Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics.

BACKGROUND: The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. RESULTS: Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. CONCLUSIONS: The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Stochastic Simulation of Process Calculi for Biology

Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

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

Multiscale Modeling of the ErbB Receptor Tyrosine Kinase Signaling Network Through Theory and Experiment

The biochemical processes occurring within a living cell span a spectrum of scales in space and time, ranging from the nano- to the macro-scale. We note that a single cellular process often operates on multiple spatial and temporal scales, and thus it becomes necessary to combine modeling techniques in multiscale approaches, in which different levels of theory are synergized to describe a system at a number of scales or resolutions. In this work we apply a multiscale modeling framework to investigate the molecular regulatory mechanisms governing the activation of the ErbB receptor tyrosine kinases, a family of kinases which are commonly over-expressed or mutated in human cancers, with a focus on the HER3 and HER4 kinases. Our multiscale model of HER3, a kinase which, until recently, has been considered kinase-dead, presents evidence of HER3 catalytic activity and demonstrates that even a weak HER3 signal can be amplified by other cellular signaling mechanisms to induce drug resistance to tyrosine kinase inhibitors in silico. Thus HER3, rather than the commonly-targeted EGFR and HER2 kinases, may represent a superior therapeutic target in specific ErbB-driven cancers. In the second major study, we construct a multiscale model of activity in the HER4 kinase, which has been shown to perform an anti-cancer role in certain tumor cells, by steering the cell toward a program of cellular differentiation and away from a program of uncontrolled proliferation. Our HER4 model, which applies a combined computational and experimental approach, elucidates the molecular mechanisms underlying this HER4-mediated ‘switch’ to the cellular differentiation program, with the ultimate aim of exploiting or modulating the HER4 pathway as a potential therapy in specific ErbB-driven cancers. Furthermore, the model provides structural insights into the effects of several HER4 somatic mutations which have recently been discovered in a subset of cancer patients, and which abrogate the anti-cancer effects of HER4 activity. We have illustrated that multiscale modeling provides a powerful and quantitative platform for investigating the complexity inherent in intracellular signaling pathways and rationalizing the effects of molecular perturbations on downstream signaling events and ultimately, on the cell phenotype

Cell-signalling dynamics in time and space

The specificity of cellular responses to receptor stimulation is encoded by the spatial and temporal dynamics of downstream signalling networks. Computational models provide insights into the intricate relationships between stimuli and responses and reveal mechanisms that enable networks to amplify signals, reduce noise and generate discontinuous bistable dynamics or oscillations. These temporal dynamics are coupled to precipitous spatial gradients of signalling activities, which guide pivotal intracellular processes, but also necessitate mechanisms to facilitate signal propagation across a cell

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

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

<p>Abstract</p> <p>Background</p> <p>Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.</p> <p>Results</p> <p>By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.</p> <p>Conclusions</p> <p>We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream <it>in silico </it>functional inference analyses based on high content data.</p

Mechanistic Modeling of Cell Signaling Heterogeneity and Chemokine Gradients in Cancer

Heterogeneity in cancer can give rise to rare subpopulations of cells that are unlike the bulk average of the cancer cell population, such as metastatic cells which promote the formation of secondary tumors at distant sites. These subpopulations of cells can dramatically affect the progression of the disease. Recent work suggests that heterogeneity in cancer cell populations assessed by clonal analysis cannot fully account for differences in single cell behaviors. Nonclonal heterogeneity, such as variability due to the cellular microenvironment, can play a key role in promoting cancer behaviors. Signaling heterogeneity to downstream kinase effectors is one manifestation of nonclonal heterogeneity. Seemingly-identical cancer cells activate heterogeneous signaling to extracellular signal-regulated kinase (ERK) and Akt, two kinases implicated in cancer growth, survival, proliferation, and metastasis. The mechanistic drivers that promote signaling heterogeneity remain unclear and understanding them is crucial in effectively treating cancer. Advancements in single-cell experimental techniques and computational modeling can elucidate how the spatiotemporal tumor microenvironment shapes cancer cell behavior. In this thesis, we construct mechanistic computational models coupled to experimental data to understand the major drivers of nonclonal heterogeneity in cancer. First, we built a single-cell computational model to explain the heterogeneity in cell signaling responses to ERK and Akt that we observed in breast cancer cells in experiments. The model predicted that the pre-existing signaling state of cancer cells controls signaling responses through chemokine receptor CXCR4, a critical receptor in cancer initiation and metastasis, and that these pre-existing states are shaped by environmental stimuli. The model also predicted that targeted therapies currently under clinical investigation may inadvertently potentiate pro-metastatic signaling through CXCR4. Second, we expanded our computational model to test its robustness in predicting signaling through another key receptor in cancer proliferation, epidermal growth factor receptor (EGFR). Our model predicted single-cell signaling responses in two breast cancer cell lines of various mutational backgrounds to different doses of both CXCR4 and EGFR stimuli. The robustness of our model solidified our hypothesis that variation in cell signaling stems from extrinsic noise in three key pathway components: phosphatidylinositol-3-kinase (PI3K), Ras, and mammalian target of rapamycin complex 1 (mTORC1). Third, we built a spatial model of the tumor microenvironment to understand the impact of circadian rhythms and heterogeneity in the spatial tumor composition in promoting metastasis. We found that the magnitude of chemokine gradients, which can act as the molecular highway directing cancer cells where to invade and metastasize, varies throughout the course of the day with the circadian rhythm such that therapies may be more or less effective based on time of administration. Additionally, the spatial arrangement of a tumor with regard to cells secreting and scavenging these chemokines, which can vary tumor-to-tumor or within a single tumor, has a marked impact on the direction of chemokine gradients. We found that specific arrangements of cells in tumors promote chemokine gradients that can direct cancer cells to intravasate and metastasize. Overall, this thesis builds on our knowledge of heterogeneity in cancer and provides suggestions for clinical opportunities.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153359/1/spinospc_1.pd

Structure-Functional Prediction and Analysis of Cancer Mutation Effects in Protein Kinases

A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal low activity state to the active state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes