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1.  Clinical activity and safety of combination therapy with temsirolimus and bevacizumab for advanced melanoma: A Phase II trial (CTEP 7190/Mel47) 
Purpose
A CTEP-sponsored phase II trial was performed to evaluate safety and clinical activity of combination therapy with CCI-779 (temsirolimus) and bevacizumab in patients with advanced melanoma.
Experimental Design
Patients with unresectable stage III to IV melanoma were treated intravenously with temsirolimus 25mg weekly and bevacizumab 10mg every 2 weeks. Adverse events were recorded using CTCAE v3.0. Tumor response was assessed by Response Evaluation Criteria in Solid Tumors, and overall survival was recorded. Correlative studies measured protein kinases and histology of tumor biopsies, and immune function in peripheral blood.
Results
Seventeen patients were treated. Most patients tolerated treatment well, but two had grade 4 lymphopenia and one developed reversible grade 2 leukoencephalopathy. Best clinical response was partial response (PR) in three patients (17.7%, 90%CI 5,0–39.6), stable disease at 8 weeks (SD) in 9 patients, progressive disease (PD) in 4 patients, and not evaluable in 1 patient. Maximal response duration for PR was 35 months. Ten evaluable patients had BRAFWT tumors, among whom 3 had PRs, 5 had SD, and 2 had PD. Correlative studies of tumor biopsies revealed decreased phospho-S6K (d2 and d23 vs d1, p<0.001), and decreased mitotic rate (Ki67+) among melanoma cells by d23 (p=0.007). Effects on immune functions were mixed, with decreased alloreactive T cell responses and decreased circulating CD4+FoxP3+ cells.
Conclusion
These data provide preliminary evidence for clinical activity of combination therapy with temsirolimus and bevacizumab, which may be greater in patients with BRAFwt melanoma. Mixed effects on immunologic function also support combination with immune therapies.
doi:10.1158/1078-0432.CCR-12-3919
PMCID: PMC3700572  PMID: 23620404
melanoma; mTOR; VEGF; targeted therapy; biomarkers; BRAF; clinical outcome
2.  Targeting Src in Mucinous Ovarian Carcinoma 
PURPOSE
Mucinous ovarian carcinomas have a distinct clinical pattern compared to other subtypes of ovarian carcinoma. Here, we evaluated (i) stage-specific clinical significance of mucinous ovarian carcinomas in a large cohort and (ii) the functional role of src kinase in pre-clinical models of mucinous ovarian carcinoma.
EXPERIMENTAL DESIGN
1302 ovarian cancer patients including 122 (9.4%) cases of mucinous carcinoma were evaluated for survival analyses. Biological effects of src kinase inhibition were tested in a novel orthotopic mucinous ovarian cancer model (RMUG-S-ip2) using dasatinib-based therapy.
RESULTS
Patients with advanced-stage mucinous ovarian cancer had significantly worse survival compared to those with serous histology: median overall survival, 1.67 versus 3.41 years, p=0.002; and median survival time after recurrence of 0.53 versus 1.66 years, p<0.0001. Among multiple ovarian cancer cell lines, RMUG-S-ip2 mucinous ovarian cancer cells showed the highest src kinase activity. Moreover, oxaliplatin treatment induced phosphorylation of src kinase. This induced activity by oxaliplatin therapy was inhibited by concurrent administration of dasatinib. Targeting src with dasatinib in vivo showed significant anti-tumor effects in the RMUG-S-ip2 model, but not in the serous ovarian carcinoma (SKOV3-TR) model. Combination therapy of oxaliplatin with dasatinib further demonstrated significant effects on reducing cell viability, increasing apoptosis, and in vivo anti-tumor effects in the RMUG-S-ip2 model.
CONCLUSIONS
Our results suggest that poor survival of women with mucinous ovarian carcinoma is associated with resistance to cytotoxic therapy. Targeting src kinase with combination of dasatinib and oxaliplatin may be an attractive approach in this disease.
doi:10.1158/1078-0432.CCR-10-3176
PMCID: PMC4028171  PMID: 21737505
ovarian cancer; mucinous; oxaliplatin; dasatinib; src kinase
3.  Mapping Network Motif Tunability and Robustness in the Design of Synthetic Signaling Circuits 
PLoS ONE  2014;9(3):e91743.
Cellular networks are highly dynamic in their function, yet evolutionarily conserved in their core network motifs or topologies. Understanding functional tunability and robustness of network motifs to small perturbations in function and structure is vital to our ability to synthesize controllable circuits. In establishing core sets of network motifs, we selected topologies that are overrepresented in mammalian networks, including the linear, feedback, feed-forward, and bifan circuits. Static and dynamic tunability of network motifs were defined as the motif ability to respectively attain steady-state or transient outputs in response to pre-defined input stimuli. Detailed computational analysis suggested that static tunability is insensitive to the circuit topology, since all of the motifs displayed similar ability to attain predefined steady-state outputs in response to constant inputs. Dynamic tunability, in contrast, was tightly dependent on circuit topology, with some motifs performing superiorly in achieving observed time-course outputs. Finally, we mapped dynamic tunability onto motif topologies to determine robustness of motif structures to changes in topology and identify design principles for the rational assembly of robust synthetic networks.
doi:10.1371/journal.pone.0091743
PMCID: PMC3958390  PMID: 24642504
4.  Tumour angiogenesis regulation by the miR-200 family 
Nature communications  2013;4:2427.
The miR-200 family is well known to inhibit the epithelial–mesenchymal transition, suggesting it may therapeutically inhibit metastatic biology. However, conflicting reports regarding the role of miR-200 in suppressing or promoting metastasis in different cancer types have left unanswered questions. Here we demonstrate a difference in clinical outcome based on miR-200's role in blocking tumour angiogenesis. We demonstrate that miR-200 inhibits angiogenesis through direct and indirect mechanisms by targeting interleukin-8 and CXCL1 secreted by the tumour endothelial and cancer cells. Using several experimental models, we demonstrate the therapeutic potential of miR-200 delivery in ovarian, lung, renal and basal-like breast cancers by inhibiting angiogenesis. Delivery of miR-200 members into the tumour endothelium resulted in marked reductions in metastasis and angiogenesis, and induced vascular normalization. The role of miR-200 in blocking cancer angiogenesis in a cancer-dependent context defines its utility as a potential therapeutic agent.
doi:10.1038/ncomms3427
PMCID: PMC3904438  PMID: 24018975
5.  Network Motifs in JNK Signaling 
Genes & Cancer  2013;4(9-10):409-413.
Multiple growth factors and extracellular signals can lead to activation of the c-Jun amino N-terminal protein kinase (JNK) pathway. Activation of JNK can in turn lead to a multitude of downstream changes in phosphorylation and transcriptional activation within the cell. Mapping the upstream and downstream connectivity within the JNK network reveals an enrichment of bi-fan and feed-forward network motifs formed immediately upstream and downstream of JNK. In addition, negative feedback loops also exist through transcriptional activation of phosphatases that target the JNK pathway. The combinations of these motifs allow flexibility and tunability in signal integration and processing within the JNK network and may confer the wide range of biological responses that can be regulated by JNK activation. In this review, we highlight the pathways and motifs leading to JNK activation and its downstream signaling as well as the complexity in isoforms within this network.
doi:10.1177/1947601913507577
PMCID: PMC3863341  PMID: 24349639
networks; motifs; JNK pathway; signaling
6.  Bioinformatics and systems biology 
Molecular Oncology  2012;6(2):147-154.
doi:10.1016/j.molonc.2012.01.008
PMCID: PMC3345061  PMID: 22377422
7.  Silencing of p130Cas in Ovarian Carcinoma: A Novel Mechanism for Tumor Cell Death 
Background
We investigated the clinical and biological significance of p130cas, an important cell signaling molecule, in ovarian carcinoma.
Methods
Expression of p130cas in ovarian tumors, as assessed by immunohistochemistry, was associated with tumor characteristics and patient survival. The effects of p130cas gene silencing with small interfering RNAs incorporated into neutral nanoliposomes (siRNA-DOPC), alone and in combination with docetaxel, on in vivo tumor growth and on tumor cell proliferation (proliferating cell nuclear antigen) and apoptosis (terminal deoxynucleotidyl transferase dUTP nick-end labeling) were examined in mice bearing orthotopic taxane-sensitive (HeyA8 and SKOV3ip1) or taxane-resistant (HeyA8-MDR) ovarian tumors (n = 10 per group). To determine the specific mechanisms by which p130cas gene silencing abrogates tumor growth, we measured cell viability (MTT assay), apoptosis (fluorescence-activated cell sorting), autophagy (immunoblotting, fluorescence, and transmission electron microscopy), and cell signaling (immunoblotting) in vitro. All statistical tests were two-sided.
Results
Of 91 ovarian cancer specimens, 70 (76%) had high p130cas expression; and 21 (24%) had low p130cas expression. High p130cas expression was associated with advanced tumor stage (P < .001) and higher residual disease (>1 cm) following primary cytoreduction surgery (P = .007) and inversely associated with overall survival and progression-free survival (median overall survival: high p130cas expression vs low expression, 2.14 vs 9.1 years, difference = 6.96 years, 95% confidence interval = 1.69 to 9.48 years, P < .001; median progression-free survival: high p130cas expression vs low expression, 1.04 vs 2.13 years, difference = 1.09 years, 95% confidence interval = 0.47 to 2.60 years, P = .01). In mice bearing orthotopically implanted HeyA8 or SKOV3ip1 ovarian tumors, treatment with p130cas siRNA-DOPC in combination with docetaxel chemotherapy resulted in the greatest reduction in tumor growth compared with control siRNA therapy (92%–95% reduction in tumor growth; P < .001 for all). Compared with control siRNA therapy, p130cas siRNA-DOPC reduced SKOV3ip1 cell proliferation (31% reduction, P < .001) and increased apoptosis (143% increase, P < .001) in vivo. Increased tumor cell apoptosis may have persisted despite pan-caspase inhibition by the induction of autophagy and related signaling pathways.
Conclusions
Increased p130cas expression is associated with poor clinical outcome in human ovarian carcinoma, and p130cas gene silencing decreases tumor growth through stimulation of apoptotic and autophagic cell death.
doi:10.1093/jnci/djr372
PMCID: PMC3206039  PMID: 21957230
8.  The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells 
This study implicates the glucose deprivation response in breast cancer cell resistance to lapatinib and high relapse rates in Her2-positive patients. Identification of these compensatory networks suggests novel strategies to target cancer signaling and metabolism.
Increased expression of the glucose deprivation response network, including glucagon signaling, glucose uptake, gluconeogenesis and unfolded protein response genes is found in breast cancer cells with acquired resistance to lapatinib.The glucose deprivation response gene network correlated significantly with high clinical relapse rates in ErbB2-positive breast cancer patients.Chemical genomics bioinformatics data mining identified drugs that target the glucose deprivation response networks to reduced survival of resistant cells.
Dynamic interactions between intracellular networks regulate cellular homeostasis and responses to perturbations. Targeted therapy is aimed at perturbing oncogene addiction pathways in cancer, however, development of acquired resistance to these drugs is a significant clinical problem. A network-based computational analysis of global gene expression data from matched sensitive and acquired drug-resistant cells to lapatinib, an EGFR/ErbB2 inhibitor, revealed an increased expression of the glucose deprivation response network, including glucagon signaling, glucose uptake, gluconeogenesis and unfolded protein response in the resistant cells. Importantly, the glucose deprivation response markers correlated significantly with high clinical relapse rates in ErbB2-positive breast cancer patients. Further, forcing drug-sensitive cells into glucose deprivation rendered them more resistant to lapatinib. Using a chemical genomics bioinformatics mining of the CMAP database, we identified drugs that specifically target the glucose deprivation response networks to overcome the resistant phenotype and reduced survival of resistant cells. This study implicates the chronic activation of cellular compensatory networks in response to targeted therapy and suggests novel combinations targeting signaling and metabolic networks in tumors with acquired resistance.
doi:10.1038/msb.2012.25
PMCID: PMC3421441  PMID: 22864381
bioinformatics; computational methods; functional genomics; metabolic and regulatory networks; signal transduction
9.  NetWalker: a contextual network analysis tool for functional genomics 
BMC Genomics  2012;13:282.
Background
Functional analyses of genomic data within the context of a priori biomolecular networks can give valuable mechanistic insights. However, such analyses are not a trivial task, owing to the complexity of biological networks and lack of computational methods for their effective integration with experimental data.
Results
We developed a software application suite, NetWalker, as a one-stop platform featuring a number of novel holistic (i.e. assesses the whole data distribution without requiring data cutoffs) data integration and analysis methods for network-based comparative interpretations of genome-scale data. The central analysis components, NetWalk and FunWalk, are novel random walk-based network analysis methods that provide unique analysis capabilities to assess the entire data distributions together with network connectivity to prioritize molecular and functional networks, respectively, most highlighted in the supplied data. Extensive inter-operability between the analysis components and with external applications, including R, adds to the flexibility of data analyses. Here, we present a detailed computational analysis of our microarray gene expression data from MCF7 cells treated with lethal and sublethal doses of doxorubicin.
Conclusion
NetWalker, a detailed step-by-step tutorial containing the analyses presented in this paper and a manual are available at the web site http://netwalkersuite.org.
doi:10.1186/1471-2164-13-282
PMCID: PMC3439272  PMID: 22732065
Biological networks; NetWalker; NetWalk; Network analyses
10.  Kinome siRNA-phosphoproteomic screen identifies networks regulating AKT signaling 
Oncogene  2011;30(45):4567-4577.
To identify regulators of intracellular signaling we targeted 541 kinases and kinase-related molecules with siRNAs and determined their effects on signaling with a functional proteomics reverse phase protein array (RPPA) platform assessing 42 phospho and total proteins. The kinome wide screen demonstrated a strong inverse correlation between phosphorylation of AKT and MAPK with 115 genes that when targeted by siRNAs demonstrated opposite effects on MAPK and AKT phosphorylation. Network based analysis identified the MAPK subnetwork of genes along with p70S6K and FRAP1 as the most prominent targets that increased phosphorylation of AKT, a key regulator of cell survival. The regulatory loops induced by the MAPK pathway are dependent on TSC2 but demonstrate a lesser dependence on p70S6K than the previously identified FRAP1 feedback loop. The siRNA screen also revealed novel bi-directionality in the AKT and GSK3 interaction, whereby genetic ablation of GSK3 significantly blocks AKT phosphorylation, an unexpected observation as GSK3 has only been predicted to be downstream of AKT. This method uncovered novel modulators of AKT phosphorylation and facilitated the mapping of regulatory loops.
doi:10.1038/onc.2011.164
PMCID: PMC3175328  PMID: 21666717
AKT; MAPK; proteomics; signaling networks; siRNA
11.  Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis 
Cancer research  2010;70(17):6704-6714.
Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass-action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combinations that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combinations inhibited cell signaling and proliferation, while non-optimal combination of inhibitors increased phosphorylation of non-targeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combinations and discover novel pharmacologic targets for cancer therapy.
doi:10.1158/0008-5472.CAN-10-0460
PMCID: PMC2932856  PMID: 20643779
12.  Formation of Regulatory Patterns During Signal Propagation in a Mammalian Cellular Network 
Science (New York, N.Y.)  2005;309(5737):1078-1083.
We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.
doi:10.1126/science.1108876
PMCID: PMC3032439  PMID: 16099987
13.  Patterns of human gene expression variance show strong associations with signaling network hierarchy 
BMC Systems Biology  2010;4:154.
Background
Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV) of genes and their relationship to cellular functions and physiological responses is poorly understood.
Results
To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes.
Conclusion
Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.
doi:10.1186/1752-0509-4-154
PMCID: PMC2992512  PMID: 21073694
14.  Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data 
PLoS Computational Biology  2010;6(8):e1000889.
Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.
Author Summary
Analysis of high-content genomic data within the context of known networks of interactions of genes can lead to a better understanding of the underlying biological processes. However, finding the networks of interactions that are most relevant to the given data is a challenging task. We present a random walk-based algorithm, NetWalk, which integrates genomic data with networks of interactions between genes to score the relevance of each interaction based on both the data values of the genes as well as their local network connectivity. This results in a distribution of Edge Flux values, which can be used for dynamic reconstruction of user-defined networks. Edge Flux values can be further subjected to statistical analyses such as clustering, allowing for direct numerical comparisons of context-specific networks between different conditions. To test NetWalk performance, we carried out microarray gene expression analysis of MCF7 cells subjected to lethal and sublethal doses of a DNA damaging agent. We compared NetWalk to other network-based analysis methods and found that NetWalk was superior in identifying coherently altered sub-networks from the genomic data. Using NetWalk, we further identified p53-regulated networks that are differentially involved in cell cycle arrest and apoptosis, which we experimentally tested.
doi:10.1371/journal.pcbi.1000889
PMCID: PMC2924243  PMID: 20808879
15.  Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle 
BMC Systems Biology  2008;2:76.
Background
In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior.
Results
We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model.
Conclusion
PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models. The application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features. PathwayOracle is freely available for download and use.
doi:10.1186/1752-0509-2-76
PMCID: PMC2527501  PMID: 18713463
16.  The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks 
PLoS Computational Biology  2008;4(2):e1000005.
Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods.
Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity.
We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.
Author Summary
Many cellular behaviors including growth, differentiation, and movement are influenced by external stimuli. Such external stimuli are obtained, processed, and carried to the nucleus by the signaling network—a dense network of cellular biochemical reactions. Beyond being interesting for their role in directing cellular behavior, deleterious changes in a cell's signaling network can alter a cell's responses to external stimuli, giving rise to devastating diseases such as cancer. As a result, building accurate mathematical and computational models of cellular signaling networks is a major endeavor in biology. The scale and complexity of these networks render them difficult to analyze by experimental techniques alone, which has led to the development of computational analysis methods. In this paper, we present a novel computational simulation technique that can provide qualitatively accurate predictions of the behavior of a cellular signaling network without requiring detailed knowledge of the signaling network's parameters. Our approach makes use of recent discoveries that network structure alone can determine many aspects of a network's dynamics. When compared against experimental results, our method correctly predicted 90% of the cases considered. In those where it did not agree, our approach provided valuable insights into discrepancies between known network structure and experimental observations.
doi:10.1371/journal.pcbi.1000005
PMCID: PMC2265486  PMID: 18463702

Results 1-16 (16)