Hybrid Modeling of Cell Signaling and Transcriptional Reprogramming and Its Application in C. elegans Development

Modeling of signal driven transcriptional reprogramming is critical for understanding of organism development, human disease, and cell biology. Many current modeling techniques discount key features of the biological sub-systems when modeling multiscale, organism-level processes. We present a mechanistic hybrid model, GESSA, which integrates a novel pooled probabilistic Boolean network model of cell signaling and a stochastic simulation of transcription and translation responding to a diffusion model of extracellular signals. We apply the model to simulate the well studied cell fate decision process of the vulval precursor cells (VPCs) in C. elegans, using experimentally derived rate constants wherever possible and shared parameters to avoid overfitting. We demonstrate that GESSA recovers (1) the effects of varying scaffold protein concentration on signal strength, (2) amplification of signals in expression, (3) the relative external ligand concentration in a known geometry, and (4) feedback in biochemical networks. We demonstrate that setting model parameters based on wild-type and LIN-12 loss-of-function mutants in C. elegans leads to correct prediction of a wide variety of mutants including partial penetrance of phenotypes. Moreover, the model is relatively insensitive to parameters, retaining the wild-type phenotype for a wide range of cell signaling rate parameters

Dynamics in hybrid complex systems of switches and oscillators

While considerable progress has been made in the analysis of large systems containing a single type of coupled dynamical component (e.g., coupled oscillators or coupled switches), systems containing diverse components (e.g., both oscillators and switches) have received much less attention. We analyze large, hybrid systems of interconnected Kuramoto oscillators and Hopfield switches with positive feedback. In this system, oscillator synchronization promotes switches to turn on. In turn, when switches turn on, they enhance the synchrony of the oscillators to which they are coupled. Depending on the choice of parameters, we find theoretically coexisting stable solutions with either (i) incoherent oscillators and all switches permanently off, (ii) synchronized oscillators and all switches permanently on, or (iii) synchronized oscillators and switches that periodically alternate between the on and off states. Numerical experiments confirm these predictions. We discuss how transitions between these steady state solutions can be onset deterministically through dynamic bifurcations or spontaneously due to finite-size fluctuations

Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments

This paper explores the potential of Local Ensemble Transform Kalman Filter (LETKF) by comparing the performance of LETKF with an operational 3D-Var assimilation system, Physical-Space Statistical Analysis System (PSAS), under a perfect model scenario. The comparison is carried out on the finite volume Global Circulation Model (fvGCM) with 72 grid points zonally, 46 grid points meridionally and 55 vertical levels. With only forty ensemble members, LETKF obtains an analysis and forecasts with lower RMS errors than those from PSAS. The performance of LETKF is further improved, especially over the oceans, by assimilating simulated temperature observations from rawinsondes and conventional surface pressure observations instead of geopotential heights. An initial decrease of the forecast errors in the NH observed in PSAS but not in LETKF suggests that the PSAS analysis is less balanced. The observed advantage of LETKF over PSAS is due to the ability of the forty-member ensemble from LETKF to capture flow-dependent errors and thus create a good estimate of the true background uncertainty. Furthermore, localization makes LETKF highly parallel and efficient, requiring only 5 minutes per analysis in a cluster of 20 PCs with forty ensemble members.Comment: 50 pages, 11 figure

Gene expression signatures modulated by epidermal growth factor receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma.

BACKGROUND: Aberrant activation of signaling pathways downstream of epidermal growth factor receptor (EGFR) has been hypothesized to be one of the mechanisms of cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to ligand stimulation and transfected with EGFR, RELA/p65, or HRASVal12D. RESULTS: The gene expression patterns that distinguished the HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12D further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines. CONCLUSIONS: Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies

1999 Media Guide

1999 Men\u27s Track and Field Media Guide, George Fox College

Loss of Trop2 causes ErbB3 activation through a neuregulin-1-dependent mechanism in the mesenchymal subtype of HNSCC

In head and neck squamous cell cancer (HNSCC), four intrinsic subtypes (or groups) have been identified, and each one possesses a unique biology that will require specific treatment strategies. We previously reported that mesenchymal (group 2) tumors exhibit reduced levels of Trop2 expression. In this study, we investigated the functional role of Trop2 in HNSCC and find that loss results in autocrine activation of the EGFR family member ErbB3 via neuregulin-1. Trop2 localizes to both the cell surface and cytosol of HNSCC cells and forms a complex with neuregulin-1, which is predominantly cytosolic. Inactivation of Trop2 increases the concentration of neuregulin-1 at the cell surface where it is cleaved to activate ErbB3. In primary HNSCC, detection of ErbB3 activation was limited to Trop2 negative tumors. An analysis of the Cancer Genome Atlas (TCGA) HNSCC dataset confirms enrichment for ErbB3 activity in mesenchymal tumors. Notably, Trop2 loss triggers sensitivity to anti-ErbB3 antibodies, which results in reduced proliferation and tumorigenic growth of Trop2 negative HNSCC cancer cells. These results uncover a molecular mechanism by which tumor cells control the amount of cell-surface neuregulin-1 available for cleavage and ErbB3 activation. Moreover, we demonstrate that Trop2 is a potential surrogate biomarker to identify tumors with ErbB3 activation and may therefore respond to anti-ErbB3 therapeutics

A preliminary analysis of interleukin-1 ligands as potential predictive biomarkers of response to cetuximab

The epidermal growth factor receptor (EGFR) monoclonal IgG1 antibody cetuximab is approved for first-line treatment of recurrent and metastatic (R/M) HNSCC as a part of the standard of care EXTREME regimen (platinum/5-fluorouracil/cetuximab). This regimen has relatively high response and disease control rates but is generally not curative and many patients will experience recurrent disease and/or metastasis. Therefore, there is a great need to identify predictive biomarkers for recurrence and disease progression in cetuximab-treated HNSCC patients to facilitate patient management and allow for treatment modification. The goal of this work is to assess the potential of activating interleukin-1 (IL-1) ligands (IL-1 alpha [IL-1α], IL-1 beta [IL-1β]) as predictive biomarkers of survival outcomes in HNSCC patients treated with cetuximab-based chemotherapy.2016 AACR-Bayer Innovation and Discovery Grant [16-80-44-SIMO]; National Institutes of Health (NIH) [R01DE024550, F99CA223062, R01 CA177669, P30 CA006973, P50 DE019032, T32 AI007511]; University of Iowa Department of Pathology Research Grant; University of Iowa Head and Neck Cancer Symposium Seed Grant; Johns Hopkins University Catalyst AwardOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.

Although promoter-associated CpG islands have been established as targets of DNA methylation changes in cancer, previous studies suggest that epigenetic dysregulation outside the promoter region may be more closely associated with transcriptional changes. Here we examine DNA methylation, chromatin marks, and transcriptional alterations to define the relationship between transcriptional modulation and spatial changes in chromatin structure. Using human papillomavirus-related oropharyngeal carcinoma as a model, we show aberrant enrichment of repressive H3K9me3 at the transcriptional start site (TSS) with methylation-associated, tumor-specific gene silencing. Further analysis identifies a hypermethylated subtype which shows a functional convergence on MYC targets and association with CREBBP/EP300 mutation. The tumor-specific shift to transcriptional repression associated with DNA methylation at TSSs was confirmed in multiple tumor types. Our data may show a common underlying epigenetic dysregulation in cancer associated with broad enrichment of repressive chromatin marks and aberrant DNA hypermethylation at TSSs in combination with MYC network activation