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, 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?

Two rather contrasting videos on synthetic biology this month. In the first videocast, released by TED, Craig Venter exposes his grand vision of synthetic genomics. He insists on the notion of 'combinatorial genomics', that will combine the power of large scale DNA synthesis ('robots that can make a million chromosomes a day') with a database of 20 million genes, 'the design components of the future'. This approach, a pragmatic mixture of rational function-oriented design and empirical large-scale selection, is envisioned to prepare a modern 'Cambrian explosion' of new synthetic species. It is good to see Craig Venter laughing when announcing casually the 'modest goal of replacing the entire petro-chemical industry'. In any case, Craig Venter appears to be more concerned that the technology may not develop sufficiently rapidly to match the urgency and scale of the major ecological and medical challenges faced by our planet than by potential threats represented by harmful biohacking and bioterror.

The second video, admittedly less entertaining, is a recording of the recent deliberations of the National Science Advisory Board for Biosecurity (NSABB). In his presentation entitled 'Assessing Biosecurity Concerns Related to Synthetic Biology', David Relman presents some preliminary findings and recommendations of the Working Group on Synthetic Genomics (jump to 1hr:34min:37sec). It is interesting to see that no consensus definition of synthetic biology exists among the various practitioners of the field, who all use different blends of the typical bottom-up engineering approach assembling circuits from standard components and top-down strategy, based on the modifications of existing genomes. Beyond the lack of definition, the current ability to predict biological functions from sequence (eg virulence) remains very limited complicating the possibility of realistic risk assessment. Finally, the development of synthetic biology can be seen as an extension of the success of 'kit-based' molecular biology, which facilitates access of these technologies to groups outside the traditional Life Sciences communities and institutions, making the mission of oversight, outreach and eduction more challenging. David Relman also clearly emphasizes the importance of not discouraging the enthusiasm directed towards potentially beneficial research and applications by overzealous oversight and regulations.

The intersection between the two talks above was perhaps made when the question of virulence was raised (jump to 1hr:59min:35sec). The fraction of pathogenic agents is very small compared to the number of existing species, a point also made by Craig Venter, and the rate of appearance of new pathogens is low. The idea was then raised as whether it would be possible to roughly estimate the risk of creating synthetic pathogens by calculating the likelihood that the amount of natural recombination responsible for the emergence of new pathogens 'in the wild' could be matched by an equivalent amount of experimental recombination in the laboratory. In other words, is there any way to estimate the probability that new forms of virulence could emerge from the announced synthetic 'Cambrian explosion'?

In a nice post at bbgm, Deepak writes:

...historical online literature lacks the relevant structure and metadata to make our task easier, but it is time that publishers thought ahead about some of the advantages of online publishing.

I can't agree more. I heard sometimes the claim that within 5-10 years, more than 95% of the scientific literature is going to be read by computers only. Possible. However, the converse alternative might be interesting to consider: what if 95% of scientific papers could be 'written' by computers? Even if this formulation is obviously provocative and unrealistic, the point is that harnessing the 'network effect' of the web may have two complementary components, one community- the other computer-driven. On one hand, web 2.0 functionalities enable community-driven commenting, rating and even writing of scientific publications. On the other hand, semantic web technologies are expected to facilitate computer-driven integration of scientific data from multiple sources, which is likely to play an increasingly important role in science. Rather than mining thousands of unread papers, the scientist of the future may rather search the web for relevant data first and integrate it to generate – or 'write' – novel insight. In fact, integration of large datasets already represents a major field of research in systems biology (see Chuang et al 2007, Xue et al 2007 or Mani et al 2008 as recent examples published in Mol Syst Biol).

It seems thus that, in addition of being web 2.0 enabled, new publishing models should 'embed' more structured data into online publications. In short, 'papers' could progressively transform into hybrid online objects that resemble more to database records (see Timo Hannay's post on this topic) or highly structured documents. At the extreme, one could even imagine to publish 'naked' datasets, without any 'stories' around them. Of course, efficient data integration will require the data to be in a standard and structured format and its quality will have to be well characterized. These are all far from trivial qualities.

The good old-fashioned papers are probably not going to disappear as publication units, in particular for high-impact studies reporting novel and deep insights. It is also not the point here to propose dumping every scientist's hard drive into the web. Data-rich publications would be published only when the authors would feel it appropriate. There might thus be some equilibrium to find between papers that will never be read except by a text mining engine and pure datasets, published as a resource, easier to search, to mine and to integrate. This dialectic may ultimately boil down to the issue of how well will text mining and data integration technologies perform in the future.

In any case, within the context of the current debate about the saturation of the peer-review system, I wonder whether a data-centric form of scientific publishing could help to release somewhat the pressure. Reviewing of datasets might be quicker and could rely more on standardized evaluation parameters. If assorted with proper credit attribution mechanisms and metrics of impact, data-rich (or even data-only) publications may represent an alternative model complementing the traditional 'paper' format. It would prevent the loss of useful data otherwise buried in verbal descriptions and, most importantly, would hopefully stimulate web-wide integration of disparate datasets.

(via Scintilla)

The article on "Probiotics modulation of mammalian metabolism" published this week in Molecular Systems Biology by Jeremy Nicholson and colleagues (Martin at al, 2008) has attracted some attention (read the nice summary in Science News) in some (very) popular media (here, here, here and here).

In this follow-up study of the paper published last year (Martin et al, 2007), the team lead by Jeremy Nicholson, in collaboration with Nestlé, demonstrates clear physiological effects of oral probiotics administration on mice harbouring a humanized microbiome. The effects are intricate: both the host flora and metabolism are altered. By analyzing metabolite pools in several compartments (liver, blood, urine, feces, gut), and following in parallel the host microbiota, patterns of correlations between microbial species and metabolites start to be visible and reveal the probiotics-induced modulation of the microbial-mammalian interactions. But the actual paper is really just next door (synopsis), so have a look...

How will these results translate to humans? What will be the best way to influence our microbiome? Drugs or yoghurt? These are fascinating questions and the understanding of how our physiology depends on the microbial flora could have profound consequences, particularly in these times when we seem to be in a "rush to gene-based solutions to all our problems" (Wilson, 2007). Will personal genomics have to ultimately develop into personal metagenomics to include our "extended" microbial genome?

Even if I usually prefer to resist the temptation of a self-promoting section in this blog, I find the attention of the media for this topic interesting (despite the usual variable accuracy of newspaper reports) because it points to an area where systems biology provides insights into topics of immediate interest to the general public.

The NIH has recently started its Human Microbiome Project. In this context, this study also underscores the importance of developing model systems and tools to manipulate the microbiome and to analyze the incredibly dense and intricate interactions that connect host and microbial species. A field where top-down systems biology seems indeed a very pragmatic and promising approach.

I highly recommend to visit the NIH VideoCasting page, which hosts many interesting video/podcasts. Even if I realize that this is a bit old according to the blogosphere time scale, I would like to point to this one: "The Future: Consumer Health Information Technology", featuring talks given at a NCI-sponsored meeting on Dec 10, 2007 by Adam Bosworth (formerly "Google Health architect", now starting his own company Keas), Bern Shen (Intel) and Bill Crounse (Microsoft).

In his introduction to the meeting, Bradford Hesse (NCI) colorfully summarizes one of the main concepts exposed by the speakers (the video is very long, so I give some pointers: 0h16min43sec) by comparing the future of healthcare to...an "IKEA flat pack": patients will progressively be empowered to assemble their own care from home, like they would build a piece of (cheap) furniture.

Adam Bosworth (0h25min53sec) presents his very pragmatic vision of how IT could concretely help healthcare (0h39min07sec): a) help the consumer to own and control his personal health data, and this already for very simple basic information; b) provide tools for doctors so that they can deliver personalized care as easily as producing a spreadsheet; c) develop tools for researchers to facilitate the design and implementation of new protocols and clinical trials.

Bill Crounse (Microsoft's other Bill...1h14min30sec) sees 5 major current trends that will increasingly challenge the healthcare system and call for IT solutions (1h26min22sec): a) increasing personal responsibility ("the end of health insurance"); b) progressive "retailization" of healthcare services (eg appearance of "retail minute clinics"); c) commoditization of healthcare providers; d) globalization of access to information (through the web of course); e) globalization of healthcare services. I recommend his little funny anecdote on the high-tech GPS wireless-connected plumber (1h25min30sec) who appears to better equipped than any practicing physician...

The speakers also all insist on the need for massive data integration promoted by the interoperability of formats and coding information, themes that probably sound familiar to many systems biologists.

Toward the end of his talk (1h35min00sec), Bill Crounse shows a short "science-fiction" movie on Microsoft's vision of the future of healthcare: a world full of credit-card sized tablet PCs, touch screens and many other very exciting gadgets (I love gadgets!). But I can't help missing a bit the warmth of human-to-human interactions within this jungle of virtual consultations, retail clinics, remote controlled metabolic parameters, etc... and I didn't quite see in that movie that the doctor would spend more time with his patient or the daughter with her sick Grandma. But this may of course only reflect some old-fashioned side of my temperament...

The wave of personal genomics is progressing rapidly. A string of four papers appeared recently (Porreca et al, 2007, Albert et al, 2007, Okou et al 2007, Hodges et al, 2007) reporting on microarrray-based technologies that enable the enrichment of selected genomic fragments in a single massively multiplexed reaction, thus greatly facilitating subsequent resequencing of pre-defined portions of the human genome (eg all coding exons). These technologies are expected to reduce dramatically the cost of targeted resequencing of individual genomes.

On the commercial front, deCODE and 23andMe have launched their personal genome service offering genome-wide SNPs profiling for a little less than $1,000 (NYT articles: Nicholas Wade, Amy Harmon, or Wired, ScienceRoll, Sandra, DNA and You).

The chips used by 23andMe are the "Illumina HumanHap550+ BeadChip, which reads more than 550,000 SNPs (single nucleotide polymorphisms) plus a 23andMe custom-designed set that analyzes more than 30,000 additional SNPs." The profile provided by deCODEme includes "over one million variants across the genome."

So what do you think?

Contrary to what Charlotte Hunt-Grubbe predicted in her interview published in the Sunday Times (The elementary DNA of Dr Watson), James Watson did not "enthusiastically counter the inevitable criticisms" that arose from his unacceptable comments on racial differences in intelligence. After being suspended, he apologized and finally resigned yesterday as Chancellor of Cold Spring Harbor Laboratory (Watson's statement).

It is striking to observe that these very sad events occur in the current context of a literal explosion of studies in human genetics and genomics. Thus, it is only a few months ago that Watson's and Venters' personal genome sequences have been released, while an uninterrupted stream of new genome-wide association studies are being published. If we just consider some of the papers that appeared in Nature and Nature Genetics the last few weeks, we see an impressive concentration of genome-wide studies on human genetic variation, addressing the genetic basis of highly visible phenotypes like skin, eye, and hair color, the impact of geographical location, revealing evidence of positive selection and analyzing heritability of gene expression in human populations:

• Genetic determinants of hair, eye and skin pigmentation in Europeans. Nat Genet. 2007 Oct 21
• Population genomics of human gene expression. Nat Genet. 2007 Oct
• A genome-wide association study of global gene expression. Nat Genet. 2007 Oct
• Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet. 2007 Oct
• A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007 Oct 18
• Genome-wide detection and characterization of positive selection in human populations. Nature. 2007 Oct 18
• The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007 Oct

The extraordinary development of the field of human genomics will inevitably lead to important questions on the social and ethical implications of this research. If anything, "Watson's folly" might be a warning that we may expect to see in the future more confrontations between racist ideologies and scientific discoveries. Beyond the issues surrounding ethnicity, one can also anticipate that tense debates will arise as how to define the line that separates "patient stratification" from mere genetic "discrimination" of human beings.

A cardinal value in Science, perhaps even above openness, is the ability of critical reasoning. This implies rigor and depth with very little place for unsubstantiated provocations. In this regard, I disagree with PZ Myers (Pharyngula), when he writes that the prompt decision of CSHL to suspend Watson appears as a "declaration that their director must be an inoffensive, mealy-mouthed mumbler who never challenges (even stupidly)". I do hope that there is an alternative to inoffensiveness but debates on these very sensitive issues and at this level of responsibility and visibility require the highest scientific and ethical standards, and we should definitely expect much more from our prestigious leaders than being "challenging" just by making outrageous statements...

Note: publication of this post was unfortunately delayed due to technical problems

(Illustration drawn after http://www.nlm.nih.gov/visibleproofs/media/detailed/vi_a_206.jpg)