Tracking transcriptional activities with high-content epifluorescent imaging

IEEE Trans. Signal Process.

Dougherty

2016

Self Cite

The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models.

Section: Experiments and Discussionmentioning

confidence: 99%

Bayesian Regression With Network Prior: Optimal Bayesian Filtering Perspective

IEEE Trans. Signal Process.

Dougherty

2016

Self Cite

“…A single assay is carried out by epifluorescent imaging of a portion of the bottom of a well in a 384 well plate, producing an image of the cells in that region (200-500) bearing fluorescent reporters. Fluorescent intensity data can be extracted from these images using specialized image analysis tools developed for this application [14].…”

Section: Methodsmentioning

confidence: 99%

Robust identification of molecularly targeted drug effect coefficient using H<inf>∞</inf> filter

2013 IEEE Global Conference on Signal and Information Processing

Dougherty

2013

In recent years, targeted therapy to treat cancer is gaining popularity. However, how to quantitatively and dynamically analyze the drug effect for molecularly targeted agents is quite different from traditional cytotoxic drugs. A novel preclinical model combining experimental methods and theoretical analysis is proposed to investigate the mechanisms of action and identify pharmacodynamic characteristic of a targeted drug. A robust estimator, namely the H∞ filter, is applied to timecourse data of tumor cells' responses to drugs. The results show strong correlation between dosage and drug effect coefficient. The proposed method will help us gain more comprehensive understanding of the molecular mechanisms of action of drugs and design better dosing regimens for targeted therapy.

“…This produces data on ~200-400 individual cells per well that can be analyzed both individually, as a distribution, or in aggregate, as an average. Fluorescent intensity data can be extracted from these images using specialized image analysis tools developed for this application [43]. This image processing procedures include finding cells, identifying individual cells, and quantifying the fluorescence associated with each cell.…”

Section: Methodsmentioning

confidence: 99%

“…To facilitate automatic processing of the experiment results, the transcriptional levels of the fluorescent images need be properly extracted, quantized, and saved and the image processing algorithm should be fast with good balance between performance and robustness [43]. An algorithm based on morphological image processing [44], in particular, the watershed transformation [45] is currently adopted in our study.…”

Section: Methodsmentioning

confidence: 99%

Assessing the efficacy of molecularly targeted agents on cell line-based platforms by using system identification

Jiang

et al. 2012

BMC Genomics

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Background Molecularly targeted agents (MTAs) are increasingly used for cancer treatment, the goal being to improve the efficacy and selectivity of cancer treatment by developing agents that block the growth of cancer cells by interfering with specific targeted molecules needed for carcinogenesis and tumor growth. This approach differs from traditional cytotoxic anticancer drugs. The lack of specificity of cytotoxic drugs allows a relatively straightforward approach in preclinical and clinical studies, where the optimal dose has usually been defined as the "maximum tolerated dose" (MTD). This toxicity-based dosing approach is founded on the assumption that the therapeutic anticancer effect and toxic effects of the drug increase in parallel as the dose is escalated. On the contrary, most MTAs are expected to be more selective and less toxic than cytotoxic drugs. Consequently, the maximum therapeutic effect may be achieved at a "biologically effective dose" (BED) well below the MTD. Hence, dosing study for MTAs should be different from cytotoxic drugs. Enhanced efforts to molecularly characterize the drug efficacy for MTAs in preclinical models will be valuable for successfully designing dosing regimens for clinical trials. Results A novel preclinical model combining experimental methods and theoretical analysis is proposed to investigate the mechanism of action and identify pharmacodynamic characteristics of the drug. Instead of fixed time point analysis of the drug exposure to drug effect, the time course of drug effect for different doses is quantitatively studied on cell line-based platforms using system identification, where tumor cells' responses to drugs through the use of fluorescent reporters are sampled over a time course. Results show that drug effect is time-varying and higher dosages induce faster and stronger responses as expected. However, the drug efficacy change along different dosages is not linear; on the contrary, there exist certain thresholds. This kind of preclinical study can provide valuable suggestions about dosing regimens for the in vivo experimental stage to increase productivity.