Computational and Mathematical Modelling of the EGF Receptor System

This chapter gives an overview of computational and mathematical modelling of the EGF receptor system. It begins with a survey of motivations for producing such models, then describes the main approaches that are taken to carrying out such modelling, viz. differential equations and individual-based modelling. Finally, a number of projects that applying modelling and simulation techniques to various aspects of the EGF receptor system are described

Logic-Based Models for the Analysis of Cell Signaling Networks

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.National Institutes of Health (U.S.) (Grant P50- GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

Unveiling the Molecular Mechanisms Regulating the Activation of the ErbB Family Receptors at Atomic Resolution through Molecular Modeling and Simulations

The EGFR/ErbB/HER family of kinases contains four homologous receptor tyrosine kinases that are important regulatory elements in key signaling pathways. To elucidate the atomistic mechanisms of dimerization-dependent activation in the ErbB family, we have performed molecular dynamics simulations of the intracellular kinase domains of the four members of the ErbB family (those with known kinase activity), namely EGFR, ErbB2 (HER2) and ErbB4 (HER4) as well as ErbB3 (HER3), an assumed pseudokinase, in different molecular contexts: monomer vs. dimer, wildtype vs. mutant. Using bioinformatics and fluctuation analyses of the molecular dynamics trajectories, we relate sequence similarities to correspondence of specific bond-interaction networks and collective dynamical modes. We find that in the active conformation of the ErbB kinases (except ErbB3), key subdomain motions are coordinated through conserved hydrophilic interactions: activating bond-networks consisting of hydrogen bonds and salt bridges. The inactive conformations also demonstrate conserved bonding patterns (albeit less extensive) that sequester key residues and disrupt the activating bond network. Both conformational states have distinct hydrophobic advantages through context-specific hydrophobic interactions. The inactive ErbB3 kinase domain also shows coordinated motions similar to the active conformations, in line with recent evidence that ErbB3 is a weakly active kinase, though the coordination seems to arise from hydrophobic interactions rather than hydrophilic ones. We show that the functional (activating) asymmetric kinase dimer interface forces a corresponding change in the hydrophobic and hydrophilic interactions that characterize the inactivating interaction network, resulting in motion of the αC-helix through allostery. Several of the clinically identified activating kinase mutations of EGFR act in a similar fashion to disrupt the inactivating interaction network. Our molecular dynamics study reveals the asymmetric dimer interface helps progress the ErbB family through the activation pathway using both hydrophilic and hydrophobic interaction. There is a fundamental difference in the sequence of events in EGFR activation compared with that described for the Src kinase Hck

Scalable reaction network modeling with automatic validation of consistency in Event-B

Constructing a large biological model is a difficult, error-prone process. Small errors in writing a part of the model cascade to the system level and their sources are difficult to trace back. In this paper we extend a recent approach based on Event-B, a state-based formal method with refinement as its central ingredient, allowing us to validate for model consistency step-by-step in an automated way. We demonstrate this approach on a model of the heat shock response in eukaryotes and its scalability on a model of the ErbB signaling pathway. All consistency properties of the model were proved automatically with computer support.</p

Ligand-Specific c-Fos Expression Emerges from the Spatiotemporal Control of ErbB Network Dynamics

SummaryActivation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands

Multi-level model for the investigation of oncoantigen- driven vaccination effect

BACKGROUND: Cancer stem cell theory suggests that cancers are derived by a population of cells named Cancer Stem Cells (CSCs) that are involved in the growth and in the progression of tumors, and lead to a hierarchical structure characterized by differentiated cell population. This cell heterogeneity affects the choice of cancer therapies, since many current cancer treatments have limited or no impact at all on CSC population, while they reveal a positive effect on the differentiated cell populations. RESULTS: In this paper we investigated the effect of vaccination on a cancer hierarchical structure through a multi-level model representing both population and molecular aspects. The population level is modeled by a system of Ordinary Differential Equations (ODEs) describing the cancer population's dynamics. The molecular level is modeled using the Petri Net (PN) formalism to detail part of the proliferation pathway. Moreover, we propose a new methodology which exploits the temporal behavior derived from the molecular level to parameterize the ODE system modeling populations. Using this multi-level model we studied the ErbB2-driven vaccination effect in breast cancer. CONCLUSIONS: We propose a multi-level model that describes the inter-dependencies between population and genetic levels, and that can be efficiently used to estimate the efficacy of drug and vaccine therapies in cancer models, given the availability of molecular data on the cancer driving force

Computational Modeling of Protein Kinases: Molecular Basis for Inhibition and Catalysis

Protein kinases catalyze protein phosphorylation reactions, i.e. the transfer of the γ-phosphoryl group of ATP to tyrosine, serine and threonine residues of protein substrates. This phosphorylation plays an important role in regulating various cellular processes. Deregulation of many kinases is directly linked to cancer development and the protein kinase family is one of the most important targets in current cancer therapy regimens. This relevance to disease has stimulated intensive efforts in the biomedical research community to understand their catalytic mechanisms, discern their cellular functions, and discover inhibitors. With the advantage of being able to simultaneously define structural as well as dynamic properties for complex systems, computational studies at the atomic level has been recognized as a powerful complement to experimental studies. In this work, we employed a suite of computational and molecular simulation methods to (1) explore the catalytic mechanism of a particular protein kinase, namely, epidermal growth factor receptor (EGFR); (2) study the interaction between EGFR and one of its inhibitors, namely erlotinib (Tarceva); (3) discern the effects of molecular alterations (somatic mutations) of EGFR to differential downstream signaling response; and (4) model the interactions of a novel class of kinase inhibitors with a common ruthenium based organometallic scaffold with different protein kinases. Our simulations established some important molecular rules in operation in the contexts of inhibitor-binding, substrate-recognition, catalytic landscapes, and signaling in the EGFR tyrosine kinase. Our results also shed insights on the mechanisms of inhibition and phosphorylation commonly employed by many kinases

Structure and molecular interaction analysis of monoclonal antibodies in complex with receptor tyrosine kinases

Misregulated receptor tyrosine kinases (RTKs), i.e. the epidermal growth factor receptor EGFR or the insulin-like growth factor receptor 1 (IGF-1R), can be involved in the development of cancer. Monoclonal antibodies specifically inhibit the RTKs in cancer therapy. The scope of this thesis is to investigate the molecular basis of the inhibition through the therapeutic antibodies matuzumab (EMD72000) against EGFR and EMD1159476 against IGF-1R. The 3D crystal structure of matuzumab in complex with the EGFR domain III shows an eptiope connected with a novel inhibition mechanism: a non-competitive, sterical inhibition of receptor acitivation. The anti-IGF-1R targeted monoclonal antibody EMD1159476 shows a reduced binding capacity to the receptor in the presence of ligand indicating a competitive inhibition mechanism. The epitope of EMD1159476 is within domain II of the receptor. The results of these molecular interaction studies are important for the clinical therapies with these monoclonal antibodies. The matuzumab-EGFR complex crystal structure shows that a simultaneous binding of matuzumab and cetuximab (Erbitux) is possible. The latter antibody is already in clinical use. A combination of several therapeutic antibodies in cancer treatment might show synergistic effects and benefits for the patients.Verschiedene Rezeptor-Tyrosinkinasen (RTKs), wie z.B. der Epidermale Wachstumsfaktor-rezeptor EGFR oder der Insulin-ähnliche Wachstumsfaktorrezeptor 1 IGF-1R, können durch Misregulation die Entstehung von neoplastischen Zellen hervorrufen. Ein Ansatz in der Krebstherapie ist die gezielte Inhibition von RTKs durch monoklonale Antikörper. Hier wird die molekulare Basis der Inhibition durch die beiden therapeutischen Antikörper Matuzumab (EMD72000) gegen EGFR und EMD1159476 gegen IGF-1R untersucht. Die 3D-Komplexstruktur von Matuzumab mit der EGFR Domäne III zeigt ein Epitop, das auf einen neuen Inhibitionsmechanismus für EGFR hinweist: eine nicht-kompetitive, rein sterische Blockierung der Rezeptoraktivierung. Der gegen IGF-1R gerichtete monoklonale Antikörper EMD1159476 dagegen zeigt eine eingeschränkte Bindung an den Rezeptor in Gegenwart des Liganden. Das Epitop von EMD1159476 liegt innerhalb der Domäne II des Rezeptors. Die Ergebnisse dieser molekularen Interaktionsstudien könnten Einfluss auf die klinischen Therapieansätze mit monoklonalen Antikörpern haben. Die Matuzumab - EGFR Ko-Kristallstruktur zeigt, dass eine simultane Bindung Matuzumabs und des bereits zugelassenen Antikörpers Cetuximab (Erbitux) an EGFR möglich ist. Eine Kombination von mehreren therapeutischen Antikörpern könnte in der Krebstherapie einen synergistischen Effekt zeigen