finding relevant papers by targeted searches versus staying informed on a broad range of topics by systematic browsing. In our Google-driven era, we may have the tendency to forget the importance of good old-fashioned 'table-of-content-skimming' to stimulate cross-disciplinary thinking, widen our horizon and cultivate scientific curiosity.

Perhaps it is a specificity of printed media to provide "poor indexing" and therefore enforce broad exposure to unrelated areas of research. On the other hand, some web technologies already help to browse through vast amounts of online publications (for example an RSS aggregator helps me to generate a daily literature survey; this can be further combined, for example here at Frienfeed, with other community-centered feeds; other aggregators highlight information by automatic clustering: Postgenomic and Scintilla). However, these tools remain imperfect and, in our reflection on the future of scientific publishing, we will need to find the right balance between the two strategies above and think of how the increasing efficiency of search engines can be complemented by means providing a continuous exposure to diversity.

Fascinating correlations or elegant theories?

2008-07-10T22:56:35Z

Chris Anderson, Editor-in-Chief of Wired , wrote a few weeks ago a provocative piece "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete", arguing that in our Google-driven data-rich era ("The Petabyte Age") the good old "approach to science —hypothesize, model, test — is becoming obsolete", leaving place to a purely correlative vision of the world. There is a good dose of provocation in the essay and it was quite successful in spurring a flurry of skeptical reactions in the blogosphere, FriendFeed-land and lately in Edge's Reality Club.

I know that it is a bit late to write a post on this but this debate reminds me of the bottom-up vs top-down dialectic in (systems) biology. The tradition in molecular biology has been to focus on molecular mechanisms–a series of molecular events–that explain given biological functions. With detailed knowledge on the properties of an increasing number of components, bottom-up mechanistic descriptions–or models–can be constructed, which account for the experimental observations.

Of course, the purpose of models, at least for insightful ones, is more than merely providing mechanistic descriptions. As William Bialek writes, "Given a progressively more complete microscopic description of proteins and their interactions, how do we understand the emergence of function?" (Aguera y Arcas et al, 2003). There is therefore some subsequent subtle transition from description to insight, from model to theory, from detailed and specific to simple and general (watch Murray Gell-Mann's TEDTalk on "Beauty and truth in physics").

Theories are elegant.

On the other hand, high-throughput technologies (microarrays, proteomics, metabolomics, ultra high throughput sequencing, etc...) are indeed profoundly changing molecular biology and flooding the field with experimental data like never before. Currently, only part of this data can be explained within the context of mechanistic models. Still, and this is probably Chris Anderson's main point, it turns out that if the data is rich enough, one can exploit it by looking at the data globally, from the 'top', to reveal statistical patterns and correlations. Even if there is no mechanistic explanations (yet) for these correlations, they may reveal new worlds, novel structures and detect relationships between processes that were considered before as unlinked.

Correlations are fascinating.

Correlations resulting from data-driven analysis may well in turn stimulate new mechanistic investigations and hopefully new understanding. On Edge, Sean Carroll summarizes it all: "Sometimes it will be hard, or impossible, to discover simple models explaining huge collections of messy data taken from noisy, nonlinear phenomenon. But it doesn't mean we shouldn't try. Hypotheses aren't simply useful tools in some potentially-outmoded vision of science; they are the whole point. Theory is understanding, and understanding our world is what science is all about."

BUT, what is true for fundamental science is not obligatorily a rule for more applied fields, where the priority might less be on understanding than on acting. In particular, in medically related fields, top-down data-driven correlative approaches represent a pragmatic approach to obtain predictive models without waiting for still elusive fully mechanistic models that would encompass the entire complexity of human physiology (Nicholson, 2006).

As often in science, as in other human activities, different but complementary views are championed by people with different temperaments: there are those who like to build an edifice piece by piece and those who want to explore new territories. I think–I hope–that progresses in systems biology on both fronts, top-down and bottom-up, demonstrates that there is no need to turn this complementarity into an opposition.

2007 Impact Factor

2008-06-18T19:12:14Z

The 2007 Impact Factors were published yesterday by Thompson Reuters.

The Impact Factor of Molecular Systems Biology for 2007 is 9.954

This represents a substantial increase over last year's Impact Factor (see chart) and we would like to warmly thank all our authors and reviewers who have contributed to this success. We will continue to work very hard to maintain the high standards of the journal and promote innovative and insightful research in systems biology.

The significance of Impact Factors suffers from intrinsic limitations (see Ian's post) and interpretation of this metric is subject to much discussion (Rossner et al 2007, Thompson's Citation Impact Forum). These and other questions related to bibliometrics are also currently debated at the Nature Network Citation in Science group.

Google Health, Biomedical Mutual Organizations and Open Consent

2008-05-20T09:46:47Z

Google Health, the new service offered by Google is now online (via bbgm, Life as a Healthcare CIO, GTO). This service helps users to store, organize and share their health profile and medical records, to use a variety of health-related online services and to search for medical information. Understandably, Google places great emphasis on data security and confidentiality. In this regard, I thought it might be worth highlighting several recent and thought-provoking discussions around the issues of data privacy and participative medical investigations.

In a provocative editorial (Bains, 2007, see also Nature Medicine News article), William Bains advocates that collectives of individuals, so-called 'Biomedical Mutual Organization', could organize themselves on a voluntary and self-funded basis to conduct clinical trials that would rely on extensive self-experimentation, data sharing and pooling of analytical resources. This proposal challenges the classical view that those who conduct a clinical trial should avoid conflicts of interest with respect to the outcome of the trial. On the other hand, Bains argues, this system would allow more innovative and radical trials to be performed, given that the subjects of the trial would have increased trust in the research process (being their own trial managers) and, hopefully, a more accurate perception of the risk/benefit balance involved.

Another radical proposal is the concept of 'open-consent' as currently applied within George Church's Personal Genome Project (Church, 2005). Jeantine Lunshof, George Church and colleagues highlight in a recent review (Lunshof et al, 2008) the limitations of the current definitions of genetic privacy and confidentiality in view of the rapid advances in the fields of human genetics and personal genomics. In particular, the creation of large database interlinking individual genome-wide genotypes to extensive phenotypic profiles will make de-identification of such datasets increasingly difficult if not impossible (Lowrance and Collins, 2007). Under these conditions, it appears that the promise of absolute anonymity and confidentiality of private data is becoming unrealistic. Church and colleagues affirm that an 'open-consent' policy would avoid making such false promises and would therefore represent a more realistic way to formulate an adequately informed consent when accepting to participate to a human genomic research study.

At last month's ESF Conference on Systems Biology, Hiroaki Kitano discussed the potential of multi-component, combinatorial therapies (see also Kitano, 2007). He introduced the tentative idea of an 'Open Pharma' strategy, which would attempt to exploit beneficial synergistic effects that may result from combined administration of cheap generic drugs. He envisions that this type of approach could ultimately lead the way to novel and hopefully more affordable therapeutic strategies, which would provide a potential alternative to the current single-target proprietary drug paradigm.

Observing the launch of Google Health within the context of this series of rather revolutionary proposals, it is tempting to imagine for a moment what would result from large-scale self-experimentation with multi-component generic drug cocktails combined with web-enabled data sharing under some form of open-consent... Will 'Participative Open Pharma' be our future?

Rewiring E. coli transcriptional network

2008-04-29T10:38:13Z

Research highlight by Kazuharu Arakawa and Masaru Tomita, Institute for Advanced Biosciences, Keio University, Japan

Gene duplications and mutations are central driving forces in the evolution of genomes. Genomes must be robust to such changes in order to be evolvable, and many studies have probed genome robustness using systematic gene knockouts or overexpression experiments. In a recent paper, Isalan et al. (2008) took a new approach to test the robustness of Escherichia coli gene circuitry by reconstructing gene duplication events by shuffling the promoter-ORF pairs for about 300 transcription factors and introducing 598 recombined pairs one-by-one into E. coli to rewire its transcriptional network. Surprisingly, ~95% of such additions are robustly tolerated, and some networks even exhibit greater fitness under various selection pressures. Moreover, the study shows that, in contrast to naive expectations, the introduction of positive or negative feedback loops has little effect on the protein expression levels of regulated ORFs.

Since radical rewiring of the gene circuitry appears to have only a limited impact on expression levels, this work suggests that gene regulatory networks are highly dynamic and underscores the potential importance of post-transcriptional mechanisms for the robustness of transcriptional regulation. Moreover, this work illustrates the fundamental robustness and evolvability of gene regulatory networks, which is reassuring news for synthetic biology.

Isalan M, Lemerle C, Michalodimitrakis K, Horn C, Beltrao P, Raineri E, Garriga-Canut M, Serrano L (2008) Evolvability and hierarchy in rewired bacterial gene networks. Nature 452:840

ESF-UB Conference on Systems Biology

2008-04-21T12:12:02Z

The ESF meeting on Systems Biology, organized by Luis Serrano and Ruedi Aebersold, took place last week in Sant Feliu de Guixols, Spain. A lovely location (I took this picture with my iSight directly from my room...) for a small conference with a list of outstanding speakers. Together with the influence of the Mediterranean-Latin 'cultural jet lag' (understand: go to bed very very very very late), the stage was set for intense networking among the participants.

The meeting had a broad scope, and I think that the organizers did a very good job in covering the diversity of the field, form quantitative biology and mathematical modeling to network biology, large-scale phenotyping and synthetic biology. Even if I cannot summarize all the talks, here are some general impressions on some of the directions.

First, the 'systematic' branch of systems biology appears to be extending progressively to the cellular level, thanks to progresses in high-throughput imaging techniques and expression systems applied to mammalian systems. For example, large-scale sub-cellular (co-)localization of proteins are used to help deduce extensive maps of molecular interactions that underly the biological function of an organelle (Anthony Hyman), while the analysis of cell-to-cell variability in morphological or other cellular-level features reveals effects that would otherwise be undetectable (Lucas Pelkmans).

At the molecular level, the analysis of large biological networks (transcriptional, Luis Serrano; protein-protein interactions, Marc Vidal) is now progressing towards a large-scale analysis of the impact of perturbations of specific interactions ('edges') rather than the more conventional approach of looking at the absence/presence of individual 'nodes'. This emphasis on 'edges' is further illustrated by efforts in increasing the resolution of protein-protein interaction networks to the level of individual protein domains (Anthony Hyman, Marc Vidal).

The roles and consequences of biochemical interactions are seen somewhat differently by those who study quantitatively signal transduction mechanisms. There, great emphasis was put on the fact that seemingly simple biochemical interactions can result in surprisingly rich spatial and temporal behaviors (Boris Kholodenko) and that considerations of these dynamical aspects are crucial to provide fundamental mechanistic insights into the functions performed by signaling systems. As an example, the quantitative analysis of NF-kappaB signaling dynamics reveals that a sophisticated temporal code is used to discriminate between a variety of stimuli to achieve a stimulus-specific transcriptional response (Alexander Hoffmann).

Clearly, significant efforts remain to bridge large-scale 'systematic' systems biology to its small-scale 'quantitative' branch and one may at first wonder whether these two visions belong to the same field. A recurrent and potentially unifying theme was however that both approaches attempt to understand the relationship linking a biological function to the components of the system that performs this function. As nicely formulated by Tony Hyman, one of the key problems in (systems) biology is to understand how 'individuals' contribute to a 'collective behaviour' (Denis Noble also notes that the 'collective behaviour' can impact on the properties of 'individuals'). This view of systems biology has the advantage that it provides a similar objective for research applied at various scales (eg a cell, an organelle, a signaling pathway, a protein complex) without imposing arbitrary constraints in terms of experimental or computational approaches.

Engineering of biological systems able to perform a human-specified function is intimately related to advances in systems biology. An example of how system-level engineering is pushed to the limits was illustrated by Ron Weiss, who is progressively implementing cell-cell communication, information processing, and cell differentiation control circuits into mammalian stem-cells to ultimately enable rational 'programmed tissue engineering'. But these types of extremely complex circuits currently require enormous efforts and a major emphasis is to develop tools that allow proper engineering practice in biology. Such efforts are the most advanced for systems hosted in bacteria and Adam Arkin provided some spectacular examples of modular design and illustrated how well designed circuits (eg oxygen sensing module from a tumour-invading bacteria) can be rapidly re-used to enormously shorten the development time required to engineer new functions (eg artificial blood cell), without eternal tweaking and tuning.

On a more frivolous note, it did not take us too many glasses of wine at dinner, to start speculating with Hiroaki Kitano about mixing the Robocup and iGEM competitions to create a new 'bio vs nanomachine' league that would let nano-robots play against engineered microorganisms. As I said, we may not have had always enough sleep...