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

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...

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.

Research highlight by Doron Lancet, Crown Human Genome Center, Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Living cells are typically asymmetric, having tens of thousands different biopolymers (proteins and polynucleotides), but merely <1000 types of small molecules, such as amino acids and lipids. An exception is certain plant cells that harbor members of ~40,000 strong group of low molecular weight terpenoids, often displaying a complex compositional balance essential for plant growth and survival (Aharoni et al, 2005). Understanding the intricacies of biosynthesis and interconversion of such unusual cellular components appears to require the full power of Systems Biology. In a recent paper, Rios-Estepa et al (2008) harness a systems approach, including iterative cycles of mathematical modeling and experimental testing, to help elucidate the metabolic dynamics of the terpenoid universe.

Specifically they ask how plants vary their monoterpene profiles in response to environmental stress – changing levels of illumination. A highlight of their results is that the variation of terpene metabolic fluxes is mediated by specific events in which members of the terpenoid repertoire exert a regulatory effect on terpene biosynthesis enzymes. Rewardingly, this is predicted by a computer simulation and subsequently verified by experiment. The broader conclusion, applicable to all living organisms, is that as the power of computing grows, it will become possible to make increasingly specific and accurate predictions, that will allow both a better global understanding and the successful engineering of cellular networks.

Aharoni A, Jongsma MA, Bouwmeester HJ (2005) Volatile science? Metabolic engineering of terpenoids in plants. Trends Plant Sci. 10:594-602.

Rios-Estepa R, Turner GW, Lee JM, Croteau RB, Lange BM (2008) A systems biology approach identifies the biochemical mechanisms regulating monoterpenoid essential oil composition in peppermint. Proc Natl Acad Sci U S A. 105:2818-2823

Research highlight by Jeongah Yoon and Thomas S. Deisboeck, Massachusetts General Hospital, Charlestown, MA

Targeting receptor tyrosine kinases (RTKs) is currently thought to be a promising anti-cancer strategy (Baselga, 2006). However, clinical trials with RTK inhibitors demonstrated that some solid tumors are sensitive to these drugs while others are not. For instance, only a subset of non small cell lung cancer (NSCLC) tumors with EGFR-activating mutations seems to respond to EGFR inhibitors (Lynch et al, 2004).

The recent study by Guo et al (2008) aims to shed more light on the causes for such selective drug sensitivity by investigating the downstream signaling pathways of several NSCLC cell lines and a gastric cancer cell line. Using a quantitative global proteomic analysis (PhosphoScan-SILAC) they analyzed the EGFR and c-Met networks, treated with the EGFR inhibitor gefitinib and the c-Met inhibitor Su11274, respectively.

The results show a dramatic decrease in EGFR phosphorylation from 5- to 200-fold after gefitinib treatment as well as a reduction of some adaptor proteins (e.g., Her3, Gab1, and Shc1), adhesion and cytoskeletal proteins. Furthermore, a c-Met-driven gastric cancer cell line demonstrated sensitivity to the c-Met inhibitor, Su11274. The authors observed that the inhibited EGFR and c-Met signaling networks share a number of molecular components which underscores that amplified c-Met can drive the activity of (mutated) EGFR and vice versa. In both cases, the targeted kinase is positioned on top of the hierarchical signaling network and thus controls downstream signaling.

In conclusion, this interesting study suggests that there is a common sub-cellular signaling module that processes drug sensitivity and that the effect of an anti-RTK therapeutic compound is maximized when the targeted kinase uniquely controls the downstream signaling networks.

Baselga J (2006) Targeting tyrosine kinases in cancer: the second wave. Science 312:1175-8

Guo A, Villén J, Kornhauser J, Lee KA, Stokes MP, Rikova K, Possemato A, Nardone J, Innocenti G, Wetzel R, Wang Y, MacNeill J, Mitchell J, Gygi SP, Rush J, Polakiewicz RD, Comb MJ (2008) Signaling networks assembled by oncogenic EGFR and c-Met. Proc Natl Acad Sci U S A. 105:692-7

Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL, Haserlat SM, Supko JG, Haluska FG, Louis DN, Christiani DC, Settleman J, Haber DA (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 350:2129-39

In a very recent lecture (see full video from NIH VideoCasting) given for the NIH Systems Biology Special Interest Group, Trey Ideker presents a great overview of the various strategies his group has been developing in the recent years in order to integrate multiple types of large scale datasets. While one of the most pervasive 'meme' about high-throughput measurement is that they are "notoriously unreliable" (see Hakes et al, 2008, for a recent example), Trey beautifully illustrates how predictive computational models and novel biological insights can be generated by sophisticated data integration strategies. Three types of applications are presented in his talk:

1. mapping of transcriptional response pathways
2. functional mapping of protein complexes
3. disease diagnosis and stratification

In the last section, Trey presents the study recently published in Molecular Systems Biology (Chuang et al, 2007, video: 00hr:39min:15sec) where the information provided by microarray expression profiling is superposed to a protein-protein physical interaction network to identify 'subnetwork' biomarkers that classify metastatic vs non-metastatic breast tumors.

How to make biology easy to engineer and what are the consequences of success? Drew Endy exposes his views on these key issues in the field of synthetic biology in a video released in the last issue of EDGE.

As a teaser, here are a few quotes from this interview, summarizing in a nutshell his opinion on the current priorities of the field and its future development:

Engineers hate complexity. I hate emergent properties. I like simplicity. I don't want the plane I take tomorrow to have some emergent property while it's flying.

How do you manage the information going into a DNA synthesizer so that you can construct some useful object that'll help you do genetics? [...] I think George Church and Craig Venter have a lot to contribute to it, which will be terrific. It will be part of synthetic biology, but it will be synthetic biology impacting science, which is the worst case scenario for synthetic biology.

Five years from now, we may have just begun to make some good progress on reliable functional composition of standard biological parts. Nobody knows how expensive solving that problem will be, but because biology works there's plenty of existence proofs. [...] If I had to guess, I'd say we'll have a collection of tens of thousands of genetic objects that support reliable functional composition between ten and 15 years from now.

Drew Endy also mentions the need to develop an "ownership sharing and innovation framework" that will be appropriate to this pure engineering approach to synthetic biology. A related question might be to find the appropriate publishing instruments that would provide suitable incentives and (micro)attribution mechanisms for those who will embark in contributing, probably often incrementally, to the projected "tens of thousands of genetic objects". One idea could be here to adopt a two-layered system inspired from the one proposed for "Human Variome Microattribution Reviews". In such a system, a "Part Browser" would provide the list and number of all articles/database entries referring to a specific part while partner journals would commission high-level Part/Device Review articles to highlight a "family" of parts or device that might be of particular relevance to the community. Would this make sense (eventually)? How did the electronic engineering field deal with this problem in its early days?

Research highlight by Frank C.P. Holstege, Department of Physiological Chemistry, University Medical Center Utrecht, the Netherlands.

For eukaryotes, it is widely thought that transcription is primarily regulated through recruitment of the essential machinery to transcription start-sites. Previous hints challenging this paradigm have been confirmed by recent analyses showing that transcription regulation of a large number of genes actually occurs after recruitment. Mechanistically, such studies have gone furthest in Drosophila melanogaster (Muse et al, 2007; Zeitlinger et al, 2007). Here, conservative estimates indicate that more than 10% of genes are regulated through promoter-proximal pausing. On such genes, RNA polymerase II is recruited and initiates transcription, but then pauses around 50 bp downstream of the transcription start-site where it awaits further signals to resume elongation and complete transcription proper. These observations tie in with other observations made in yeast (Radonjic et al, 2005), embryonic stem cells (Bernstein et al, 2006; Lee et al, 2006) and differentiated mammalian cells (Guenther et al, 2007). There are numerous implications to these findings. For example, the widely assumed link between the presence of gene-specific transcription activators and full-length transcription appears to be much looser than expected. These results also underscore the importance of testing established models on a genome-wide scale. Indeed, other such surveys (Birney et al, 2007), indicate that to understand transcription, we may need to take into account even more surprises – such as the presence of ten times more start-sites than protein-coding genes and overlapping transcription units, etc… – than the post-recruitment mechanisms demonstrated in Drosophila.

Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, et al. (2006) A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125: 315-326

Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447: 799-816

Guenther MG, Levine SS, Boyer LA, Jaenisch R, and Young RA (2007) A chromatin landmark and transcription initiation at most promoters in human cells. Cell 130: 77-88

Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, Chevalier B, Johnstone SE, Cole MF, Isono K, et al. (2006) Control of developmental regulators by Polycomb in human embryonic stem cells. Cell 125: 301-313

Muse GW, Gilchrist DA, Nechaev S, Shah R, Parker JS, Grissom SF, Zeitlinger J, and Adelman K (2007) RNA polymerase is poised for activation across the genome. Nat Genet 39: 1507-1511

Radonjic M, Andrau JC, Lijnzaad P, Kemmeren P, Kockelkorn TT, van Leenen D, van Berkum NL, and Holstege FC (2005) Genome-wide analyses reveal RNA polymerase II located upstream of genes poised for rapid response upon S. cerevisiae stationary phase exit. Mol Cell 18: 171-183

Zeitlinger J, Stark A, Kellis M, Hong JW, Nechaev S, Adelman K, Levine M, and Young RA (2007) RNA polymerase stalling at developmental control genes in the Drosophila melanogaster embryo. Nat Genet 39: 1512-1516

Research highlight by Charles Auffray, Functional Genomics and Systems Biology for Health, UMR7091, CNRS and Pierre & Marie Curie University—Paris VI, Villejuif, France

The work presented by Helikar et al. (2008) in a paper recently published in the PNAS represents a promising new step in the development of computational cellular physiology in eukaryotes. From curated cellular and biochemical data available in the literature, the authors have assembled a discrete Boolean model of signal transduction comprising 130 nodes, and examined in a systematic and controlled manner how varying combinations of external inputs translate into a range of cellular responses. The qualitative model is not only able to reproduce known input-output relationships representative of major transduction pathways, but it also provides evidence in support of the emergence of information-processing functions from the complex cellular network of molecular interactions. This is strikingly demonstrated by the fact that a large sample of randomly selected input combinations result in a very limited fraction of the possible outputs, which correspond to well-characterized global biological responses, a result which is obtained irrespective of the level of noise introduced in the inputs of the model. Moreover, similar input combinations are neatly clustered by the model into equivalence classes of global outputs, reflecting the ability of the cell to integrate complex environmental signals and translate them into robust specific responses and behaviours through common intracellular pathways. While discrete Boolean modelling makes it possible to highlight emergent properties of transduction networks, overcoming the hurdle of parameter estimation, very much as in classical physiology, it provides only high-order views in the form of black boxes with limited predictive and explanatory power. Integration with continuous models will be essential to unravel and engineer the underlying mechanisms.

Helikar T, Konvalina J, Heidel J, Rogers JA (2008). Emergent decision-making in biological signal transduction networks. PNAS 105, 1913-1918