[Research highlight] Multicellular computers

Elaborate computation tasks can be performed by distributing the work across interconnected elementary information processing units. This principle underlies not only the operation of integrated electronic circuits, but also of many biological processes including development and, of course, the activity of the brain.

In two reports recently published in Nature, Chris Voigt’s lab and the team lead by Ricard Solé and Francesc Posas report the construction of synthetic biological circuits performing distributed multicellular computation (Tamsir et al, 2010, Regot et al 2010). In the implementation presented by Tamsir et al, individual E. coli colonies carrying a simple genetic cascade (NOR gate) are interconnected via quorum sensing signaling molecules to perform complex operations (XOR, or EQUAL). Similarly, Regot et al build multicellular circuits (e.g. multiplexer or 1-bit adder with carry) using mating pheromones to chemically ‘wire’ together engineered yeast cells that perform a variety of basic 2-input logical functions.

These works show that compartmentalizing elementary synthetic circuits enables combinatorial and flexible assembly of complex circuits and can improve the robustness of the resulting computation. What could be the next step? Looking at the operation of the brain, arguably the most powerful living computing device known, one is tempted to suggest that narrowing the diffusion range of the chemical cell-to-cell transmitter within ‘synthetic synapses’ would facilitate the miniaturization of multicellular computing networks and potentially open the door to scalable designs of arbitrary complexity.


Tamsir A, Tabor JJ, Voigt CA (2010). Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature doi: 10.1038/nature09565

Regot S, Macia J, Conde N, Furukawa K, Kjellén J, Peeters T, Hohmann S, de Nadal E, Posas F, Solé (2010). Distributed biological computation with multicellular engineered networks. Nature doi: 10.1038/nature09679

[Research highlight] Laws of microbial growth

In a work recently published in Science, Scott et al reveal a series of microbial “growth laws” that describe simple relationships between translation, nutrition, and cellular growth. They show that these laws hold across different experimental perturbations and E. coli strains, and, ultimately, provide a phenomenological model describing the delicate balancing act cells maintain when deciding how much of their proteome to allocate to ribosome-related processes.


Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T (2010) Interdependence of cell growth and gene expression: origins and consequences. Science 330:1099-102

→ also see the related Perspective

Lerman J, Palsson BO (2010) Topping off a multiscale balancing act. Science 330:1058-9

q-bio 2010 Conference on Cellular Information Processing

This last August 11-14, systems biologists convened in beautiful Santa Fe, New Mexico, for the Fourth Annual q-bio Conference on Cellular Information Processing. The conference brought together a potent mix of theoretical and quantitative experimental biology across a wide range of topics. The full program and abstracts for each talk can be browsed on the conference’s Wiki page.

St. John’s College, the q-bio venue

Highlighting the value of systems-level analysis, many of the talks revealed the functional importance of features of biological systems that may often be tempting to disregard:

  • Thierry Emonet showed that noise in the chemotactic signaling pathways actually acts to help coordinate the bacteria’s multiple flagella.  (In fact, chemotaxis and bacterial swarming were popular topics. See also the talks by Jan Liphardt, Ned Wingreen, Victor Sourjik, Bonnie Bassler, Christopher Rao, and Yi Jiang).
  • Talks by Anat Burger and Narendra Maheshri explored the ways that non-functional transcription factor binding sites (sites that do not directly affect gene regulation) can nonetheless have dramatic effects on the dynamics of gene regulatory circuits.
  • Debora Marks discussed her work showing that saturation and competition play a potentially important role in determining the efficiency of siRNA and microRNA target gene repression. (See also her recent work in Molecular Systems Biology, Arvey et al. 2010).

The conference also hosted several excellent talks on cell cycle regulation — a classical model in systems biology research — including a closing lecture by James Ferrell and a talk by John Tyson describing his detailed stochastic model of the eukaryotic cell cycle (recently published in Molecular Systems Biology, Barik et al. 2010). See also talks by Jan Skotheim, Silvia Santos, and Xiaojing Yang. Galit Lahav also provided some exciting insights into another extremely well-studied system — p53 signaling (see Loewer et al. 2010).

In addition, two researchers studying HIV1 provided some of the most thought-provoking presentations:

    • Leor Weinberger proposed a way to treat HIV1 with a transmissible therapeutic agent, and described both cell culture experiments demonstrating the ability of their agent to slow HIV1 propagation, and computational modeling showing how this agent could spread through the human population.
  • Alex Sigal used a combination of modeling and cell culture experiments to make a compelling case that direct cell-to-cell transmission of HIV1 may help maintain a low-level “smoldering infection” during anti-retroviral drug treatment.

Naturally, these are just a few highlights from the conference, which hosted many other excellent talks. Once again, we encourage you to browse the full program and abstracts on the conference’s Wiki page.


Barik D, Baumann WT, Paul MR, Novak B, Tyson JJ (2010) A model of yeast cell-cycle regulation based on multisite phosphorylation. Mol Syst Biol 6:405

Arvey A, Larsson E, Sander C, Leslie CS, Marks DS (2010) Target mRNA abundance dilutes microRNA and siRNA activity. Mol Syst Biol 6:363

Loewer A, Batchelor E, Gaglia G, Lahav G (2010) Basal Dynamics of p53 Reveal Transcriptionally Attenuated Pulses in Cycling Cells. Cell 142:89-100

[Research highlight] NF-kappaB signaling goes digital

In a report published this week at Nature, Tay et al. reveal that populations of mouse 3T3 cells exposed to TNF-α show a digital NF-κB response, where increasing TNF-α concentrations lead to a higher proportion of cells with nuclear localized NF-κB — an effect that depends, in part, on pre-existing heterogeneity within the cell population. These results provide another compelling example of the way that studies using single cell measurements are transforming our understanding of cellular signaling mechanisms. Interestingly, these results seem to contrast with another recent single-cell-based study of NF-κB dynamics (Giorgetti et al. 2010), which observed a relatively uniform population-level NF-κB response to TNF-α in human HCT116 cells, indicating that there is still much to learn about the dynamics of NF-κB signaling.


Giorgetti L, Siggers T, Tiana G, Caprara G, Notarbartolo S, Corona T, Pasparakis M, Milani P, Bulyk ML, Natoli G (2010) Noncooperative interactions between transcription factors and clustered DNA binding sites enable graded transcriptional responses to environmental inputs. Mol Cell 37:418-28

Tay S, Hughey JJ, Lee TK, Lipniacki T, Quake SR, Covert MW (2010) Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature 466:267-71

→ Also, see Cheong et al. for a history of systems biology modeling of NF-κB signaling:

Cheong R, Hoffmann A, Levchenko A (2008) Understanding NF-kappaB signaling via mathematical modeling. Mol Syst Biol 4:192.

[Research highlight] Cis-regulatory evolution, not so mysterious after all?

Animal genomes are littered with conserved non-coding elements (CNEs)—most of which represent evolutionarily constrained cis-regulatory sequences—however, it is often not clear why these sequences are so exceptionally conserved, since anecdotal examples have shown that orthologous CNEs can have divergent functions in vivo (Strähle and Rastegar 2008; Elgar and Vavouri 2008). In an article recently published in Molecular Biology & Evolution, Ritter et al. compare the functional activities of 41 pairs of orthologous conserved non-coding elements (CNEs) from humans and zebrafish (2010). Interestingly, sequence similarity was found to be a poor predictor of which CNEs had conserved function. In contrast, the authors found that measuring transcription factor binding site change, instead of simple sequence divergence, improves their ability to predict functional conservation. While this set of tested CNEs remains relatively small, these results are encouraging because they suggest that as scientists move from phenomenological measures of CNE evolution to models based explicitly on binding site evolution, the patterns of cis-regulatory evolution observed within animal genomes should become far less mysterious.


Elgar G, Vavouri T (2008) Tuning in to the signals: noncoding sequence conservation in vertebrate genomes. Trends Genet 24: 344–352

Ritter DI, Li Q, Kostka D, Pollard KS, Guo S, Chuang JH (2010) The Importance of Being Cis: Evolution of Orthologous Fish and Mammalian Enhancer Activity. Mol Biol Evol advance online publication May 21

Strähle U, Rastegar S (2008) Conserved non-coding sequences and transcriptional regulation. Brain Res Bull 75: 225–230

Editors’ conference agenda

I spent May 14-15th at the Symposium on Integrative Network Biology and Cancer, hosted by the Institute of Cancer Research in London. The organizers, Chris Bakal and Rune Linding, managed to attract a stellar speakers list, and I had great discussions with many of the attendees. Inspired by this, I thought it could be useful to share a tentative list of conferences in 2010 that will be attended by the Molecular Systems Biology editors. If you happen to be at one these conferences, we would be delighted to meet you in person and hear about your research.

Please note that this is a tentative schedule. Moreover, please do not feel slighted if your favorite conference is not on this list. There are many high-quality conferences that we will not be able to attend this year due to scheduling limitations.

<table border=“0” cellpadding=“4” cellspacing=“0” width = “100%”>

Conference

Place

Date

Who Systems Biology & New Sequencing Technologies Barcelona June 16-18 TL 8th International Conference on Pathways, Networks, and Systems Medicine Rhodes July 9-14 TL <a href = “http://www.iscb.org/ismb2010”>ISMB 2010 Boston July 11-13 ALH q-bio Conference on Cellular Information Processing Santa Fe August 11-14 ALH The EMBO Meeting 2010 Barcelona Sept. 4-7 TL CSHL Personal Genomes Cold Spring Harbor Sept. 10-12 ALH HUPO2010 Sydney Sept. 19-23 TL 11th International Conference on Systems Biology Edinburgh Oct. 11-14 TL EMBO Conference: From Functional Genomics to Systems Biology Heidelberg Nov. 13-16 ALH Pharmacogenomics & Personalized Therapy Cold Spring Harbor Nov. 17-21 TL

TL: Thomas Lemberger, ALH: Andrew L. Hufton

[Research highlight] Mycoplasma rebooted

Upshot of a series of four papers published over the last years (Gibson et al, 2010, Lartigue et al, 2009, Gibson et al, 2008, Lartigue et al, 2007), J. Craig Venter’s team now reports the successful transplantation of a chemically synthesized genome into a host bacterial cell (Gibson et al, 2010). As proof of principle, a slightly altered Mycoplasma mycoides genome (JCVI-syn1.0) was synthesized, assembled and transplanted into M. capricolum recipient cells.

This achievement results from the integration of several techniques developed in previous works: 1) a hierarchical strategy to assemble, via homologous recombination in yeast, a full genome from chemically synthesized overlapping fragments (Gibson et al, 2008); 2) a method to transform a full genome into a host cell and replace the recipient genome by the donor genome (‘transplantation’, Lartigue et al 2007); 3) a method to transplant DNA engineered in yeast into bacteria without being inactivated by the host restriction system (Lartigue et al, 2009). Finally, in the last work, systematic debugging methods were needed to identify a single base pair deletion that prevented productive transplantation (Gibson et al 2010).

The experiment represents certainly a highly symbolic milestone. A fascinating potential of this technology, if generalized and automated, is to enable the introduction of many genomic alterations simultaneously and, thus, to be able to reprogram cellular phenotypes with non-trivial genetic combinations that would have been impossible to identify with a sequential gene by gene approach. In this sense, while technically and ‘philosophically’ distinct, Venter’s approach appears complementary to multiplexed mutagenesis technologies that introduce simultaneously multiple modifications in a target genome (Wang et al, 2009).


Gibson DG, Glass JI, Lartigue C, Noskov VN, Chuang RY, Algire MA, Benders GW, Montague MG, Ma L, Moodie MM, Merryman C, Vashee S, Krishnakumar R, Assad-Garcia N, Andrews-Pfannkoch C, Denisova EA, Young L, Qi, ZQ, Segall-Shapiro TH, Calvey CH, Parmar PP, Hutchison CA, Smith HO, Venter JC (2010). Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome. Science advance online publication doi: 10.1126/science.1190719

Lartigue C, Vashee S, Algire MA, Chuang RY, Benders GA, Ma L, Noskov VN, Denisova EA, Gibson DG, Assad-Garcia N, Alperovich N, Thomas DW, Merryman C, Hutchison CA 3rd, Smith HO, Venter JC, Glass JI (2009). Creating bacterial strains from genomes that have been cloned and engineered in yeast. Science 325:1693

Gibson DG, Benders GA, Andrews-Pfannkoch C, Denisova EA, Baden-Tillson H, Zaveri J, Stockwell TB, Brownley A, Thomas DW, Algire MA, Merryman C, Young L, Noskov VN, Glass JI, Venter JC, Hutchison CA 3rd, Smith HO (2008). Complete chemical synthesis, assembly, and cloning of a Mycoplasma genitalium genome. Science 319:1215

Lartigue C, Glass JI, Alperovich N, Pieper R, Parmar PP, Hutchison CA 3rd, Smith HO, Venter JC (2007). Genome transplantation in bacteria: changing one species to another. Science 317:632

Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G, Forest CR, Church GM (2009). Programming cells by multiplex genome engineering and accelerated evolution. Nature 460:894

Impact Factors 2008

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

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)

thumb090202a.jpg

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

thumb090202b.jpg

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