Michael Bittner

Texas A&M University, College Station, Texas, United States

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Publications (199)1199.86 Total impact

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    ABSTRACT: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory. We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis. The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data.
    Full-text · Article · Sep 2015 · BMC Bioinformatics
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    Jianping Hua · Michael L Bittner · Edward R Dougherty
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    ABSTRACT: Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive simulation study focusing on the ranking performance of seven representative GSA methods. We overcome the limitation on the availability of real data sets by creating hybrid data models from existing large data sets. To build the data model, we pick a master gene from the data set to form the ground truth and artificially generate the phenotype labels. Multiple hybrid data models can be constructed from one data set and multiple data sets of smaller sizes can be generated by resampling the original data set. This approach enables us to generate a large batch of data sets to check the ranking performance of GSA methods. Our simulation study reveals that for the proposed data model, the Q2 type GSA methods have in general better performance than other GSA methods and the global test has the most robust results. The properties of a data set play a critical role in the performance. For the data sets with highly connected genes, all GSA methods suffer significantly in performance.
    Full-text · Article · Feb 2014 · Cancer informatics
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    ABSTRACT: Unlabelled: Insensitivity to standard clinical interventions, including chemotherapy, radiotherapy, and tyrosine kinase inhibitor (TKI) treatment, remains a substantial hindrance towards improving the prognosis of patients with non-small cell lung cancer (NSCLC). The molecular mechanism of therapeutic resistance remains poorly understood. The TNF-like weak inducer of apoptosis (TWEAK)-FGF-inducible 14 (TNFRSF12A/Fn14) signaling axis is known to promote cancer cell survival via NF-κB activation and the upregulation of prosurvival Bcl-2 family members. Here, a role was determined for TWEAK-Fn14 prosurvival signaling in NSCLC through the upregulation of myeloid cell leukemia sequence 1 (MCL1/Mcl-1). Mcl-1 expression significantly correlated with Fn14 expression, advanced NSCLC tumor stage, and poor patient prognosis in human primary NSCLC tumors. TWEAK stimulation of NSCLC cells induced NF-κB-dependent Mcl-1 protein expression and conferred Mcl-1-dependent chemo- and radioresistance. Depletion of Mcl-1 via siRNA or pharmacologic inhibition of Mcl-1, using EU-5148, sensitized TWEAK-treated NSCLC cells to cisplatin- or radiation-mediated inhibition of cell survival. Moreover, EU-5148 inhibited cell survival across a panel of NSCLC cell lines. In contrast, inhibition of Bcl-2/Bcl-xL function had minimal effect on suppressing TWEAK-induced cell survival. Collectively, these results position TWEAK-Fn14 signaling through Mcl-1 as a significant mechanism for NSCLC tumor cell survival and open new therapeutic avenues to abrogate the high mortality rate seen in NSCLC. Implications: The TWEAK-Fn14 signaling axis enhances lung cancer cell survival and therapeutic resistance through Mcl-1, positioning both TWEAK-Fn14 and Mcl-1 as therapeutic opportunities in lung cancer.
    Full-text · Article · Jan 2014 · Molecular Cancer Research
  • Xiangfang Li · Lijun Qian · Michael L. Bittner · Edward R. Dougherty
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    ABSTRACT: In this paper, a model that combining cell population and genetic regulation within a single cell by using stochastic hybrid systems is proposed. The objective is to study the response of a population of cancer cells to various drugs that targeting the proliferation and survival pathways. The proposed model captures both the dynamics of the cell population and the dynamics of gene regulations within each individual cell. We use drug Lapatinib applied to colon cancer cell line HCT-116 as an example to validate the proposed model. Simulation results demonstrate the phenomena that observed in TGen experiments.
    No preview · Conference Paper · Nov 2013
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    ABSTRACT: Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. Conclusions The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.
    Full-text · Article · Feb 2013 · BMC Genomics
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    ABSTRACT: Two issues are critical to the development of effective cancer-drug combinations. First, it is necessary to determine common combinations of alterations that exert strong control over proliferation and survival regulation for the general type of cancer being considered. Second, it is necessary to have a drug testing method that allows one to assess the variety of responses that can be provoked by drugs acting at key points in the cellular processes dictating proliferation and survival. Utilizing a previously reported GFP (green fluorescent protein) reporter-based technology that provides dynamic measurements of individual reporters in individual cells, the present paper proposes a dynamical systems approach to these issues. It involves a three-state experimental design: (1) formulate an oncologic pathway model of relevant processes; (2) perturb the pathways with the test drug and drugs with known effects on components of the pathways of interest; and (3) measure process activity indicators at various points on cell populations. This design addresses the fundamental problems in the design and analysis of combinatorial drug treatments. We apply the dynamical approach to three issues in the context of colon cancer cell lines: (1) identification of cell subpopulations possessing differing degrees of drug sensitivity; (2) the consequences of different drug dosing strategies on cellular processes; and (3) assessing the consequences of combinatorial versus monotherapy. Finally, we illustrate how the dynamical systems approach leads to a mechanistic hypothesis in the colon cancer HCT116 cell line.
    No preview · Article · Feb 2013 · Journal of Biological Systems
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    Michael L Bittner · Edward R Dougherty
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    ABSTRACT: For science, theoretical or applied, to significantly advance, researchers must use the most appropriate mathematical methods. A century and a half elapsed between Newton's development of the calculus and Laplace's development of celestial mechanics. One cannot imagine the latter without the former. Today, more than three-quarters of a century has elapsed since the birth of stochastic systems theory. This article provides a perspective on the utilization of systems theory as the proper vehicle for the development of systems biology and its application to complex regulatory diseases such as cancer.
    Preview · Article · Oct 2012 · Cancer informatics
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    Xiangfang Li · Lijun Qian · Jianping Hua · Michael L Bittner · Edward R Dougherty
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    ABSTRACT: 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.
    Full-text · Article · Oct 2012 · BMC Genomics
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    Chen Zhao · Jianping Hua · Michael L Bittner · Ivan Ivanov · Edward R Dougherty
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    ABSTRACT: In early drug development, it would be beneficial to be able to identify those dynamic patterns of gene response that indicate that drugs targeting a particular gene will be likely or not to elicit the desired response. One approach would be to quantitate the degree of similarity between the responses that cells show when exposed to drugs, so that consistencies in the regulation of cellular response processes that produce success or failure can be more readily identified. We track drug response using fluorescent proteins as transcription activity reporters. Our basic assumption is that drugs inducing very similar alteration in transcriptional regulation will produce similar temporal trajectories on many of the reporter proteins and hence be identified as having similarities in their mechanisms of action (MOA). The main body of this work is devoted to characterizing similarity in temporal trajectories/signals. To do so, we must first identify the key points that determine mechanistic similarity between two drug responses. Directly comparing points on the two signals is unrealistic, as it cannot handle delays and speed variations on the time axis. Hence, to capture the similarities between reporter responses, we develop an alignment algorithm that is robust to noise, time delays and is able to find all the contiguous parts of signals centered about a core alignment (reflecting a core mechanism in drug response). Applying the proposed algorithm to a range of real drug experiments shows that the result agrees well with the prior drug MOA knowledge. The R code for the RLCSS algorithm is available at http://gsp.tamu.edu/Publications/supplementary/zhao12a.
    Full-text · Article · May 2012 · Bioinformatics
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    Full-text · Article · Apr 2012 · Breast Cancer Research
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    ABSTRACT: High-content cell imaging based on fluorescent protein reporters has recently been used to track the transcriptional activities of multiple genes under different external stimuli for extended periods. This technology enhances our ability to discover treatment-induced regulatory mechanisms, temporally order their onsets and recognize their relationships. To fully realize these possibilities and explore their potential in biological and pharmaceutical applications, we introduce a new data processing procedure to extract information about the dynamics of cell processes based on this technology. The proposed procedure contains two parts: (1) image processing, where the fluorescent images are processed to identify individual cells and allow their transcriptional activity levels to be quantified; and (2) data representation, where the extracted time course data are summarized and represented in a way that facilitates efficient evaluation. Experiments show that the proposed procedure achieves fast and robust image segmentation with sufficient accuracy. The extracted cellular dynamics are highly reproducible and sensitive enough to detect subtle activity differences and identify mechanisms responding to selected perturbations. This method should be able to help biologists identify the alterations of cellular mechanisms that allow drug candidates to change cell behavior and thereby improve the efficiency of drug discovery and treatment design.
    No preview · Article · Apr 2012 · Journal of Biomedical Optics
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    Chen Zhao · Ivan Ivanov · Michael L Bittner · Edward R Dougherty
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    ABSTRACT: To effectively intervene when cells are trapped in pathological modes of operation it is necessary to build models that capture relevant network structure and include char-acterization of dynamical changes within the system. The model must be of sufficient detail that it facilitates the selection of intervention points where pathological cell behav-ior arising from improper regulation can be stopped. What is known about this type of cellular decision-making is consistent with the general expectations associated with any kind of decision-making operation. If the result of a decision at one node is seri-ally transmitted to other nodes, resetting their states, then the process may suffer from mechanistic inefficiencies of transmission or from blockage or activation of transmission through the action of other nodes acting on the same node. A standard signal-processing network model, Bayesian networks, can model these properties. This paper employs a Bayesian tree model to characterize conditional pathway logic and quantify the effects of different branching patterns, signal transmission efficiencies and levels of alternate or redundant inputs. In particular, it characterizes master genes and canalizing genes within the quantitative framework. The model is also used to examine what inferences about the network structure can be made when perturbations are applied to various points in the network.
    Full-text · Article · Dec 2011 · Journal of Biological Systems
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: Aristotelian epistemology, three pillars;causality, of knowledge;Hume's cause, effect on habit
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: biology, model-based experiment;systems-level knowledge;commercial imaging systems
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: scientific knowledge, mathematics;causality-based epistemology;classical measurement uncertainty
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: science and knowledge;uncertainty, test of time;pre-Galilean, post-Galilean mind
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: Example: External Control in Gene Regulatory Networks
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: sola fides, causality to expectation;mathematical training;pure reason, and Nature
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: Example: A Differential Equation Model for Protein Concentrations in Escherichia Coli
    No preview · Article · Oct 2011
  • Edward R. Dougherty · Michael L. Bittner
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    ABSTRACT: Example: A Contextual Model of Gene Regulation Example: The Hypothetico - Deductive Method in Cell Biology - Structural Intervention Example: Intrinsically Multivariate Predictive (IMP) Genes
    No preview · Article · Oct 2011

Publication Stats

13k Citations
1,199.86 Total Impact Points

Institutions

  • 2000-2015
    • Texas A&M University
      • Department of Electrical and Computer Engineering
      College Station, Texas, United States
    • Cancer Institute of New Jersey (CINJ)
      New York, New York, United States
  • 2003-2014
    • Translational Genomics Research Institute
      • Division of Computational Biology
      Phoenix, Arizona, United States
    • Northern Inyo Hospital
      BIH, California, United States
  • 2003-2012
    • Lund University
      • Department of Oncology
      Lund, Skåne, Sweden
  • 2008
    • University of São Paulo
      • Institute of Mathematics and Statistics (IME) (São Paulo)
      San Paulo, São Paulo, Brazil
  • 1997-2007
    • National Human Genome Research Institute
      Maryland, United States
    • Stanford University
      • Department of Biochemistry
      Stanford, California, United States
    • Universität Heidelberg
      • Institute of Human Genetics
      Heidelburg, Baden-Württemberg, Germany
  • 1996-2007
    • National Institutes of Health
      • • Branch of Pediatric Oncology
      • • Laboratory of Cancer Biology and Genetics
      Maryland, United States
  • 2002
    • National Cancer Institute (USA)
      베서스다, Maryland, United States
  • 1999-2001
    • Cancer Genetics, Inc.
      رادرفورد، نیوجرسی, New Jersey, United States
    • NCI-Frederick
      Фредерик, Maryland, United States
    • The University of Arizona
      Tucson, Arizona, United States
  • 1995
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States