123 research outputs found
Assimilating Satellite Observations with a Local Ensemble Kalman Filter
Numerical weather prediction relies on data
assimilation to estimate the current state of the atmosphere.
Generally speaking, data assimilation methods combine information from
observations and from a prior forecast state, taking into account
their respective uncertainties. Ensemble-based data
assimilation schemes estimate the forecast uncertainty with the sample
covariance from an ensemble of forecasts. While these schemes have
been shown to successfully assimilate conventional observations of
model state variables, they have only recently begun to
assimilate satellite observations. This dissertation explores some of
the complications that arise when ensemble-based schemes
assimilate satellite observations.
Although ensemble data assimilation schemes often assume that
observations are taken at the time of assimilation, satellite
observations are available almost continuously between consecutive
assimilation times. In Chapter 2, we formulate a
``four-dimensional'' extension to ensemble-based schemes that is
analogous to the operationally used scheme 4D-VAR. Using perfect
model experiments with the Lorenz-96 model, we find that the
four-dimensional ensemble scheme can perform comparably to 4D-VAR.
Many ensemble data assimilation schemes utilize spatial
localization so that a small ensemble can capture the unstable degrees
of freedom in the model state. These local ensemble-based schemes
typically allow the analysis at a given location to depend only on
observations near that location. Meanwhile, the location of satellite
observations cannot be pinpointed in the same manner as conventional
observations. In Chapter 3, we propose a technique to update the
state at a given location by assimilating satellite radiance
observations that are strongly correlated to the model state there.
For satellite retrievals, we propose incorporating the observation
error covariance matrix and selecting the retrievals that have errors
correlated to observations near the location to be updated. Our
selection techniques improve the analysis obtained when assimilating
simulated satellite observations with a seven-layer primitive equation
model, the SPEEDY model.
Finally, satellite radiance observations are subject to
state-dependent, systematic errors due to errors in the radiative
transfer model used as the observation operator. In Chapter 4 we
propose applying state-space augmentation to ensemble based
assimilation schemes to estimate satellite radiance biases during the
data assimilation procedure. Our approach successfully corrects such
systematic errors in simulated biased satellite observations with the
SPEEDY model
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
Recommended from our members
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
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
Extracellular Vesicles Released by Tumor Endothelial Cells Spread Immunosuppressive and Transforming Signals Through Various Recipient Cells
- …