A comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells. PLoS One. 2013;8:e61757. [PMID: 23637902 PMCID: PMC3630219 DOI: 10.1371/journal.pone.0061757]

A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data are sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy, and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms.

A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.

Network Analysis Identifies Regulators of Basal-Like Breast Cancer Reprogramming and Endocrine Therapy Vulnerability

Basal-like breast cancer is the most aggressive breast cancer subtype with the worst prognosis. Despite its high recurrence rate, chemotherapy is the only treatment for basal-like breast cancer, which lacks expression of hormone receptors. In contrast, luminal A tumors express ERα and can undergo endocrine therapy for treatment. Previous studies have tried to develop effective treatments for basal-like patients using various therapeutics but failed due to the complex and dynamic nature of the disease. In this study, we performed a transcriptomic analysis of patients with breast cancer to construct a simplified but essential molecular regulatory network model. Network control analysis identified potential targets and elucidated the underlying mechanisms of reprogramming basal-like cancer cells into luminal A cells. Inhibition of BCL11A and HDAC1/2 effectively drove basal-like cells to transition to luminal A cells and increased ERα expression, leading to increased tamoxifen sensitivity. High expression of BCL11A and HDAC1/2 correlated with poor prognosis in patients with breast cancer. These findings identify mechanisms regulating breast cancer phenotypes and suggest the potential to reprogram basal-like breast cancer cells to enhance their targetability. SIGNIFICANCE: A network model enables investigation of mechanisms regulating the basal-to-luminal transition in breast cancer, identifying BCL11A and HDAC1/2 as optimal targets that can induce basal-like breast cancer reprogramming and endocrine therapy sensitivity.

Molecular mechanisms of mammary gland remodeling: A review of the homeostatic versus bisphenol a disrupted microenvironment

Mammary gland (MG) undergoes critical points of structural changes throughout a woman's life. During the perinatal and pubertal stages, MG develops through growth and differentiation to establish a pre-mature feature. If pregnancy and lactation occur, the epithelial compartment branches and differentiates to create a specialized structure for milk secretion and nurturing of the newborn. However, the ultimate MG modification consists of a regression process aiming to reestablish the smaller and less energy demanding structure until another production cycle happens. The unraveling of these fascinating physiologic cycles has helped the scientific community elucidate aspects of molecular regulation of proliferative and apoptotic events and remodeling of the stromal compartment. However, greater understanding of the hormonal pathways involved in MG developmental stages led to concern that endocrine disruptors such as bisphenol A (BPA), may influence these specific development/involution stages, called "windows of susceptibility". Since it is used in the manufacture of polycarbonate plastics and epoxy resins, BPA is a ubiquitous chemical present in human everyday life, exerting an estrogenic effect. Thus, descriptions of its deleterious effects on the MG, especially in terms of serum hormone concentrations, hormonal receptor expression, molecular pathways, and epigenetic alterations, have been widely published. Therefore, allied to a didactic description of the main physiological mechanisms involved in different critical points of MG development, the current review provides a summary of key mechanisms by which the endocrine disruptor BPA impacts MG homeostasis at different windows of susceptibility, causing short- and long-term effects.

Partial Response to Pyrotinib Plus Capecitabine in an Advanced Breast Cancer Patient with HER2 Amplification and R157W Mutation After Anti-HER2 Treatment: A Case Report and Literature Review

Human epidermal growth factor receptor2 (HER2) overexpression/amplification is associated with high malignancy, rapid disease progression and poor overall survival in breast cancer. The application of anti-HER2 drugs has greatly improved the survival of patients with HER2-positive breast cancer, but drug resistance issues affect the long-term efficacy. The HER2 mutation is considered to be one of the reasons for resistance to anti-HER2 therapy, and there is currently no standard treatment. We report for the first time the detection of HER2 amplification with R157W mutation by second-generation sequencing (NGS) in a 57-year-old hormone receptor-negative, HER2-positive woman with advanced breast cancer who was resistant to multi-line anti-HER2 therapies. She subsequently received pyrotinib combined with capecitabine treatment and achieved partial response. The small-molecule pan-HER family irreversible inhibitor pyrotinib combined with capecitabine has shown a promising effect in the treatment of HER2 mutation-induced resistance, but the molecular mechanism and efficacy need to be further verified.

A Near-Optimal Control Method for Stochastic Boolean Networks

One of the ultimate goals in systems biology is to develop control strategies to find efficient medical treatments. One step towards this goal is to develop methods for changing the state of a cell into a desirable state. We propose an efficient method that determines combinations of network perturbations to direct the system towards a predefined state. The method requires a set of control actions such as the silencing of a gene or the disruption of the interaction between two genes. An optimal control policy defined as the best intervention at each state of the system can be obtained using existing methods. However, these algorithms are computationally prohibitive for models with tens of nodes. Our method generates control actions that approximates the optimal control policy with high probability with a computational efficiency that does not depend on the size of the state space. Our C++ code is available at https://github.com/boaguilar/SDDScontrol.

Attractor - a new turning point in drug discovery

Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.

Reaction Systems and Synchronous Digital Circuits

A reaction system is a modeling framework for investigating the functioning of the living cell, focused on capturing cause-effect relationships in biochemical environments. Biochemical processes in this framework are seen to interact with each other by producing the ingredients enabling and/or inhibiting other reactions. They can also be influenced by the environment seen as a systematic driver of the processes through the ingredients brought into the cellular environment. In this paper, the first attempt is made to implement reaction systems in the hardware. We first show a tight relation between reaction systems and synchronous digital circuits, generally used for digital electronics design. We describe the algorithms allowing us to translate one model to the other one, while keeping the same behavior and similar size. We also develop a compiler translating a reaction systems description into hardware circuit description using field-programming gate arrays (FPGA) technology, leading to high performance, hardware-based simulations of reaction systems. This work also opens a novel interesting perspective of analyzing the behavior of biological systems using established industrial tools from electronic circuits design.

Boolean model of growth signaling, cell cycle and apoptosis predicts the molecular mechanism of aberrant cell cycle progression driven by hyperactive PI3K

The PI3K/AKT signaling pathway plays a role in most cellular functions linked to cancer progression, including cell growth, proliferation, cell survival, tissue invasion and angiogenesis. It is generally recognized that hyperactive PI3K/AKT1 are oncogenic due to their boost to cell survival, cell cycle entry and growth-promoting metabolism. That said, the dynamics of PI3K and AKT1 during cell cycle progression are highly nonlinear. In addition to negative feedback that curtails their activity, protein expression of PI3K subunits has been shown to oscillate in dividing cells. The low-PI3K/low-AKT1 phase of these oscillations is required for cytokinesis, indicating that oncogenic PI3K may directly contribute to genome duplication. To explore this, we construct a Boolean model of growth factor signaling that can reproduce PI3K oscillations and link them to cell cycle progression and apoptosis. The resulting modular model reproduces hyperactive PI3K-driven cytokinesis failure and genome duplication and predicts the molecular drivers responsible for these failures by linking hyperactive PI3K to mis-regulation of Polo-like kinase 1 (Plk1) expression late in G2. To do this, our model captures the role of Plk1 in cell cycle progression and accurately reproduces multiple effects of its loss: G2 arrest, mitotic catastrophe, chromosome mis-segregation / aneuploidy due to premature anaphase, and cytokinesis failure leading to genome duplication, depending on the timing of Plk1 inhibition along the cell cycle. Finally, we offer testable predictions on the molecular drivers of PI3K oscillations, the timing of these oscillations with respect to division, and the role of altered Plk1 and FoxO activity in genome-level defects caused by hyperactive PI3K. Our model is an important starting point for the predictive modeling of cell fate decisions that include AKT1-driven senescence, as well as the non-intuitive effects of drugs that interfere with mitosis.

BioLQM: A Java Toolkit for the Manipulation and Conversion of Logical Qualitative Models of Biological Networks

Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks. BioLQM provides core modeling operations as building blocks for the development of integrated modeling software, or for the assembly of heterogeneous analysis workflows involving several complementary tools. Based on the definition of multi-valued logical models, bioLQM implements import and export facilities, notably for the recent SBML qual exchange format, as well as for formats used by several popular tools, facilitating the design of workflows combining these tools. Model modifications enable the definition of various perturbations, as well as model reduction, easing the analysis of large models. Another modification enables the study of multi-valued models with tools limited to the Boolean case. Finally, bioLQM provides a framework for the development of novel analysis tools. The current version implements various updating modes for model simulation (notably synchronous, asynchronous, and random asynchronous), as well as some static analysis features for the identification of attractors. The bioLQM software can be integrated into analysis workflows through command line and scripting interfaces. As a Java library, it further provides core data structures to the GINsim and EpiLog interactive tools, which supply graphical interfaces and additional analysis methods for cellular and multi-cellular qualitative models.

ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.

Boolean network modeling in systems pharmacology

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

HDAC inhibitors enhance neratinib activity and when combined enhance the actions of an anti-PD-1 immunomodulatory antibody in vivo

Patients whose NSCLC tumors become afatinib resistant presently have few effective therapeutic options to extend their survival. Afatinib resistant NSCLC cells were sensitive to clinically relevant concentrations of the irreversible pan-HER inhibitor neratinib, but not by the first generation ERBB1/2/4 inhibitor lapatinib. In multiple afatinib resistant NSCLC clones, HDAC inhibitors reduced the expression of ERBB1/3/4, but activated c-SRC, which resulted in higher total levels of ERBB1/3 phosphorylation. Neratinib also rapidly reduced the expression of ERBB1/2/3/4, c-MET and of mutant K-/N-RAS; K-RAS co-localized with phosphorylated ATG13 and with cathepsin B in vesicles. Combined exposure of cells to [neratinib + HDAC inhibitors] caused inactivation of mTORC1 and mTORC2, enhanced autophagosome and subsequently autolysosome formation, and caused an additive to greater than additive induction of cell death. Knock down of Beclin1 or ATG5 prevented HDAC inhibitors or neratinib from reducing ERBB1/3/4 and K-/N-RAS expression and reduced [neratinib + HDAC inhibitor] lethality. Neratinib and HDAC inhibitors reduced the expression of multiple HDAC proteins via autophagy that was causal in the reduced expression of PD-L1, PD-L2 and ornithine decarboxylase, and increased expression of Class I MHCA. In vivo, neratinib and HDAC inhibitors interacted to suppress the growth of 4T1 mammary tumors, an effect that was enhanced by an anti-PD-1 antibody. Our data support the premises that neratinib lethality can be enhanced by HDAC inhibitors, that neratinib may be a useful therapeutic tool in afatinib resistant NSCLC, and that [neratinib + HDAC inhibitor] exposure facilitates anti-tumor immune responses.

Molecular Signaling of Progesterone, Growth Hormone, Wnt, and HER in Mammary Glands of Dogs, Rodents, and Humans: New Treatment Target Identification

Mammary tumors are the most common form of neoplasia in the bitch. Female dogs are protected when they are spayed before the first estrus cycle, but this effect readily disappears and is already absent when dogs are spayed after the second heat. As the ovaries are removed during spaying, ovarian steroids are assumed to play an essential role in tumor development. The sensitivity toward tumor development is already present during early life, which may be caused by early mutations in stem cells during the first estrus cycles. Later on in life, tumors arise that are mostly steroid-receptor positive, although a small subset of tumors overexpressing human epidermal growth factor 2 (HER2) and some lacking estrogen receptor, progesterone receptor (PR), and HER2 (triple negative) are present, as is the situation in humans. Progesterone (P₄), acting through PR, is the major steroid involved in outgrowth of mammary tissue. PRs are expressed in two forms, the progesterone receptor A (PRA) and progesterone receptor B (PRB) isoforms derived from splice variants from a single gene. The dog and the whole family of canids have only a functional PRA isoform, whereas the PRB isoform, if expressed at all, is devoid of intrinsic biological activity. In human breast cancer, overexpression of the PRA isoform is related to more aggressive carcinomas making the dog a unique model to study PRA-related mammary cancer. Administration of P₄ to adult dogs results in local mammary expression of growth hormone (GH) and wing less-type mouse mammary tumor virus integration site family 4 (Wnt4). Both proteins play a role in activation of mammary stem cells. In this review, we summarize what is known on P₄, GH, and Wnt signaling in canine mammary cancer, how the family of HER receptors could interact with this signaling, and what this means for comparative and translational oncological aspects of human breast cancer development.

Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms

Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity stems from the updating schedule. Standard updating schedules include the synchronous update, where all the nodes are updated at the same time, and the asynchronous update where a random node is updated at each time step. The former produces a deterministic dynamics while the latter a stochastic dynamics. A more general stochastic setting considers propensity parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS) are a modeling framework that considers two propensity parameters for updating each node and uses one when the update has a positive impact on the variable, that is, when the update causes the variable to increase its value, and uses the other when the update has a negative impact, that is, when the update causes it to decrease its value. This framework offers additional features for simulations but also adds a complexity in parameter estimation of the propensities. This paper presents a method for estimating the propensity parameters for SDDS. The method is based on adding noise to the system using the Google PageRank approach to make the system ergodic and thus guaranteeing the existence of a stationary distribution. Then with the use of a genetic algorithm, the propensity parameters are estimated. Approximation techniques that make the search algorithms efficient are also presented and Matlab/Octave code to test the algorithms are available at http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html.

Logical Modeling and Dynamical Analysis of Cellular Networks

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

An anti-HER2 antibody conjugated with monomethyl auristatin E is highly effective in HER2-positive human gastric cancer

Antibody-drug conjugate (ADC) is a novel class of therapeutics for cancer target therapy. This study assessed antitumor activity of ADC with an antimitotic agent, monomethyl auristatin E (MMAE) and a humanized monoclonal anti-HER2 antibody, hertuzumab, in gastric cancer. The efficacy of hertuzumab-MC-Val-Cit-PAB-MMAE (hertuzumab-vcMMAE) on human epidermal growth factor receptor 2 (HER2) positive human gastric cancer cells, NCI-N87, was evaluated in vitro and in vivo. The cytotoxicity of hertuzumab was significantly enhanced after conjugation with MMAE. Compared to trastuzumab, hertuzumab had a higher affinity to HER2 and had more potent antibody-dependent cell-mediated cytotoxicity (ADCC) activity in vitro. After conjugation with MMAE, the binding specificity for HER2 was not affected. Furthermore, the internalization of hertuzumab-vcMMAE in HER2 positive gastric cancer cells was verified. Although the conjugation of hertuzumab and MMAE decreased the ADCC effect, the overall cytotoxicity was dramatically increased in HER2 positive gastric cancer cells. In vitro data on this hertuzumab-vcMMAE has exerted much stronger antitumor activity compared to trastuzumab-DM1 in HER2 positive gastric cancer cells. A single administration of hertuzumab-vcMMAE at 5 or 10 mg/kg showed high potency and a sustained tumor inhibitory effect on NCI-N87 xenografts in mice. In conclusion, hertuzumab-vcMMAE conjugate is a highly effective anti-HER2 targeted therapy for HER2-positive gastric cancer.

Molecular network control through boolean canalization

Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a p53-mdm2 model to identify potential control targets.

Development of nanotoxicology: implications for drug delivery and medical devices

Current nanotoxicology research suffers from suboptimal in vitro models, lack of in vitro–in vivo correlations, variability within in vitro protocols, deficits in both material purity and physicochemical characterization. Reliable nanomaterial toxicity and mechanistic insights are required for health and toxicity risk assessments. Much in vitro toxicological data is inconclusive in designating whether nanomaterials for drug delivery and medical device implants are truly safe. A critique is presented to analyze the interface between toxicology and nanopharmaceuticals. Deficiencies of existing practices in toxicology are reviewed and useful emerging techniques (e.g., lab-on-a-chip, tissue engineering, atomic force microscopy, high-content analysis) are highlighted. Cross-fertilization between disciplines will aid development of biocompatible delivery and implant platforms while improvements are being suggested for better translation of nanotoxicology.

Cooperative development of logical modelling standards and tools with CoLoMoTo

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.

Nexus of signaling and endocytosis in oncogenesis driven by non-small cell lung cancer-associated epidermal growth factor receptor mutants

Epidermal growth factor receptor (EGFR) controls a wide range of cellular processes, and aberrant EGFR signaling as a result of receptor overexpression and/or mutation occurs in many types of cancer. Tumor cells in non-small cell lung cancer (NSCLC) patients that harbor EGFR kinase domain mutations exhibit oncogene addiction to mutant EGFR, which confers high sensitivity to tyrosine kinase inhibitors (TKIs). As patients invariably develop resistance to TKIs, it is important to delineate the cell biological basis of mutant EGFR-induced cellular transformation since components of these pathways can serve as alternate therapeutic targets to preempt or overcome resistance. NSCLC-associated EGFR mutants are constitutively-active and induce ligand-independent transformation in nonmalignant cell lines. Emerging data suggest that a number of factors are critical for the mutant EGFR-dependent tumorigenicity, and bypassing the effects of TKIs on these pathways promotes drug resistance. For example, activation of downstream pathways such as Akt, Erk, STAT3 and Src is critical for mutant EGFR-mediated biological processes. It is now well-established that the potency and spatiotemporal features of cellular signaling by receptor tyrosine kinases such as EGFR, as well as the specific pathways activated, is determined by the nature of endocytic traffic pathways through which the active receptors traverse. Recent evidence indicates that NSCLC-associated mutant EGFRs exhibit altered endocytic trafficking and they exhibit reduced Cbl ubiquitin ligase-mediated lysosomal downregulation. More recent work has shown that mutant EGFRs undergo ligand-independent traffic into the endocytic recycling compartment, a behavior that plays a key role in Src pathway activation and oncogenesis. These studies are beginning to delineate the close nexus between signaling and endocytic traffic of EGFR mutants as a key driver of oncogenic processes. Therefore, in this review, we will discuss the links between mutant EGFR signaling and endocytic properties, and introduce potential mechanisms by which altered endocytic properties of mutant EGFRs may alter signaling and vice versa as well as their implications for NSCLC therapy.

Design, Assessment, and in vivo Evaluation of a Computational Model Illustrating the Role of CAV1 in CD4(+) T-lymphocytes

Caveolin-1 (CAV1) is a vital scaffold protein heterogeneously expressed in both healthy and malignant tissue. We focus on the role of CAV1 when overexpressed in T-cell leukemia. Previously, we have shown that CAV1 is involved in cell-to-cell communication, cellular proliferation, and immune synapse formation; however, the molecular mechanisms have not been elucidated. We hypothesize that the role of CAV1 in immune synapse formation contributes to immune regulation during leukemic progression, thereby warranting studies of the role of CAV1 in CD4⁺ T-cells in relation to antigen-presenting cells. To address this need, we developed a computational model of a CD4⁺ immune effector T-cell to mimic cellular dynamics and molecular signaling under healthy and immunocompromised conditions (i.e., leukemic conditions). Using the Cell Collective computational modeling software, the CD4⁺ T-cell model was constructed and simulated under CAV1^+/+, CAV1^+/−, and CAV1^−/− conditions to produce a hypothetical immune response. This model allowed us to predict and examine the heterogeneous effects and mechanisms of CAV1 in silico. Experimental results indicate a signature of molecules involved in cellular proliferation, cell survival, and cytoskeletal rearrangement that were highly affected by CAV1 knock out. With this comprehensive model of a CD4⁺ T-cell, we then validated in vivo protein expression levels. Based on this study, we modeled a CD4⁺ T-cell, manipulated gene expression in immunocompromised versus competent settings, validated these manipulations in an in vivo murine model, and corroborated acute T-cell leukemia gene expression profiles in human beings. Moreover, we can model an immunocompetent versus an immunocompromised microenvironment to better understand how signaling is regulated in patients with leukemia.

A logical model of HIV-1 interactions with the T-cell activation signalling pathway

MOTIVATION

Human immunodeficiency virus type 1 (HIV-1) hijacks host cellular processes to replicate within its host. Through interactions with host proteins, it perturbs and interrupts signaling pathways that alter key cellular functions. Although networks of viral-host interactions have been relatively well characterized, the dynamics of the perturbation process is poorly understood. Dynamic models of infection have the potential to provide insights into the HIV-1 host interaction.

RESULTS

We employed a logical signal flow network to model the dynamic interactions between HIV-1 proteins and key human signal transduction pathways necessary for activation of CD4+ T lymphocytes. We integrated viral-host interaction and host signal transduction data into a dynamic logical model comprised of 137 nodes (16 HIV-1 and 121 human proteins) and 336 interactions collected from the HIV-1 Human Interaction Database. The model reproduced expected patterns of T-cell activation, co-stimulation and co-inhibition. After simulations, we identified 26 host cell factors, including MAPK1&3, Ikkb-Ikky-Ikka and PKA, which contribute to the net activation or inhibition of viral proteins. Through in silico knockouts, the model identified a further nine host cell factors, including members of the PI3K signalling pathway that are essential to viral replication. Simulation results intersected with the findings of three siRNA gene knockout studies and identified potential drug targets. Our results demonstrate how viral infection causes the cell to lose control of its signalling system. Logical Boolean modelling therefore provides a useful approach for analysing the dynamics of host-viral interactions with potential applications for drug discovery.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A kinase inhibitor screen reveals protein kinase C-dependent endocytic recycling of ErbB2 in breast cancer cells

ErbB2 overexpression drives oncogenesis in 20-30% cases of breast cancer. Oncogenic potential of ErbB2 is linked to inefficient endocytic traffic into lysosomes and preferential recycling. However, regulation of ErbB2 recycling is incompletely understood. We used a high-content immunofluorescence imaging-based kinase inhibitor screen on SKBR-3 breast cancer cells to identify kinases whose inhibition alters the clearance of cell surface ErbB2 induced by Hsp90 inhibitor 17-AAG. Less ErbB2 clearance was observed with broad-spectrum PKC inhibitor Ro 31-8220. A similar effect was observed with Go 6976, a selective inhibitor of classical Ca(2+)-dependent PKCs (α, β1, βII, and γ). PKC activation by PMA promoted surface ErbB2 clearance but without degradation, and ErbB2 was observed to move into a juxtanuclear compartment where it colocalized with PKC-α and PKC-δ together with the endocytic recycling regulator Arf6. PKC-α knockdown impaired the juxtanuclear localization of ErbB2. ErbB2 transit to the recycling compartment was also impaired upon PKC-δ knockdown. PMA-induced Erk phosphorylation was reduced by ErbB2 inhibitor lapatinib, as well as by knockdown of PKC-δ but not that of PKC-α. Our results suggest that activation of PKC-α and -δ mediates a novel positive feedback loop by promoting ErbB2 entry into the endocytic recycling compartment, consistent with reported positive roles for these PKCs in ErbB2-mediated tumorigenesis. As the endocytic recycling compartment/pericentrion has emerged as a PKC-dependent signaling hub for G-protein-coupled receptors, our findings raise the possibility that oncogenesis by ErbB2 involves previously unexplored PKC-dependent endosomal signaling.

Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions

Background

An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network.

Results

We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes.

Conclusions

The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure.

Steady state analysis of Boolean molecular network models via model reduction and computational algebra

Background

A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general.

Results

This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean network. The code for the algorithm, as well as the test suite of benchmark networks, is available upon request from the corresponding author.

Conclusions

The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for large Boolean networks with high average connectivity remains an open problem.

Computational modeling of the main signaling pathways involved in mast cell activation

A global and rigorous understanding of the signaling pathways and cross-regulatory processes involved in mast cell activation requires the integration of published information with novel functional datasets into a comprehensive computational model. Based on an exhaustive curation of the existing literature and using the software CellDesigner, we have built and annotated a comprehensive molecular map for the FcεRI signaling network. This map can be used to visualize and interpret high-throughput expression data. Furthermore, leaning on this map and using the logical modeling software GINsim, we have derived a qualitative dynamical model, which recapitulates the most salient features of mast cell activation. The resulting logical model can be used to explore the dynamical properties of the system and its responses to different stimuli, in normal or mutant conditions.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

BACKGROUND

Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

RESULTS

We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.

CONCLUSIONS

SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.