St. John's College, the q-bio venue

• 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 determining the efficiency of siRNA and microRNA target gene repression. (See also her recent work in Molecular Systems Biology, Arvey et al. 2010).

• 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.

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

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.

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.

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

A controversy seems to be brewing over some recent theories and quantitative analyses addressing the fundamental question of how the Bicoid morphogen gradient is established and decoded in early Drosophila embryos. The transcription factor Bicoid controls the anterior-posterior patterning of the developing embryo. It is translated from maternal mRNA localized at the anterior pole of the egg and its graded distribution activates, in a concentration-dependent manner, the expression of gap genes, thus determining their spatial domain of expression. Synthesis from a localized source combined with diffusion and uniform degradation of the Bicoid morphogen provides one of the simplest models to explain the approximately exponential shape of its gradient. While, historically, patterning has been thought to rely on the gradient at its steady state – that is when synthesis, transport and degradation processes balance each other – the question arose as to whether steady-state can be reached rapidly enough in the quickly developing embryo (Lander, 2007).

In February last year, Naama Barkai and colleagues published a study (Bergmann et al, 2007) in which they propose that the gradient would in fact be interpreted before it has reached its steady-state, when the gradient is still "moving". Experimental evidence for a dynamic evolution of Bcd profile between cleavage cycle 11 and 12 is provided using a reporter gene driven by bicoid-binding sites. These authors further show that a pre-steady-state model implies a reduced sensitivity of the gradient readout to variations in the production of morphogen at its source. One biologically relevant example of this robustness is the observation that the domain of expression of hunchback, a Bicoid target gene, shifts much less in embryos from mothers with altered bicoid gene dosage than would be predicted by a steady-state model.

A few months later, Thomas Gregor and colleagues published two papers (Gregor et al, 2007a, 2007b) reporting a detailed analysis of the profile and dynamics of the Bicoid gradient. Quantitative in vivo imaging of a transgenic bicoid-eGFP reporter revealed several paradoxes. While a stable gradient of nuclear Bicoid is quickly established (within 90min, approx. cleavage cycle 9), the (local) diffusion coefficient of Bicoid, as deduced from photobleaching experiments, appears to be far too small (D=0.3 μm²/s, much less than expected from previous estimations made by injecting labeled dextran molecules) to be compatible with such a rapid establishment of the (long-range) gradient by diffusion alone. These experiments further show that nuclear Bicoid is under a highly dynamic nuclocytoplasmic equilibrium, pointing to a fundamental role for the nucleus in gradient establishment and stability. Finally, the precision with which the Bicoid gradient is transformed into Hunchback expression (see illustration, after Gregor et al 2007b) is estimated to be around 10%. This remarkable level of precision would not only be close to the physical limits of the system, but also strikingly matches the accuracy required to detect changes of Bicoid expression between adjacent cells (10%, equivalent to a difference of only 70 Bicoid molecules per nucleus) and the level of reproducibility of the absolute morphogen concentration from embryo to embryo (10% as well).

In a Correspondence published last week, Bergmann and colleagues (2008) dispute these interpretations and claim that a "reanalysis of their [Gregor et al's] data demonstrates that their findings are consistent with the well-accepted paradigm of diffusion-based patterning and provides further support for the notion that the Bicoid profile is decoded prior to reaching its steady state". Thus, according to these authors, constant nuclear Bicoid levels are not indicative of steady-state of the gradient itself given that cytoplasmic levels may still be changing. The small diffusion coefficient of Bicoid would then be an additional argument in favor of the necessity of a pre-steady-state decoding mechanism. If this is the case, the differences in Bicoid levels between adjascent cells would be much bigger at cleavage cycle 9 (50% instead of 10% at cycle 14), thus resolving the paradox of the high precision of the hunchback response.

In their response (Bialek et al, 2008), Gregor and colleagues reply that if cells would make a decision by reading Bicoid concentration at cycle 9, the boundary between expression domains would be 5 cells wide at stage 14 (=\sqrt{2^14/2^9}), while in reality it is only a single cell wide. While they agree that the overall gradient might not be at steady-state at these early stages, they argue that the stability of nuclear Bicoid levels is functionally highly relevant given that Bicoid is a transcription factor. Finally, they also point out that the deduced local diffusion constant is so small that it is in fact incompatible with observing any Bicoid in the middle of the embryo in the first place, thus suggesting the existence of additional mechanisms to explain establishment of the gradient at the scale of the entire embryo. These and some additional arguments lead Bialek et al to conclude that "the small values of the diffusion constant for Bcd we reported are superficially consistent with their model, but the model provides no basis for understanding any of our observations."

Mmmmh... not an easy one. Those who have additional insights into these subtle but fascinating questions, please let us know!

By James CW Locke, California Institute of Technology

A new Science paper from the lab of Alex Webb (Dodd et al, Science, 2007) represents an important step forward in plant circadian research (read also commentary by Imaizumi et al, Science, 2007). The circadian (24 h) clock controls processes throughout the day and night in most organisms, and in plants is involved in multiple pathways including photosynthesis, leaf movement and floral opening. The circadian clock has evolved to consist of multiple interlocking transcriptional feedback loops (at least in eukaryotes), which generate the 24 h rhythm even under constant environmental conditions.

Using a series of elegant experiments Dodd et al uncover a new level of complexity to the plant clock. They first show that cytosolic signaling molecule cyclic adenosine diphosphate ribose (cADPR) is regulated by the clock and is responsible for the previously reported circadian rhythm in intracellular calcium. They go on to show that disruption of cADPR signaling by addition of nicotinamide causes a strong period lengthening of the clock. Thus they have discovered an additional feedback loop (see drawing), and revealed a new class of circadian clock components: cytosolic signaling molecules.

It is also of note that Dodd et al use an existing mathematical model of the Arabidopsis clock (Locke et al, Mol Syst Biol, 2005) to frame the expected effects of their predicted feedback loop. A large amount of systems modeling work has recently been carried out on the clock (summarized in Ueda, Mol Syst Biol, 2006). It is exciting to see how the Arabidopsis clock community has responded to this work. In some cases the models have been used to simulate experiments and to test putative mechanisms (Dodd et al, Science, 2007, Martin-Tryon et al, Plant Physiol, 2007). I believe for systems biology approaches to succeed it is crucial that models must be made easily accessible to experimentalists, which is the case in this field (eg Circadian Modelling). I look forward to seeing the next iteration of the plant circadian clock model, perhaps from the Webb lab including the cADPR feedback loop.

Nature Neuroscience has a nice special focus on glia and disease. The featured reviews and perspective articles discuss multiple aspects of neuron-glia interactions and their role in disease. The reason why I am highlighting this collection here is that I have the feeling that this field could potentially be a nice playground for systems biology.

For example, Rossi and colleagues (2007) review the various metabolic processes affected during brain ischemia. Several of the examples discussed illustrate very well how the extent of brain damage is determined by the concurrent dynamics of both harmful and protective processes engaging complex interactions between neurons and astrocytes. A critical determinant for ischemic damage is the catastrophic loss of ATP levels caused by deficient glucose and oxygen delivery. Astrocytes have glycogen stores that can normally be converted to lactate which is exported to neurons to provide energy during phases of high activity. In absence of oxygen however, lactate can no longer be oxidized. In this case, glucose may then help delay loss of ATP levels, via anaerobic glycolysis. But this beneficial effect might be counteracted by lactic acidosis caused by continued glycolysis in the absence of O₂, which is known to accentuate ischemic damage in the case of hyperglycemia. Moreover, acidosis may activate Na⁺-H⁺ exchange, cytosolic Na⁺ accumulation, reversal of Na⁺-Ca²⁺ exchange resulting in astrocyte Ca²⁺ overload, either impairing their protective functions or even killing them.

A similar complexity is seen in the events underlying ischemic glutamate release. Loss of cellular ATP levels impairs the function of the Na⁺-K⁺ ATPase and thus disrupts ionic gradients. The resulting depolarization leads to a large increase in extracellular glutamate that is amplified by positive feedback, ultimately resulting in neuronal death by excitotoxicity. Astrocytes may contribute to increased extracellular glutamate levels via direct vesicular glutamate release and vesicular ATP release that in turn activates glutamate-permeable P2X receptors. Glutamate reuptake is normally carried out by five high-affinity sodium-dependent glutamate transporters. Disruption of transmembrane potential and of ionic gradients can cause transporter reversal thus further contributing to glutamate release. This depends in turn on the intracellular glutamate concentration which is much higher in astrocytes than neurons, determining the relative kinetic of neuronal and astrocytic reuptake/release as the ischemic perturbations progress. Further details are visible on Figure 3 from Rossi et al (2007):

Even if this short overview is condensed and incomplete, it suggests to me that quantitative measurements and integrated modeling could be quite helpful, if feasible, to understand the various contributions of the many processes involved and to identify potential points of protective synergies or characterize regimes under which the stability of the astrocyte-neuron system is catastrophically compromised. Perhaps this type of model and its calibration could even serve as a starting point to investigate the involvement of astrocytes in computational aspect of neuronal functions (Wang et al, 2006).

There were many interesting talks during these two last days at the First q-bio Conference on cellular information processing. Too many in fact and I can only try to give some excerpts, reflecting more the limitations of my memory than anything else...

John Reinitz presented his work on the quantitative analysis of gene expression in Drosophila early embryo. He first mentioned the FlyEx database, which collects quantitative data on the segmentation gene expression patterns (Poustelnikova, et al 2004) and allows to investigate how errors in segmentation gene expression and cell specification can be corrected. One interesting observation is that the inter-individual variations in the distribution of the bicoid morphogen gradient are significantly larger than the variations observed in the boundary of the downstream gap gene hunchback (Houchmandzadeh et al, 2002). With a model of gap gene expression (Jaeger et al, 2004a, 2004b), it is possible to show that this property of filtering noise from the morphogen's positional information emerges from the dynamical properties of the system itself. John Reinitz proposed that this phenomenon represents an example of "canalization" of gene expression, a term first coined by Waddington (1942, Nature 150:563) to metaphorically describe how cell specification corresponds to navigating down the valleys of an "epigenetic landscape" that guides the cell to its final (stable) fate, in spite of initial variations.

Alexander Hoffmann studies the properties of the NF-kB signaling pathway and discussed how its dynamical properties may underly the stimulus-specificity of the response to major inflammatory mediators like TNFa, LPS or IL-1. These stimuli induce different temporal profiles of activation of the component of the pathway (Werner et al, 2005). He showed how the systematical analysis--both by varying the shapes of IKK activation profiles and by parameter sensitivity analysis--of a detailed model could provide insight into the determinants of stimulus-specificity of the response. Experimentally, the use of a specific mutant of NF-kappaB with altered temporal control leads to a loss of stimulus-specificity for 50% of those NF-kappaB target genes that normally exhibit specificity. Alexander Hoffmann also discussed further how the understanding of the dynamical mechanisms underlying stimulus-specificity may enable novel strategies to develop multi-target drugs interfering with only one stimulus-specific branch of the pathway.

In a fast paced talk, Bernhard Palsson presented the last progresses in elucidating E. coli transcriptional regulatory network. Using high-density genome tiling arrays and chromatin immunoprecipitation, his team produced a very impressive series of high-resolution genome-wide location maps of binding sites for the RNA polymerase (RNAP), many transcription factors and several DNA-binding proteins. These experiments were performed under various environmental conditions and in parallel with the respective mRNA expression profiles (also via tiling arrays). In addition, RNAP location was determined in presence and absence of Rifampicin to contrast a static map of bound RNAP with a map of the elongating polymerase. Integration of these various dataset will allow to extract causal relationships between the various binding events and gene expression, providing the blueprint of the global bacterial regulatory transcriptional network. Further integration (Barrett et al, 2005) with the reconstructed genome-wide metabolic network (Feist et al, 2007) will connect transcriptional and metabolic functional states, enabling a top-down approach to identify the regulatory needs of the bacterial cell.

Johan Paulsson uses regulation of plasmid copy number as a simple model system to investigate some fundamental aspects of negative feedback regulation. Plasmids have to keep their copy number low to avoid excessive burden to the cell but have also to suppress the resulting fluctuations, which may cause plasmid loss at cell division. Johan Paulsson showed in a very general way that the time delay inevitably associated with negative feedback imposes a lower limit to fluctuations (essentially the delay provides a window during which noise accumulation cannot be prevented). The first surprise is that these general theoretical limits are relatively high: with 4 copies, plasmid fluctuations are at least 5-15%. The second surprise is that when plasmid copy number is measured experimentally at the single cell level (a difficult task, requiring new techniques developed in Paulsson's lab), it appears that plasmids suppress fluctuation remarkably close to the theoretical limit.

Again, there were many other great talks and there sure will be tomorrow--unfortunately I will not be able to attend tomorrow's sessions :-(. I hope that the few examples above illustrate the exceptional quality of this conference and the kind of insight gained from a quantitative study of "small-scale" systems. Only a handful of presentations (BO Palsson, R Linding, B Blagoev) dealt with genome-wide large scale models and datasets--the other end of the spectrum in systems biology. While the organizers told me that this was in part a deliberate choice, it also reminded me that it will probably represent one of the key challenges in systems biology to find ways to bridge the gap between the dynamical properties of small systems and the architecture of large biological networks.

The First q-bio Conference on cellular information processing, Santa Fe, New Mexico started today with an opening lecture by William Bialek. Here is an attempt to provide a brief account of his beautiful talk.

Let's imagine a biological system to be modeled, says William Bialek, and for which sufficient experimental data are available to determine the relevant model parameters: the model can be "located" within a given region of the parameter space. Bialek asks: should we really be satisfied with the observation that this particular set of parameters fits the experimental data or is the fact that the model is located in this particular region of the parameter space tells us something more fundamental? Given that living organisms are shaped by evolutionary processes, which acts on biological functions (well, at least in part, see Lynch, 2007...), finding an appropriate notion of biological functionality would define some "metric" on the parameter space that would tell us why the living organisms is sitting in this particular region. But how can this concept of functionality be defined and quantified?

As concrete examples, Bialek mentioned the typical sigmoidal intput-output curves observed in neurobiology (eg photoreceptor membrane voltage in function of light stimulus, action potential frequency of visual neurons in function of a stimulus feature like motion) or in molecular biology (for example the expression levels of a target gene in function of transcription factor concentration). Why do these input-output functions have this shape and what are the general rules, independent of underlying molecular mechanisms, that explains where the midpoint of the curve is, its width etc... It appears that one of the keys to this problem is to look at the distribution of inputs received by the system in its "natural context" and analyze how this distribution matches the shape of the output function, something that was initially done by Laughlin (A simple coding procedure enhances a neuron’s information capacity. Z Naturforsch 1981 36c, 910–912). One can then ask the question: "how much information does the output provide about the input?" To do so, the level of resolution with which the output can distinguish between certain number of levels has to be taken into account, given the noise in the system. As it turns out, given the observed distribution of input, imposing maximal information transmission leads precisely to the observed input-output curve. The system seems to have been optimized for transmission of information, which represents a very general constraint. Wiliam Bialek went on with additional examples to show that the input-output curve may in fact, for the same system, depend on the context (the distribution of input can change over time) and to demonstrate how this type of analysis also extends to transcriptional regulation using the Bicoid-Hunchback as a model system.

The talk was followed by a quite animated discussion on how to interpret the observation that these particular systems appear to have optimized information transmission. Is optimization of information transmission the only key? Probably not... But William Bialek's very insightful lecture illustrated what sort of fundamental and general insight can be gained from the combination of a theoretical approach to a simple biological model system. And the resulting discussion also showed that even if the idealized theory might be wrong, the confrontation between theory and reality of biological systems serves to reveal those aspects of a system that are not sufficiently deeply understood.

How do proofreading and repair activities emerge and what is their impact on system-level properties such as robustness or evolvability?

Biological networks and the Internet may share common architectural principles common to "robust yet fragile" systems (Doyle and Csete, 2007). But the Internet does not (yet) repair itself while cells or tissues do. Repair may represent an example of "downward causation" (The Music of Life, Noble 2006 or a book review) in which a high-level functional property (eg repair activity) emerging from a multi-component system (eg the DNA repair machinery) acts on a component at a lower level of organization (eg one nucleotide). In other words, could repair be considered as an example of a "cross-scale" feedback motif? Can these types of motifs be generalized and how would they evolve? The topic could in fact be extended to the field of synthetic biology: how to assemble synthetic systems with self-repair capability?

Many questions... Who has answers?

see also: Fidelity and infidelity (2001) Radman, 2001

How does the connectivity of biochemical networks influence the response to drug combinations?
To address this question, Joseph Lehár and colleagues analyzed the response of yeast and human tumour cell lines to thousands of drug pairs, and compared these experimental data to a computational model (Lehár et al, 2007) . By systematically measuring the biological effects of drug pairs over a range of concentrations, 'response surfaces' could be constructed. This strategy enabled the authors to establish a clear relationship between the shape of the biological response surface and the type of connection linking the targeted biochemical pathways. This dependancy can be used to learn how biological systems are connected as well as to elucidate the mechanism of multi-drug treatments, which are increasingly preferred for many diseases.

It will be interesting to see how far this kind of analysis takes us in the real world: how much will 'off-target' effects confound the analysis? To what extent is the mapping between network topologies and response surfaces a one-to-one function? Further, it would be important to extend this idea to three or more drug components. (Yeh and Kishony, 2007)