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

To raise awareness of important advances in systems and synthetic biology, today we open a new section in this blog: Research Highlights.

Research Highlights will be contributed by members of the Advisory Editorial Board of Molecular Systems Biology and they will cover recent studies on topics related to systems and synthetic biology. These short posts are not intended to represent evaluations of the selected papers. Rather, our hope is that subjective filtering of the literature by our Editorial Board will provide some unique views on the diversity of the field and its progress.

Enjoy!