Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy

Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy. Here, we developed LogiRx, a logic-based mechanistic machine learning method that predicts drug-induced pathways. We applied LogiRx to discover how drugs discovered in a previous compound screen attenuate cardiomyocyte hypertrophy. We experimentally validated LogiRx predictions in neonatal cardiomyocytes, adult mice, and two patient databases. Using LogiRx, we predicted antihypertrophic pathways for seven drugs currently used to treat noncardiac disease. We experimentally validated that escitalopram (Lexapro) and mifepristone inhibit hypertrophy of cultured cardiomyocytes in two contexts. The LogiRx model predicted that escitalopram prevents hypertrophy through an "off-target" serotonin receptor/PI3Kγ pathway, mechanistically validated using additional investigational drugs. Further, escitalopram reduced cardiomyocyte hypertrophy in a mouse model of hypertrophy and fibrosis. Finally, mining of both FDA and University of Virginia databases showed that patients with depression on escitalopram have a lower incidence of cardiac hypertrophy than those prescribed other serotonin reuptake inhibitors that do not target the serotonin receptor. Mechanistic machine learning by LogiRx discovers drug pathways that perturb cell states, which may enable repurposing of escitalopram and other drugs to limit cardiac remodeling through off-target pathways.

In Vitro and In Vivo Comparative Analysis of Differentially Expressed Genes and Signaling Pathways in Breast Cancer Cells on Interaction with Mesenchymal Stem Cells

The interaction of breast cancer cells (BCC) with mesenchymal stem cells (MSC) plays a vital role in influencing the gene expression in breast cancer cells and thereby its uncontrolled proliferation, metastasis, and drug resistance. The extent of MSC governing the BCC or the extent of BCC influencing the MSC is a complex process, and the interaction strongly depends upon conditions such as the presence or absence of other cell types and in vivo tumor microenvironment or simple in vitro conditions. Hence, understanding this interaction through gene expression profiling may provide key insights about potential genes which can be targeted for breast cancer treatment. In the current study, in vitro microarray dataset and in vivo RNA-seq dataset of BCC on interaction with the MSC were downloaded from NCBI GEO database and analyzed for differentially expressed genes (DEGs), gene ontology (GO) term enrichment, and Reactome pathway analysis. To target the genes which have similar effect on both in vitro and in vivo, a comparative analysis was performed, 24 genes were commonly upregulated in both in vitro and in vivo datasets, while no common downregulated genes were observed. Out of which, 16 significant genes based upon fold change (logFC > 2) are identified for manipulating the interactions between MSC and BCC. Among them, 6 of the identified genes (FSTL1, LOX, SERPINE1, INHBA, FN1, and VEGFA) have already been reported to be upregulated in BCC on interaction with MSC by various studies. Further experiments need to be conducted to understand the role of remaining 10 identified genes (EFEMP1, IGFBP3, EDIL3, IFITM1, IGFBP4, ITGA5, SLC3A2, HRH1, PPP1R15A, and NNMT) in MSC-BCC interaction. In addition to the reported significant genes and its associated pathways, the expression of long non-coding RNA identified in this study may increase our understanding about the way MSC interacts with BCC and accelerate MSC-based treatment strategies for breast cancer.

Binary Matrix Method to Enumerate, Hierarchically Order, and Structurally Classify Peptide Aggregation

Protein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as center-of-mass to center-of-mass (COM-COM) distance, the radius of gyration (R_g), hydrogen bonding (HB), and solvent accessible surface area do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. An nxn two-dimensional AM is created by using the interpeptide C_α-C_α cutoff distances, which are binarily encoded (0 or 1). These aggregation matrices are analyzed to enumerate, hierarchically order, and structurally classify the aggregates. Comparison of the present AM method suggests that it is superior to the HB method since it can incorporate nonspecific interactions and the R_g and COM-COM methods since the cutoff distance is independent of the length of the peptide. More importantly, the present method can structurally classify the peptide aggregates, which the conventional R_g, COM-COM, and HB methods fail to do. The unique selling point of this method is its ability to structurally classify peptide aggregates using two-dimensional matrices.

Defects in LC3B2 and ATG4A underlie HSV2 meningitis and reveal a critical role for autophagy in antiviral defense in humans

Recurrent herpesvirus infections can manifest in different forms of disease, including cold sores, genital herpes, and encephalitis. There is an incomplete understanding of the genetic and immunological factors conferring susceptibility to recurrent herpes simplex virus 2 (HSV2) infection in the central nervous system (CNS). Here, we describe two adult patients with recurrent HSV2 lymphocytic Mollaret's meningitis that each carry a rare monoallelic variant in the autophagy proteins ATG4A or LC3B2. HSV2-activated autophagy was abrogated in patient primary fibroblasts, which also exhibited significantly increased viral replication and enhanced cell death. HSV2 antigen was captured in autophagosomes of infected cells, and genetic inhibition of autophagy by disruption of autophagy genes, including ATG4A and LC3B2, led to enhanced viral replication and cell death in primary fibroblasts and a neuroblastoma cell line. Activation of autophagy by HSV2 was sensitive to ultraviolet (UV) irradiation of the virus and inhibited in the presence of acyclovir, but HSV2-induced autophagy was independent of the DNA-activated STING pathway. Reconstitution of wild-type ATG4A and LC3B2 expression using lentiviral gene delivery or electroporation of in vitro transcribed mRNA into patient cells restored virus-induced autophagy and the ability to control HSV2 replication. This study describes a previously unknown link between defective autophagy and an inborn error of immunity that can lead to increased susceptibility to HSV2 infection, suggesting an important role for autophagy in antiviral immunity in the CNS.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

Cell cycle and protein complex dynamics in discovering signaling pathways

Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

Intrinsic limits of information transmission in biochemical signalling motifs

All living things have evolved to sense changes in their environment in order to respond in adaptive ways. At the cellular level, these sensing systems generally involve receptor molecules at the cell surface, which detect changes outside the cell and relay those changes to the appropriate response elements downstream. With the advent of experimental technologies that can track signalling at the single-cell level, it has become clear that many signalling systems exhibit significant levels of 'noise,' manifesting as differential responses of otherwise identical cells to the same environment. This noise has a large impact on the capacity of cell signalling networks to transmit information from the environment. Application of information theory to experimental data has found that all systems studied to date encode less than 2.5 bits of information, with the majority transmitting significantly less than 1 bit. Given the growing interest in applying information theory to biological data, it is crucial to understand whether the low values observed to date represent some sort of intrinsic limit on information flow given the inherently stochastic nature of biochemical signalling events. In this work, we used a series of computational models to explore how much information a variety of common 'signalling motifs' can encode. We found that the majority of these motifs, which serve as the basic building blocks of cell signalling networks, can encode far more information (4-6 bits) than has ever been observed experimentally. In addition to providing a consistent framework for estimating information-theoretic quantities from experimental data, our findings suggest that the low levels of information flow observed so far in living system are not necessarily due to intrinsic limitations. Further experimental work will be needed to understand whether certain cell signalling systems actually can approach the intrinsic limits described here, and to understand the sources and purpose of the variation that reduces information flow in living cells.

Topological alternate centrality measure capturing drug targets in the network of MAPK pathways

A new centrality of the nodes in the network is proposed called alternate centrality, which can isolate effective drug targets in the complex signalling network. Alternate centrality metric defined over the network substructure (four nodes - motifs). The nodes involving in alternative activation in the motifs gain in metric values. Targeting high alternative centrality nodes hypothesised to be destructive free to the network due to their alternative activation mechanism. Overlapping and crosstalk among the gene products in the conserved network of MAPK pathways selected for the study. In silico knock-out of high alternate centrality nodes causing rewiring in the network is investigated using MCF-7 breast cancer cell line-based data. Degree of top alternate centrality nodes lies between the degree of bridging and pagerank nodes. Node deletion of high alternate centrality on the centralities such as eccentricity, closeness, betweenness, stress, centroid and radiality causes low perturbation. The authors identified the following alternate centrality nodes ERK1, ERK2, MEKK2, MKK5, MKK4, MLK3, MLK2, MLK1, MEKK4, MEKK1, TAK1, P38alpha, ZAK, DLK, LZK, MLTKa/b and P38beta as efficient drug targets for breast cancer. Alternate centrality identifies effective drug targets and is free from intertwined biological processes and lethality.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling

T-cell prolymphocytic leukemia (T-PLL) is a rare and aggressive neoplasm of mature T-cells with an urgent need for rationally designed therapies to address its notoriously chemo-refractory behavior. The median survival of T-PLL patients is <2 years and clinical trials are difficult to execute. Here we systematically explored the diversity of drug responses in T-PLL patient samples using an ex vivo drug sensitivity and resistance testing platform and correlated the findings with somatic mutations and gene expression profiles. Intriguingly, all T-PLL samples were sensitive to the cyclin-dependent kinase inhibitor SNS-032, which overcame stromal-cell-mediated protection and elicited robust p53-activation and apoptosis. Across all patients, the most effective classes of compounds were histone deacetylase, phosphoinositide-3 kinase/AKT/mammalian target of rapamycin, heat-shock protein 90 and BH3-family protein inhibitors as well as p53 activators, indicating previously unexplored, novel targeted approaches for treating T-PLL. Although Janus-activated kinase-signal transducer and activator of transcription factor (JAK-STAT) pathway mutations were common in T-PLL (71% of patients), JAK-STAT inhibitor responses were not directly linked to those or other T-PLL-specific lesions. Overall, we found that genetic markers do not readily translate into novel effective therapeutic vulnerabilities. In conclusion, novel classes of compounds with high efficacy in T-PLL were discovered with the comprehensive ex vivo drug screening platform warranting further studies of synergisms and clinical testing.

Crosstalk and the evolvability of intracellular communication

Metazoan signalling networks are complex, with extensive crosstalk between pathways. It is unclear what pressures drove the evolution of this architecture. We explore the hypothesis that crosstalk allows different cell types, each expressing a specific subset of signalling proteins, to activate different outputs when faced with the same inputs, responding differently to the same environment. We find that the pressure to generate diversity leads to the evolution of networks with extensive crosstalk. Using available data, we find that human tissues exhibit higher levels of diversity between cell types than networks with random expression patterns or networks with no crosstalk. We also find that crosstalk and differential expression can influence drug activity: no protein has the same impact on two tissues when inhibited. In addition to providing a possible explanation for the evolution of crosstalk, our work indicates that consideration of cellular context will likely be crucial for targeting signalling networks.

The evolutionary rationale behind the extensive crosstalk between Metazoan signalling pathways remains elusive. Here the authors provide evidence that crosstalk in the human signalling network evolves as a means to allow efficient diversification of cellular responses to the same signals between different cell types.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Primary Cilia and Coordination of Receptor Tyrosine Kinase (RTK) and Transforming Growth Factor β (TGF-β) Signaling

Since the beginning of the millennium, research in primary cilia has revolutionized our way of understanding how cells integrate and organize diverse signaling pathways during vertebrate development and in tissue homeostasis. Primary cilia are unique sensory organelles that detect changes in their extracellular environment and integrate and transmit signaling information to the cell to regulate various cellular, developmental, and physiological processes. Many different signaling pathways have now been shown to rely on primary cilia to function properly, and mutations that lead to ciliary dysfunction are at the root of a pleiotropic group of diseases and syndromic disorders called ciliopathies. In this review, we present an overview of primary cilia-mediated regulation of receptor tyrosine kinase (RTK) and transforming growth factor β (TGF-β) signaling. Further, we discuss how defects in the coordination of these pathways may be linked to ciliopathies.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway

Various genetic mutations associated with cancer are known to alter cell signaling, but it is not clear whether they dysregulate signaling pathways by altering the abundance of pathway proteins. Using a combination of RNA sequencing and ultrasensitive targeted proteomics, we defined the primary components-16 core proteins and 10 feedback regulators-of the epidermal growth factor receptor (EGFR)-mitogen-activated protein kinase (MAPK) pathway in normal human mammary epithelial cells and then quantified their absolute abundance across a panel of normal and breast cancer cell lines as well as fibroblasts. We found that core pathway proteins were present at very similar concentrations across all cell types, with a variance similar to that of proteins previously shown to display conserved abundances across species. In contrast, EGFR and transcriptionally controlled feedback regulators were present at highly variable concentrations. The absolute abundance of most core proteins was between 50,000 and 70,000 copies per cell, but the adaptors SOS1, SOS2, and GAB1 were found at far lower amounts (2000 to 5000 copies per cell). MAPK signaling showed saturation in all cells between 3000 and 10,000 occupied EGFRs, consistent with the idea that adaptors limit signaling. Our results suggest that the relative stoichiometry of core MAPK pathway proteins is very similar across different cell types, with cell-specific differences mostly restricted to variable amounts of feedback regulators and receptors. The low abundance of adaptors relative to EGFR could be responsible for previous observations that only a fraction of total cell surface EGFR is capable of rapid endocytosis, high-affinity binding, and mitogenic signaling.

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury.

A Six-Stage Workflow for Robust Application of Systems Pharmacology

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

Correlative Gene Expression to Protective Seroconversion in Rift Valley Fever Vaccinates

Rift Valley fever Virus (RVFV), a negative-stranded RNA virus, is the etiological agent of the vector-borne zoonotic disease, Rift Valley fever (RVF). In both humans and livestock, protective immunity can be achieved through vaccination. Earlier and more recent vaccine trials in cattle and sheep demonstrated a strong neutralizing antibody and total IgG response induced by the RVF vaccine, authentic recombinant MP-12 (arMP-12). From previous work, protective immunity in sheep and cattle vaccinates normally occurs from 7 to 21 days after inoculation with arMP-12. While the serology and protective response induced by arMP-12 has been studied, little attention has been paid to the underlying molecular and genetic events occurring prior to the serologic immune response. To address this, we isolated RNA from whole blood of vaccinated calves over a time course of 21 days before and after vaccination with arMP-12. The time course RNAs were sequenced by RNASeq and bioinformatically analyzed. Our results revealed time-dependent activation or repression of numerous gene ontologies and pathways related to the vaccine induced immune response and its regulation. Additional bioinformatic analyses identified a correlative relationship between specific host immune response genes and protective immunity prior to the detection of protective serum neutralizing antibody responses. These results contribute an important proof of concept for identifying molecular and genetic components underlying the immune response to RVF vaccination and protection prior to serologic detection.

ChainRank, a chain prioritisation method for contextualisation of biological networks

BACKGROUND

Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).

RESULTS

Our method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation.

CONCLUSIONS

ChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank.

A proteomics approach to identifying key protein targets involved in VEGF inhibitor mediated attenuation of bleomycin-induced pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a life expectancy of less than 5 years post diagnosis for most patients. Poor molecular characterization of IPF has led to insufficient understanding of the pathogenesis of the disease, resulting in lack of effective therapies. In this study, we have integrated a label-free LC-MS based approach with systems biology to identify signaling pathways and regulatory nodes within protein interaction networks that govern phenotypic changes that may lead to IPF. Ingenuity Pathway Analysis of proteins modulated in response to bleomycin treatment identified PI3K/Akt and Wnt signaling as the most significant profibrotic pathways. Similar analysis of proteins modulated in response to vascular endothelial growth factor (VEGF) inhibitor (CBO-P11) treatment identified natural killer cell signaling and PTEN signaling as the most significant antifibrotic pathways. Mechanistic/mammalian target of rapamycin (mTOR) and extracellular signal-regulated kinase (ERK) were identified to be key mediators of pro- and antifibrotic response, where bleomycin (BLM) treatment resulted in increased expression and VEGF inhibitor treatment attenuated expression of mTOR and ERK. Using a BLM mouse model of pulmonary fibrosis and VEGF inhibitor CBO-P11 as a therapeutic measure, we identified a comprehensive set of signaling pathways and proteins that contribute to the pathogenesis of pulmonary fibrosis that can be targeted for therapy against this fatal disease.

In silico model-based inference: an emerging approach for inverse problems in engineering better medicines

Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.

Modeling Signaling Networks to Advance New Cancer Therapies

Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

PathPPI: an integrated dataset of human pathways and protein-protein interactions

Integration of pathway and protein-protein interaction (PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset (named PathPPI). More detailed interaction type and modification information on protein interactions can be preserved in PathPPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values (O AB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The PathPPI data was provided at http://proteomeview.hupo.org.cn/PathPPI/PathPPI.html.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

BACKGROUND

Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers.

RESULTS

We found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable.

CONCLUSIONS

Next-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add to traditional approaches? Our findings indicate there is value in the way in which different subspaces of the patient sample are captured differently among the various methods, highlighting the possibility of 'combination' classifiers capable of identifying which patients will be more accurately classified by one particular method over another. We have seen this clearly for the first time because of our in-depth analysis at the level of individual patients.

A crowd-sourcing approach for the construction of species-specific cell signaling networks

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.

Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict.

Contact:ebilal@us.ibm.com or gustavo@us.ibm.com.

Supplementary information:Supplementary data are available at Bioinformatics online.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Signaling hypergraphs

Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight a representation of signaling pathways that is underutilized: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.

An Interaction Library for the FcεRI Signaling Network

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP₃ at the plasma membrane and the soluble second messenger IP₃. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

An integrative modeling framework reveals plasticity of TGF-β signaling

Background

The TGF-β transforming growth factor is the most pleiotropic cytokine controlling a broad range of cellular responses that include proliferation, differentiation and apoptosis. The context-dependent multifunctional nature of TGF-β is associated with complex signaling pathways. Differential models describe the dynamics of the TGF-β canonical pathway, but modeling the non-canonical networks constitutes a major challenge. Here, we propose a qualitative approach to explore all TGF-β-dependent signaling pathways.

Results

Using a new formalism, CADBIOM, which is based on guarded transitions and includes temporal parameters, we have built the first discrete model of TGF-β signaling networks by automatically integrating the 137 human signaling maps from the Pathway Interaction Database into a single unified dynamic model. Temporal property-checking analyses of 15934 trajectories that regulate 145 TGF-β target genes reveal the association of specific pathways with distinct biological processes. We identify 31 different combinations of TGF-β with other extracellular stimuli involved in non-canonical TGF-β pathways that regulate specific gene networks. Extensive analysis of gene expression data further demonstrates that genes sharing CADBIOM trajectories tend to be co-regulated.

Conclusions

As applied here to TGF-β signaling, CADBIOM allows, for the first time, a full integration of highly complex signaling pathways into dynamic models that permit to explore cell responses to complex microenvironment stimuli.

Logical modelling of Drosophila signalling pathways

A limited number of signalling pathways are involved in the specification of cell fate during the development of all animals. Several of these pathways were originally identified in Drosophila. To clarify their roles, and possible cross-talk, we have built a logical model for the nine key signalling pathways recurrently used in metazoan development. In each case, we considered the associated ligands, receptors, signal transducers, modulators, and transcription factors reported in the literature. Implemented using the logical modelling software GINsim, the resulting models qualitatively recapitulate the main characteristics of each pathway, in wild type as well as in various mutant situations (e.g. loss-of-function or gain-of-function). These models constitute pluggable modules that can be used to assemble comprehensive models of complex developmental processes. Moreover, these models of Drosophila pathways could serve as scaffolds for more complicated models of orthologous mammalian pathways. Comprehensive model annotations and GINsim files are provided for each of the nine considered pathways.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Systems pharmacology modeling: an approach to improving drug safety

Advances in systems biology in conjunction with the expansion in knowledge of drug effects and diseases present an unprecedented opportunity to extend traditional pharmacokinetic and pharmacodynamic modeling/analysis to conduct systems pharmacology modeling. Many drugs that cause liver injury and myopathies have been studied extensively. Mitochondrion-centric systems pharmacology modeling is important since drug toxicity across a large number of pharmacological classes converges to mitochondrial injury and death. Approaches to systems pharmacology modeling of drug effects need to consider drug exposure, organelle and cellular phenotypes across all key cell types of human organs, organ-specific clinical biomarkers/phenotypes, gene-drug interaction and immune responses. Systems modeling approaches, that leverage the knowledge base constructed from curating a selected list of drugs across a wide range of pharmacological classes, will provide a critically needed blueprint for making informed decisions to reduce the rate of attrition for drugs in development and increase the number of drugs with an acceptable benefit/risk ratio.

Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

VAN: an R package for identifying biologically perturbed networks via differential variability analysis

BACKGROUND

Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for network-level analyses. Consequently, bioinformatics approaches that enable a joint analysis of high-throughput transcriptomics datasets and large-scale molecular interaction networks for identifying perturbed networks are gaining popularity. Typically, these approaches require the sequential application of multiple bioinformatics techniques - ID mapping, network analysis, and network visualization. Here, we present the Variability Analysis in Networks (VAN) software package: a collection of R functions to streamline this bioinformatics analysis.

FINDINGS

VAN determines whether there are network-level perturbations across biological states of interest. It first identifies hubs (densely connected proteins/microRNAs) in a network and then uses them to extract network modules (comprising of a hub and all its interaction partners). The function identifySignificantHubs identifies dysregulated modules (i.e. modules with changes in expression correlation between a hub and its interaction partners) using a single expression and network dataset. The function summarizeHubData identifies dysregulated modules based on a meta-analysis of multiple expression and/or network datasets. VAN also converts protein identifiers present in a MITAB-formatted interaction network to gene identifiers (UniProt identifier to Entrez identifier or gene symbol using the function generatePpiMap) and generates microRNA-gene interaction networks using TargetScan and Microcosm databases (generateMicroRnaMap). The function obtainCancerInfo is used to identify hubs (corresponding to significantly perturbed modules) that are already causally associated with cancer(s) in the Cancer Gene Census database. Additionally, VAN supports the visualization of changes to network modules in R and Cytoscape (visualizeNetwork and obtainPairSubset, respectively). We demonstrate the utility of VAN using a gene expression data from metastatic melanoma and a protein-protein interaction network from the Human Protein Reference Database.

CONCLUSIONS

Our package provides a comprehensive and user-friendly platform for the integrative analysis of -omics data to identify disease-associated network modules. This bioinformatics approach, which is essentially focused on the question of explaining phenotype with a 'network type' and in particular, how regulation is changing among different states of interest, is relevant to many questions including those related to network perturbations across developmental timelines.

Construction of the influenza A virus infection-induced cell-specific inflammatory regulatory network based on mutual information and optimization

Background

Influenza A virus (IAV) infection-induced inflammatory regulatory networks (IRNs) are extremely complex and dynamic. Specific biological experiments for investigating the interactions between individual inflammatory factors cannot provide a detailed and insightful multidimensional view of IRNs. Recently, data from high-throughput technologies have permitted system-level analyses. The construction of large and cell-specific IRNs from high-throughput data is essential to understanding the pathogenesis of IAV infection.

Results

In this study, we proposed a computational method, which combines nonlinear ordinary differential equation (ODE)-based optimization with mutual information, to construct a cell-specific optimized IRN during IAV infection by integrating gene expression data with a prior knowledge of network topology. Moreover, we used the average relative error and sensitivity analysis to evaluate the effectiveness of our proposed approach. Furthermore, from the optimized IRN, we confirmed 45 interactions between proteins in biological experiments and identified 37 new regulatory interactions and 8 false positive interactions, including the following interactions: IL1β regulates TLR3, TLR3 regulates IFN-β and TNF regulates IL6. Most of these regulatory interactions are statistically significant by Z-statistic. The functional annotations of the optimized IRN demonstrated clearly that the defense response, immune response, response to wounding and regulation of cytokine production are the pivotal processes of IAV-induced inflammatory response. The pathway analysis results from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) showed that 8 pathways are enriched significantly. The 5 pathways were validated by experiments, and 3 other pathways, including the intestinal immune network for IgA production, the cytosolic DNA-sensing pathway and the allograft rejection pathway, are the predicted novel pathways involved in the inflammatory response.

Conclusions

Integration of knowledge-driven and data-driven methods allows us to construct an effective IRN during IAV infection. Based on the constructed network, we have identified new interactions among inflammatory factors and biological pathways. These findings provide new insight into our understanding of the molecular mechanisms in the inflammatory network in response to IAV infection. Further characterization and experimental validation of the interaction mechanisms identified from this study may lead to a novel therapeutic strategy for the control of infections and inflammatory responses.

Using biological pathway data with paxtools

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.

Importance of different types of prior knowledge in selecting genome-wide findings for follow-up

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts' opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology

If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.

Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors

Crosstalk and compensatory circuits within cancer signaling networks limit the activity of most targeted therapies. For example, altered signaling in the networks activated by the ErbB family of receptors, particularly in ERBB2-amplified cancers, contributes to drug resistance. We developed a multiscale systems model of signaling networks in ERBB2-amplified breast cancer to quantitatively investigate relationships between biomarkers (markers of network activity) and combination drug efficacy. This model linked ErbB receptor family signaling to breast tumor growth through two kinase cascades: the PI3K/AKT survival pathway and the Ras/MEK/ERK growth and proliferation pathway. The model predicted molecular mechanisms of resistance to individual therapeutics. In particular, ERBB2-amplified breast cancer cells stimulated with the ErbB3 ligand heregulin were resistant to growth arrest induced by inhibitors of AKT and MEK or coapplication of two inhibitors of the receptor ErbB2 [Herceptin (trastuzumab) and Tykerb (lapatinib)]. We used model simulations to predict the response of ErbB2-positive breast cancer xenografts to combination therapies and verified these predictions in mice. Treatment with trastuzumab, lapatinib, and the ErbB3 inhibitor MM-111 was more effective in inhibiting tumor growth than the combination of AKT and MEK inhibitors and even induced tumor regression, indicating that targeting both ErbB3 and ErbB2 may be an improved therapeutic approach for ErbB2-positive breast cancer patients.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Strategic applications of gene expression: from drug discovery/development to bedside

Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.