The crosstalk between EGF, IGF, and Insulin cell signaling pathways--computational and experimental analysis

Traversing through the Mechanistic Event Analysis in IL-6 and IL-17 Signaling for a New Therapeutic Paradigm in NSCLC

IL-6 and IL-17 are paradoxical cytokines that progress inflammatory states in chronic diseases, including cancer. In lung cancer, their role has been elucidated to favor cancer development by modulating signaling mechanisms critical to cellular growth. The intrinsic ability of these cytokines to influence macroautophagy is yet another reason to facilitate lung cancer. Here, we employed a systems immunology approach to discover the mechanistic role of these cytokines in cancer development. In a biological system, at later stages, the activation of NFkB stimulates immunosuppressive phenotypes to achieve tolerating effects in a transformed cell. We found that the upregulation of cytokines signaled M2 macrophages to modulate tumor responses through the activation of autophagic intermediates and inflammasome mediators. This caused immune perturbations in the tumor microenvironment, which were associated with cancer inflammation. To address these inflammatory states, we performed triggered event analysis to examine whether overexpressing immune effectors or downregulating immune suppressors may have an effect on cancer reversal. Interestingly, the inhibition of immune regulators opposed the model outcome to an increased immune response. Therefore, IL6-IL17-mediated regulation of lung cancer may address tumor malignancy and potentiate the development of newer therapeutics for NSCLC.

Extracting Potential New Targets for Treatment of Adenoid Cystic Carcinoma using Bioinformatic Methods

Background

Adenoid cystic carcinoma is a slow-growing malignancy that most often occurs in the salivary glands. Currently, no FDA-approved therapeutic target or diagnostic biomarker has been identified for this cancer. The aim of this study was to find new therapeutic and diagnostic targets using bioinformatics methods.

Methods

We extracted the gene expression information from two GEO datasets (including GSE59701 and GSE88804). Different expression genes between adenoid cystic carcinoma (ACC) and normal samples were extracted using R software. The biochemical pathways involved in ACC were obtained by using the Enrichr database. PPI network was drawn by STRING, and important genes were extracted by Cytoscape. Real-time PCR and immunohistochemistry were used for biomarker verification.

Results

After analyzing the PPI network, 20 hub genes were introduced to have potential as diagnostic and therapeutic targets. Among these genes, PLCG1 was presented as new biomarker in ACC. Furthermore, by studying the function of the hub genes in the enriched biochemical pathways, we found that insulin-like growth factor type 1 receptor and PPARG pathways most likely play a critical role in tumorigenesis and drug resistance in ACC and have a high potential for selection as therapeutic targets in future studies.

Conclusion

In this study, we achieved the recognition of the pathways involving in ACC pathogenesis and also found potential targets for treatment and diagnosis of ACC. Further experimental studies are required to confirm the results of this study.

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

Insulin Signaling Pathway Model in Adipocyte Cells

BACKGROUND

Worldwide, type 2 diabetes mellitus (T2DM) is one of the most pervasive and fastgrowing disorders, bringing long-term adverse effects. T2DM arises from pancreatic β-cells deficiency to produce enough insulin or when the body cannot effectively use the insulin produced by such cells. Accordingly, early diagnosis will decrease the long-term effects and high-healthcare costs of diabetes.

OBJECTIVE

The objective is developing an integrated mathematical model of the insulin signaling network based on Brännmark's model, which can simulate the signaling events more comprehensively with the added key components.

METHODS

In this study, a thorough mathematical model of the insulin signaling network was developed by expanding the previously validated model and incorporating the glycogen synthesis module. Parameters (69 parameters) of the integrated model were evaluated by a genetic algorithm by fitting the model predictions to eighty percent of experimental data from the literature. Twenty percent of the experimental data were used to evaluate the final optimized model.

RESULTS

The time-response curves indicate that the GS phosphorylation reaches its maximum in response to 10^-7 M insulin after 4 min, while the maximum phosphorylated GSK3 is attained within ~50 min. The doseresponse curves for the GSP and GSK3 of the insulin signaling intermediaries in response to the increased concentration of insulin, after 10 min, in the input from 0-100 nM exhibits a decreasing trend, whereas an increasing trend was observed for the GS and GSK3P. The GSK and GS phosphorylation sensitivity was enhanced by increasing the initial insulin concentration level from 0.001 to 100 nM. However, the sensitivity of GSK3 to insulin concentration changes (from 0.001 to 100 nM) was 3-fold higher than GS sensitivity.

CONCLUSION

Considerably, the trends of all signaling components simulated by the expanded model shows high compatibility with experimental data (R2 ≥ 0.9), which approves the accuracy of the proposed model. The proposed mathematical model can be used in many biological systems and combined with the whole-body model of the blood glucose regulation system for a better understanding of the causes and potential treatment of type 2 diabetes. Although, this model is not a complete description of insulin signaling, yet it can make profound contributions to improvements regarding other important components and signaling branches such as epidermal growth factor (EGF) signaling, as well as signaling in other cell types in the model structure of future works.

IGF-Binding Proteins and Their Proteolysis as a Mechanism of Regulated IGF Release in the Nervous Tissue

Insulin-like growth factors 1 and 2 (IGF-1 and IGF-2) play a key role in the maintenance of the nervous tissue viability. IGF-1 and IGF-2 exhibit the neuroprotective effects by stimulating migration and proliferation of nervous cells, activating cellular metabolism, inducing regeneration of damaged cells, and regulating various stages of prenatal and postnatal development of the nervous system. The availability of IGFs for the cells is controlled via their interaction with the IGF-binding proteins (IGFBPs) that inhibit their activity. On the contrary, the cleavage of IGFBPs by specific proteases leads to the IGF release and activation of its cellular effects. The viability of neurons in the nervous tissue is controlled by a complex system of trophic factors secreted by auxiliary glial cells. The main source of IGF for the neurons are astrocytes. IGFs can accumulate as an extracellular free ligand near the neuronal membranes as a result of proteolytic degradation of IGFBPs by proteases secreted by astrocytes. This mechanism promotes interaction of IGFs with their genuine receptors and triggers intracellular signaling cascades. Therefore, the release of IGF by proteolytic cleavage of IGFBPs is an important mechanism of neuronal protection. This review summarizes the published data on the role of IGFs and IGFBPs as the key players in the neuroprotective regulation with a special focus on the specific proteolysis of IGFBPs as a mechanism for the regulation of IGF bioavailability and viability of neurons.

An Evolutionary Paradigm Favoring Cross Talk between Bacterial Two-Component Signaling Systems

The prevalent paradigm governing bacterial two-component signaling systems (TCSs) is specificity, wherein the histidine kinase (HK) of a TCS exclusively activates its cognate response regulator (RR). Cross talk, where HKs activate noncognate RRs, is considered evolutionarily disadvantageous because it can compromise adaptive responses by leaking signals. Yet cross talk is observed in several bacteria. Here, to resolve this paradox, we propose an alternative paradigm where cross talk can be advantageous. We envisioned programmed environments, wherein signals appear in predefined sequences. In such environments, cross talk that primes bacteria to upcoming signals may improve adaptive responses and confer evolutionary benefits. To test this hypothesis, we employed mathematical modeling of TCS signaling networks and stochastic evolutionary dynamics simulations. We considered the comprehensive set of bacterial phenotypes, comprising thousands of distinct cross talk patterns competing in varied signaling environments. Our simulations predicted that in programmed environments phenotypes with cross talk facilitating priming would outcompete phenotypes without cross talk. In environments where signals appear randomly, bacteria without cross talk would dominate, explaining the specificity widely seen. Additionally, a testable prediction was that the phenotypes selected in programmed environments would display one-way cross talk, ensuring priming to future signals. Interestingly, the cross talk networks we deduced from available data on TCSs of Mycobacterium tuberculosis all displayed one-way cross talk, which was consistent with our predictions. Our study thus identifies potential evolutionary underpinnings of cross talk in bacterial TCSs, suggests a reconciliation of specificity and cross talk, makes testable predictions of the nature of cross talk patterns selected, and has implications for understanding bacterial adaptation and the response to interventions. IMPORTANCE Bacteria use two-component signaling systems (TCSs) to sense and respond to environmental changes. The prevalent paradigm governing TCSs is specificity, where signal flow through TCSs is insulated; leakage to other TCSs is considered evolutionarily disadvantageous. Yet cross talk between TCSs is observed in many bacteria. Here, we present a potential resolution of this paradox. We envision programmed environments, wherein stimuli appear in predefined sequences. Cross talk that primes bacteria to upcoming stimuli could then confer evolutionary benefits. We demonstrate this benefit using mathematical modeling and evolutionary simulations. Interestingly, we found signatures of predicted cross talk patterns in Mycobacterium tuberculosis. Furthermore, specificity was selected in environments where stimuli occurred randomly, thus reconciling specificity and cross talk. Implications follow for understanding bacterial evolution and for interventions.

In silico Prediction on the PI3K/AKT/mTOR Pathway of the Antiproliferative Effect of O. joconostle in Breast Cancer Models

The search for new cancer treatments from traditional medicine involves developing studies to understand at the molecular level different cell signaling pathways involved in cancer development. In this work, we present a model of the PI3K/Akt/mTOR pathway, which plays a key role in cell cycle regulation and is related to cell survival, proliferation, and growth in cancer, as well as resistance to antitumor therapies, so finding drugs that act on this pathway is ideal to propose a new adjuvant treatment. The aim of this work was to model, simulate and predict in silico using the Big Data-Cellulat platform the possible targets in the PI3K/Akt/mTOR pathway on which the Opuntia joconostle extract acts, as well as to indicate the concentration range to be used to find the mean lethal dose in in vitro experiments on breast cancer cells. The in silico results show that, in a cancer cell, the activation of JAK and STAT, as well as PI3K and Akt is related to the effect of cell proliferation, angiogenesis, and inhibition of apoptosis, and that the extract of O. joconostle has an antiproliferative effect on breast cancer cells by inhibiting cell proliferation, regulating the cell cycle and inhibiting apoptosis through this signaling pathway . In vitro it was demonstrated that the extract shows an antiproliferative effect, causing the arrest of cells in the G2/M phase of the cell cycle. Therefore, it is concluded that the use of in silico tools is a valuable method to perform virtual experiments and discover new treatments. The use of this type of model supports in vitro experimentation, reducing the costs and number of experiments in the real laboratory.

Computational System Level Approaches for Discerning Reciprocal Regulation of IL10 and IL12 in Leishmaniasis

IL12 and IL10 are two of the major cytokines which control the fate of Leishmaniasis. This paper presents two models healthy state and diseased state which shows how secretion of IL12 is responsible for parasite elimination and IL10 can jeopardize the parasite elimination and promote its survival. Epigenetic modification in the host IL12 and IL10 promoter can decide the fate of parasites. It was observed that reciprocal relationship exists between IL12 and IL10 and that is majorly controlled by a transcription factor NFAT5 from Rel family of transcription factors. By targeting this transcription factor at the cellular level, it might be possible to modulate the release of powerful pro-inflammatory cytokines, thereby reducing parasite survival. The mathematical models developed here serves as a step towards finding a key component that can pave a way for therapeutic investigation.

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

Background

Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure.

Results

Here, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the previous approaches, the LogicNet does not require an a priori known network structure to infer the logics. The proposed probabilistic logic is superior to the existing fuzzy logics and is more relevant to the biological contexts than the fuzzy logics. The performance of the LogicNet is superior to that of several Mutual Information-based and regression-based tools for reconstructing GRNs.

Conclusions

The LogicNet reconstructs GRNs and logic functions without requiring prior knowledge of the network structure. Moreover, in another application, the LogicNet can be applied for logic function detection from the known regulatory genes-target interactions. We also conclude that computational modeling of the logical interactions among the regulatory genes significantly improves the GRN reconstruction accuracy.

Downregulation of the let-7 family of microRNAs may promote insulin receptor/insulin-like growth factor signalling pathways in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type characterized by dysregulated cell signalling pathways and resistance to treatment. The insulin-like growth factor (IGF) signalling pathway has been identified to have a role in tumour progression and therapy resistance. However, its regulatory roles in PDAC have remained to be fully elucidated. In the present study, dysregulated microRNAs (miRNAs) in PDAC were explored with a focus on those that may be involved in regulating the insulin/IGF signalling pathway. A total of 208 patients were recruited, comprising 112 patients with PDAC, 50 patients with chronic pancreatitis (CP) and 46 subjects as a control group (CG). miRNA-specific quantitative PCR assays were used to measure 300 candidate miRNAs. The Student's t-test was applied to compare miRNA regulation between cancer patients and controls with a false discovery rate correction using Bonferroni-type comparison procedures. The DIANA-mirPath v.3 tool and HMDD v3.0 were used to identify miRNA-mRNA interactions within specific pathways. In patients with PDAC, 42 miRNAs were significantly upregulated and 42 were downregulated compared to the CG (P<0.01). In the PDAC vs. CP analysis, 16 significantly (P<0.01) upregulated and 16 downregulated miRNAs were identified. Of note, members of the let-7 family of miRNAs were downregulated and were indicated to target several components of the insulin receptor (INSR)/IGF pathway, including receptors and binding proteins, for upregulation and thus, may enable the activation of the pathway. Downregulation of the let-7 family may help promote the INSR/IGF pathway in PDAC. It may thus be an effective target for the development of INSR/IGF pathway-specific treatment strategies.

Nuclear localization and actions of the insulin-like growth factor 1 (IGF-1) system components: Transcriptional regulation and DNA damage response

Insulin-like growth factor (IGF) system stimulates growth, proliferation, and regulates differentiation of cells in a tissue-specific manner. It is composed of two insulin-like growth factors (IGF-1 and IGF-2), six insulin-like growth factor-binding proteins (IGFBPs), and two insulin-like growth factor receptors (IGF-1R and IGF-2R). IGF actions take place mostly through the activation of the plasma membrane-bound IGF-Rs by the circulating ligands (IGFs) released from the IGFBPs that stabilize their levels in the serum. This review focuses on the IGF-1 part of the system. The IGF-1 gene, which is expressed mainly in the liver as well as in other tissues, comprises six alternatively spliced exons that code for three protein isoforms (pro-IGF-1A, pro-IGF-1B, and pro-IGF-1C), which are processed to mature IGF-1 and E-peptides. The IGF-1R undergoes autophosphorylation, resulting in a signaling cascade involving numerous cytoplasmic proteins such as AKT and MAPKs, which regulate the expression of target genes. However, a more complex picture of the axis has recently emerged with all its components being translocated to the nuclear compartment. IGF-1R takes part in the regulation of gene expression by forming transcription complexes, modifying the activity of chromatin remodeling proteins, and participating in DNA damage tolerance mechanisms. Four IGFBPs contain a nuclear localization signal (NLS), which targets them to the nucleus, where they regulate gene expression (IGFBP-2, IGFBP-3, IGFBP-5, IGFBP-6) and DNA damage repair (IGFBP-3 and IGFBP-6). Last but not least, the IGF-1B isoform has been reported to be localized in the nuclear compartment. However, no specific molecular actions have been assigned to the nuclear pro-IGF-1B or its derivative EB peptide. Therefore, further studies are needed to shed light on their nuclear activity. These recently uncovered nuclear actions of different components of the IGF-1 axis are relevant in cancer cell biology and are discussed in this review.

Elevated serum insulin-like growth factor 1 in recurrent aphthous stomatitis

Over 100 million Americans experience recurrent aphthous stomatitis (RAS) at some point in life. To develop targeted drugs for RAS treatment, it is critical to identify its etiology. We determined if serum insulin‐like growth factor 1 (IGF‐1) and related factors are associated with RAS, because both RAS prevalence and IGF‐1 are highest during puberty. We analyzed data from 1,480 Third National Health and Nutrition Examination Survey participants aged 20–40 years. Participants with a history of diabetes or lupus, cotinine levels 6 ng/ml or higher or glycemia 110 mg/dl or higher were excluded. We compared levels of IGF‐1, IGFBP‐3, leptin, and insulin in participants with a positive vs. negative RAS history in the prior 12 months. We used logistic regression in SAS/SUDAAN to account for the complex sampling design. The odds of a positive RAS history were 1.31 times higher for every 100 ng/ml increase in serum IGF‐1. This association persisted after adjustment for age, race/ethnicity, medication intake, body mass index, insulin, leptin, glycemia, and income (adjusted OR = 1.30, 95% CI [1.06, 1.60]; p = 0.013). The odds of a positive RAS history were also higher among non‐Hispanic white compared with non‐Hispanic black participants (adjusted OR = 4.37, 95% CI [3.00, 6.38]). Leptin, IGFBP‐3, and insulin levels did not differ by RAS status. The significantly higher IGF‐1 levels in participants with a positive RAS history compared with controls suggest a possible role of the IGF‐1 pathway in RAS etiology.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells

Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.

Epidermal growth factor prevents APOE4-induced cognitive and cerebrovascular deficits in female mice

Cerebrovascular dysfunction is re-emerging as a major component of aging, and may contribute to the risk of developing Alzheimer's disease (AD). Two important risk factors for cerebrovascular dysfunction are APOE and female sex, which are primarily researched in the context of high amyloid-β (Aβ) levels as found in AD. However, APOE4 and sex modulate Aβ-independent pathways that may induce cerebrovascular dysfunction as a downstream consequence. Therefore, testing the activity of factors that target cerebrovascular dysfunction in Aβ-independent models that incorporate APOE4 and female sex is crucial. We have previously demonstrated that peripheral administration of the epidermal growth factor (EGF) prevents cognitive dysfunction, cerebrovascular leakiness, and cerebrovascular coverage deficits in female mice that express APOE4 and overproduce Aβ, without affecting Aβ levels. These data raise the question of whether EGF protects the cerebrovasculature from general stress-induced damage. Therefore, the goal of this study was to determine whether EGF prevents Aβ-independent cerebrovascular dysfunction. In eight-month old mice that express human APOE, the interaction of APOE4 and female sex induced cognitive dysfunction, increased cerebrovascular leakiness and lowered vessel coverage. Importantly, in a prevention paradigm (from six to eight and a half months of age), EGF ameliorated cognitive decline and cerebrovascular deficits in female mice that express APOE4. Thus, developing treatment strategies based on EGF signaling could provide alternative therapeutic options for age-related cerebrovascular dysfunction and reduce AD risk.

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways

BACKGROUND

Signaling pathways play important roles in the life processes of cell growth, cell apoptosis and organism development. At present the signal transduction networks are far from complete. As an effective complement to experimental methods, computational modeling is suited to rapidly reconstruct the signaling pathways at low cost. To our knowledge, the existing computational methods seldom simultaneously exploit more than three signaling pathways into one predictive model for the discovery of novel signaling components and the cross-talk modeling between signaling pathways.

RESULTS

In this work, we propose a multi-label multi-instance transfer learning method to simultaneously reconstruct 27 human signaling pathways and model their cross-talks. Computational results show that the proposed method demonstrates satisfactory multi-label learning performance and rational proteome-wide predictions. Some predicted signaling components or pathway targeted proteins have been validated by recent literature. The predicted signaling components are further linked to pathways using the experimentally derived PPIs (protein-protein interactions) to reconstruct the human signaling pathways. Thus the map of the cross-talks via common signaling components and common signaling PPIs is conveniently inferred to provide valuable insights into the regulatory and cooperative relationships between signaling pathways. Lastly, gene ontology enrichment analysis is conducted to gain statistical knowledge about the reconstructed human signaling pathways.

CONCLUSIONS

Multi-label learning framework has been demonstrated effective in this work to model the phenomena that a signaling protein belongs to more than one signaling pathway. As results, novel signaling components and pathways targeted proteins are predicted to simultaneously reconstruct multiple human signaling pathways and the static map of their cross-talks for further biomedical research.

A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks

Signaling pathways play important roles in understanding the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. Protein-protein interaction (PPI) networks are commonly-used infrastructure to infer signaling pathways. However, PPI networks generally carry no information of upstream/downstream relationship between interacting proteins, which retards our inferring the signal flow of signaling pathways. In this work, we propose a simple feature construction method to train a SVM (support vector machine) classifier to predict PPI upstream/downstream relations. The domain based asymmetric feature representation naturally embodies domain-domain upstream/downstream relations, providing an unconventional avenue to predict the directionality between two objects. Moreover, we propose a semantically interpretable decision function and a macro bag-level performance metric to satisfy the need of two-instance depiction of an interacting protein pair. Experimental results show that the proposed method achieves satisfactory cross validation performance and independent test performance. Lastly, we use the trained model to predict the PPIs in HPRD, Reactome and IntAct. Some predictions have been validated against recent literature.

Xtalk: a path-based approach for identifying crosstalk between signaling pathways

MOTIVATION

Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics.

RESULTS

We present Xtalk, a path-based approach for identifying pairs of pathways that may crosstalk. Xtalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, Xtalk reports the precise interactions and mechanisms that support the identified crosstalk. We applied Xtalk to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which Xtalk achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by Xtalk that accurately recovered known mechanisms of crosstalk.

AVAILABILITY AND IMPLEMENTATION

The XTALK software is available at http://bioinformatics.cs.vt.edu/~murali/software. Crosstalk networks are available at http://graphspace.org/graphs?tags=2015-bioinformatics-xtalk.

CONTACT

ategge@vt.edu, murali@cs.vt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

Simulation predicts IGFBP2-HIF1α interaction drives glioblastoma growth

Tremendous strides have been made in improving patients’ survival from cancer with one glaring exception: brain cancer. Glioblastoma is the most common, aggressive and highly malignant type of primary brain tumor. The average overall survival remains less than 1 year. Notably, cancer patients with obesity and diabetes have worse outcomes and accelerated progression of glioblastoma. The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway. However, while the process of invasive glioblastoma progression has been extensively studied macroscopically, it has not yet been well characterized with regards to intracellular insulin signaling. In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling. Results of the model suggest a novel observation: feedback from IGFBP2 to HIF1α is integral to the sustained growth of glioblastoma. Our study suggests that downstream signaling from IGFI to HIF1α, which has been the target of many insulin signaling drugs in clinical trials, plays a smaller role in overall tumor growth. These predictions strongly suggest redirecting the focus of glioma drug candidates on controlling the feedback between IGFBP2 and HIF1α.

Current treatment for glioblastoma patients is limited to nonspecific methods: surgery followed by a combination of radio- and chemotherapy. With these methods, glioma patient survival is less than one year post-diagnosis. Targeting specific protein signaling pathways offers potentially more potent therapies. One promising potential target is the insulin signaling pathway, which is known to contribute to glioblastoma progression. However, drugs targeting this pathway have shown mixed results in clinical trials, and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated. Here, we developed a computational model of insulin signaling in glioblastoma in order to study this pathway’s role in tumor progression. Using the model, we systematically test contributions of different insulin signaling protein interactions on glioblastoma growth. Our model highlights a key driver for the growth of glioblastoma: IGFBP2-HIF1α feedback. This interaction provides a target that could open the door for new therapies in glioma and other solid tumors.

Revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data

Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens.

Genome-scale functional genomics screens are important tools for investigating the function of genes. Technological progress allows for the simultaneous measurement of multiple parameters quantifying the response of cells to gene perturbations such as RNA interference. Such multi-dimensional screens provide rich data, but there is a lack of computational methods for interpreting these complex measurements. We have developed two computational methods that combine the data from multi-dimensional functional genomics screens with protein interaction information. These methods search for phenotype patterns that are consistent among interacting genes. Thereby, we could reduce the noise in the data and facilitate the mechanistic interpretation of the findings. The performance of the methods was demonstrated through application to a genome-wide screen studying endocytosis. Subsequent experimental validation demonstrated the improved detection of phenotypic profiles through the use of protein interaction data. Our analysis revealed unexpected roles of specific network modules and protein complexes with respect to endocytosis. Detailed follow-up experiments investigating the dynamics of endocytosis uncovered crosstalk between the cancer-related EGF and IGF pathways with so far unknown effects on endocytosis and cargo trafficking.

Function of RasGRP3 in the formation and progression of human breast cancer

Introduction

Ras guanine nucleotide exchange factors (RasGEFs) mediate the activation of the Ras signaling pathway that is over activated in many human cancers. The RasGRP3, an activator of H-Ras and R-Ras protein exerts oncogenic effects and the overexpression of the protein is observed in numerous malignant cancer types. Here, we investigated the putative alteration of expression and potential function of RasGRP3 in the formation and progression of human breast cancer.

Methods

The RasGRP3 and phosphoRasGRP3 expressions were examined in human invasive ductal adenocarcinoma derived samples and cell lines (BT-474, JIMT-1, MCF7, SK-BR-3, MDA-MB-453, T-47D) both in mRNA (Q-PCR) and protein (Western blot; immunohistochemistry) levels. To explore the biological function of the protein, RasGRP3 knockdown cultures were established. To assess the role of RasGRP3 in the viability of cells, annexin-V/PI staining and MitoProbe™ DilC1 (5) assay were performed. To clarify the function of the protein in cell proliferation and in the development of chemotherapeutic resistance, CyQuant assay was performed. To observe the RasGRP3 function in tumor formation, the Severe combined immunodeficiency (SCID) mouse model was used. To investigate the role of the protein in Ras-related signaling Q-PCR and Western blot experiments were performed.

Results

RasGRP3 expression was elevated in human breast tumor tissue samples as well as in multiple human breast cancer cell lines. Down-regulation of RasGRP3 expression in breast cancer cells decreased cell proliferation, induced apoptosis in MCF7 cells, and sensitized T-47D cells to the action of drugs Tamoxifen and trastuzumab (Herceptin). Gene silencing of RasGRP3 reduced tumor formation in mouse xenografts as well. Inhibition of RasGRP3 expression also reduced Akt, ERK1/2 and estrogen receptor alpha phosphorylation downstream from IGF-I insulin like growth factor-I (IGF-I) or epidermal growth factor (EGF) stimulation confirming the functional role of RasGRP3 in the altered behavior of these cells.

Conclusions

Taken together, our results suggest that the Ras activator RasGRP3 may have a role in the pathological behavior of breast cancer cells and may constitute a therapeutic target for human breast cancer.

Mechanisms of resistance to sorafenib and the corresponding strategies in hepatocellular carcinoma

Sorafenib, the unique drug as first-line treatment for advanced hepatocellular carcinoma (HCC), has opened a window of hope after searching for effective agents to combat HCC for decades. However, the overall outcomes are far from satisfactory. One of the explanations is the genetic heterogeneity of HCC, which has led to identifying predictive biomarkers for primary resistance to sorafenib, and then applying the concept of personalized medicine, or seeking therapeutic strategies such as combining sorafenib with other anticancer agents. Some of the combinations have demonstrated a better effectiveness than sorafenib alone, with good tolerance. The acquired resistance to sorafenib has also drawn attention. As a multikinase inhibitor, sorafenib targets several cellular signaling pathways but simultaneously or sequentially the addiction switches and compensatory pathways are activated. Several mechanisms are involved in the acquired resistance to sorafenib, such as crosstalks involving PI3K/Akt and JAK-STAT pathways, hypoxia-inducible pathways, epithelial-mesenchymal transition, etc. Based on the investigated mechanisms, some other molecular targeted drugs have been applied as second-line treatment for treat HCC after the failure of sorafenib therapy and more are under evaluation in clinical trials. However, the exact mechanisms accounting for sorafenib resistance remains unclear. Further investigation on the crosstalk and relationship of associated pathways will better our understanding of the mechanisms and help to find effective strategies for overcoming sorafenib resistance in HCC.

Dynamical systems approach to endothelial heterogeneity

Endothelial cells display remarkable phenotypic heterogeneity. An important goal is to elucidate the scope and mechanisms of endothelial heterogeneity and to use this information to develop vascular bed-specific therapies. We reexamine our current understanding of the molecular basis of endothelial heterogeneity. We introduce multistability as a new explanatory framework in vascular biology. We draw on the field of nonlinear dynamics to propose a dynamical systems framework for modeling multistability and its derivative properties, including robustness, memory, and plasticity. Our perspective allows for both a conceptual and quantitative description of system-level features of endothelial regulation.

Nonparametric simulation of signal transduction networks with semi-synchronized update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.

Brassinosteroids inhibit pathogen-associated molecular pattern-triggered immune signaling independent of the receptor kinase BAK1

Plants and animals use innate immunity as a first defense against pathogens, a costly yet necessary tradeoff between growth and immunity. In Arabidopsis, the regulatory leucine-rich repeat receptor-like kinase (LRR-RLK) BAK1 combines with the LRR-RLKs FLS2 and EFR in pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and the LRR-RLK BRI1 in brassinosteroid (BR)-mediated growth. Therefore, a potential tradeoff between these pathways mediated by BAK1 is often postulated. Here, we show a unidirectional inhibition of FLS2-mediated immune signaling by BR perception. Unexpectedly, this effect occurred downstream or independently of complex formation with BAK1 and associated downstream phosphorylation. Thus, BAK1 is not rate-limiting in these pathways. BRs also inhibited signaling triggered by the BAK1-independent recognition of the fungal PAMP chitin. Our results suggest a general mechanism operative in plants in which BR-mediated growth directly antagonizes innate immune signaling.

A hierarchical whole-body modeling approach elucidates the link between in Vitro insulin signaling and in Vivo glucose homeostasis

Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

Reconstruction and crosstalk of protein-protein interaction networks of Wnt and Hedgehog signaling in Drosophila melanogaster

In the last few years, researchers have an intense interest in the evolutionarily conserved signaling pathways which have crucial roles during embryonic development. The most intriguing factor of this interest is that malfunctioning of these signaling pathways (Hedgehog, Notch, Wnt etc.) leads to several human diseases, especially to cancer. This study deals with the β-catenin dependent branch of Wnt signaling and the Hedgehog signaling pathways which offer potential targeting points for cancer drug development. The identification of all proteins functioning in these signaling networks is crucial for the efforts of preventing tumor formation. Here, through integration of protein-protein interaction data and Gene Ontology annotations, Wnt/β-catenin and Hedgehog signaling networks consisting of proteins that have statistically high probability of being biologically related to these signaling pathways were reconstructed in Drosophila melanogaster. Next, by the structural network analyses, the crucial components functioning in these pathways were identified. The proteins Arm, Frizzled receptors (Fz and Fz2), Arr, Apc, Axn, Ci and Ptc were detected as the key proteins in these networks. Futhermore, the hub protein Mer having tumor suppressor function may be proposed as a putative drug target for cancer and deserves further investigation via experimental methods. Finally, the crosstalk analysis between the reconstructed networks reveals that these two signaling networks crosstalk to each other.

Simulating quantitative cellular responses using asynchronous threshold Boolean network ensembles

BACKGROUND

With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data.

RESULTS

We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature.

CONCLUSION

A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a) using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b) with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our results suggest that this approach is both quantitative, allowing statistical verification and calibration, and extensible, allowing modification and revision as guided by experimental evidence. The simulation methodology is part of the US EPA Virtual Liver, which is investigating the effects of everyday contaminants on living tissues. Future models will incorporate additional crosstalk surrounding proliferation as well as the putative effects of xenobiotics on these signaling cascades within hepatocytes.

Computational model of EGFR and IGF1R pathways in lung cancer: a Systems Biology approach for Translational Oncology

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome.

Angiogenesis-associated crosstalk between collagens, CXC chemokines, and thrombospondin domain-containing proteins

Excessive vascularization is a hallmark of many diseases including cancer, rheumatoid arthritis, diabetic nephropathy, pathologic obesity, age-related macular degeneration, and asthma. Compounds that inhibit angiogenesis represent potential therapeutics for many diseases. Karagiannis and Popel [Proc. Natl. Acad. Sci. USA 105(37):13775-13780, 2008] used a bioinformatics approach to identify more than 100 peptides with sequence homology to known angiogenesis inhibitors. The peptides could be grouped into families by the conserved domain of the proteins they were derived from. The families included type IV collagen fibrils, CXC chemokine ligands, and type I thrombospondin domain-containing proteins. The relationships between these families have received relatively little attention. To investigate these relationships, we approached the problem by placing the families of proteins in the context of the human interactome including >120,000 physical interactions among proteins, genes, and transcripts. We built on a graph theoretic approach to identify proteins that may represent conduits of crosstalk between protein families. We validated these findings by statistical analysis and analysis of a time series gene expression data set taken during angiogenesis. We identified six proteins at the center of the angiogenesis-associated network including three syndecans, MMP9, CD44, and versican. These findings shed light on the complex signaling networks that govern angiogenesis phenomena.

Comparative genomics of the vertebrate insulin/TOR signal transduction pathway: a network-level analysis of selective pressures

Complexity of biological function relies on large networks of interacting molecules. However, the evolutionary properties of these networks are not fully understood. It has been shown that selective pressures depend on the position of genes in the network. We have previously shown that in the Drosophila insulin/target of rapamycin (TOR) signal transduction pathway there is a correlation between the pathway position and the strength of purifying selection, with the downstream genes being most constrained. In this study, we investigated the evolutionary dynamics of this well-characterized pathway in vertebrates. More specifically, we determined the impact of natural selection on the evolution of 72 genes of this pathway. We found that in vertebrates there is a similar gradient of selective constraint in the insulin/TOR pathway to that found in Drosophila. This feature is neither the result of a polarity in the impact of positive selection nor of a series of factors affecting selective constraint levels (gene expression level and breadth, codon bias, protein length, and connectivity). We also found that pathway genes encoding physically interacting proteins tend to evolve under similar selective constraints. The results indicate that the architecture of the vertebrate insulin/TOR pathway constrains the molecular evolution of its components. Therefore, the polarity detected in Drosophila is neither specific nor incidental of this genus. Hence, although the underlying biological mechanisms remain unclear, these may be similar in both vertebrates and Drosophila.

Logic-based models for the analysis of cell signaling networks

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.

Toward the dynamic interactome: it's about time

Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.

SimBoolNet--a Cytoscape plugin for dynamic simulation of signaling networks

Summary: SimBoolNet is an open source Cytoscape plugin that simulates the dynamics of signaling transduction using Boolean networks. Given a user-specified level of stimulation to signal receptors, SimBoolNet simulates the response of downstream molecules and visualizes with animation and records the dynamic changes of the network. It can be used to generate hypotheses and facilitate experimental studies about causal relations and crosstalk among cellular signaling pathways.

Availability: SimBoolNet package (with manual) is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/SimBoolNet

Contact:przytyck@ncbi.nlm.nih.gov; zhengj@ncbi.nlm.nih.gov