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Michael L Bittner

Publications and source records attributed to Michael L Bittner.

17 recordsLinked to original sources
PubMed2005

Intervention in a family of Boolean networks.

MOTIVATION: Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is a collection of Boolean networks in which the gene state vector transitions according to the rules of one of the constituent networks and where network choice is governed by a selection distribution. The theory of automatic control has been applied to find optimal strategies for manipulating external control variables that affect the transition probabilities to desirably affect dynamic evolution over a finite time horizon. In this paper we treat a case in which we lack the governing probability structure for Boolean network selection, so we simply have a family of Boolean networks, but where these networks possess a common attractor structure. This corresponds to the situation in which network construction is treated as an ill-posed inverse problem in which there are many Boolean networks created from the data under the constraint that they all possess attractor structures matching the data states, which are assumed to arise from sampling the steady state of the real biological network. RESULTS: Given a family of Boolean networks possessing a common attractor structure composed of singleton attractors, a control algorithm is derived by minimizing a composite finite-horizon cost function that is a weighted average over all the individual networks, the idea being that we desire a control policy that on average suits the networks because these are viewed as equivalent relative to the data. The weighting for each network at any time point is taken to be proportional to the instantaneous estimated probability of that network being the underlying network governing the state transition. The results are applied to a family of Boolean networks derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation. AVAILABILITY: The software is available on request. SUPPLEMENTARY INFORMATION: The supplementary Information is available at http://ee.tamu.edu/~edward/tree

Algorithms↗
PubMed2005

Generating Boolean networks with a prescribed attractor structure.

MOTIVATION: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). RESULTS: The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. AVAILABILITY: The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm

Algorithms↗
PubMed2005

Stress-specific signatures: expression profiling of p53 wild-type and -null human cells.

Gene expression responses of human cell lines exposed to a diverse set of stress agents were compared by cDNA microarray hybridization. The B-lymphoblastoid cell line TK6 (p53 wild-type) and its p53-null derivative, NH32, were treated in parallel to facilitate investigation of p53-dependent responses. RNA was extracted 4 h after the beginning of treatment when no notable decrease in cell viability was evident in the cultures. Gene expression signatures were defined that discriminated between four broad general mechanisms of stress agents: Non-DNA-damaging stresses (heat shock, osmotic shock, and 12-O-tetradecanoylphorbol 13-acetate), agents causing mainly oxidative stress (arsenite and hydrogen peroxide), ionizing radiations (neutron and gamma-ray exposures), and other DNA-damaging agents (ultraviolet radiation, methyl methanesulfonate, adriamycin, camptothecin, and cis-Platinum(II)diammine dichloride (cisplatin)). Within this data set, non-DNA-damaging stresses could be discriminated from all DNA-damaging stresses, and profiles for individual agents were also defined. While DNA-damaging stresses showed a strong p53-dependent element in their responses, no discernible p53-dependent responses were triggered by the non-DNA-damaging stresses. A set of 16 genes did exhibit a robust p53-dependent pattern of induction in response to all nine DNA-damaging agents, however.

B-Lymphocytes↗
PubMed2004

Boolean relationships among genes responsive to ionizing radiation in the NCI 60 ACDS.

MOTIVATION: An early use of gene-expression data coming from microarrays was to discover non-linear multivariate intergene relationships. Pursuing this direction, the motivation for this paper is 2-fold: (1) to discover and elucidate multivariate logical predictive relations among gene expressions in a dataset arising from radiation studies using the NCI 60 Anti-Cancer Drug Screen (ACDS) cell lines; and (2) to demonstrate how these logical relations based on coarse quantization reflect corresponding relations in the continuous data. RESULTS: Using the coefficient of determination, a large number of logical relationships have been discovered among genes in the NCI 60 ACDS cell lines. Moreover, these relationships can be seen directly in the original continuous data, and many are robust relative to the thresholds used to obtain the logical data from the continuous data. A key observation is that a number of intergene relationships appear to be considerably stronger when p53 is functional as compared to when it is not, which is consistent with earlier findings in the literature. AVAILABILITY: The appendix is available at http://gsp.tamu.edu/Publications/supplement.htm CONTACT: edward@ee.tamu.edu.

Cell Line, Tumor↗
PubMed2004

Intervention in context-sensitive probabilistic Boolean networks.

MOTIVATION: Intervention in a gene regulatory network is used to help it avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is essentially a finite collection of Boolean networks in which at any discrete time point the gene state vector transitions according to the rules of one of the constituent networks. For an instantaneously random PBN, the governing Boolean network is randomly chosen at each time point. For a context-sensitive PBN, the governing Boolean network remains fixed for an interval of time until a binary random variable determines a switch. The theory of automatic control has been previously applied to find optimal strategies for manipulating external (control) variables that affect the transition probabilities of an instantaneously random PBN to desirably affect its dynamic evolution over a finite time horizon. This paper extends the methods of external control to context-sensitive PBNs. RESULTS: This paper treats intervention via external control variables in context-sensitive PBNs by extending the results for instantaneously random PBNs in several directions. First, and most importantly, whereas an instantaneously random PBN yields a Markov chain whose state space is composed of gene vectors, each state of the Markov chain corresponding to a context-sensitive PBN is composed of a pair, the current gene vector occupied by the network and the current constituent Boolean network. Second, the analysis is applied to PBNs with perturbation, meaning that random gene perturbation is permitted at each instant with some probability. Third, the (mathematical) influence of genes within the network is used to choose the particular gene with which to intervene. Lastly, PBNs are designed from data using a recently proposed inference procedure that takes steady-state considerations into account. The results are applied to a context-sensitive PBN derived from gene-expression data collected in a study of metastatic melanoma, the intent being to devise a control strategy that reduces the WNT5A gene's action in affecting biological regulation, since the available data suggest that disruption of this influence could reduce the chance of a melanoma metastasizing.

Algorithms↗
PubMed2004

Which is better for cDNA-microarray-based classification: ratios or direct intensities.

MOTIVATION: There are two general methods for making gene-expression microarrays: one is to hybridize a single test set of labeled targets to the probe, and measure the background-subtracted intensity at each probe site; the other is to hybridize both a test and a reference set of differentially labeled targets to a single detector array, and measure the ratio of the background-subtracted intensities at each probe site. Which method is better depends on the variability in the cell system and the random factors resulting from the microarray technology. It also depends on the purpose for which the microarray is being used. Classification is a fundamental application and it is the one considered here. RESULTS: This paper describes a model-based simulation paradigm that compares the classification accuracy provided by these methods over a variety of noise types and presents the results of a study modeled on noise typical of cDNA microarray data. The model consists of four parts: (1) the measurement equation for genes in the reference state; (2) the measurement equation for genes in the test state; (3) the ratio and normalization procedure for a dual-channel system; and (4) the intensity and normalization procedure for a single-channel system. In the reference state, the mean intensities are modeled as a shifted exponential distribution, and the intensity for a particular gene is modeled via a normal distribution, Normal(I, alphaI), about its mean intensity I, with alpha being the coefficient of variation of the cell system. In the test state, some genes have their intensities up-regulated by a random factor. The model includes a number of random factors affecting intensity measurement: deposition gain d, labeling gain, and post-image-processing residual noise. The key conclusion resulting from the study is that the coefficient of variation governing the randomness of the intensities and the deposition gain are the most important factors for determining whether a single-channel or dual-channel system provides superior classification, and the decision region in the alpha-d plane is approximately linear.

Algorithms↗
PubMed2004

Functional genomics of UV radiation responses in human cells.

The gene expression responses of MCF-7, a p53 wild-type (wt) human cell line, were monitored by cDNA microarray hybridization after exposure to different wavelengths of UV irradiation. Equitoxic doses of UVA, UVB, and UVC radiation were used to reduce survival to 37%. The effects of suramin, a signal pathway inhibitor, on the gene expression responses to the three UV wavelengths were also compared in this model system. UVB radiation triggered the broadest gene expression responses, and 172 genes were found to be consistently responsive in at least two-thirds of independent UVB experiments. These UVB radiation-responsive genes encode proteins with diverse cellular roles including cell cycle control, DNA repair, signaling, transcription, protein synthesis, protein degradation, and RNA metabolism. The set of UVB-responsive genes included most of the genes responding to an equitoxic dose of UVC radiation, plus additional genes that were not strongly triggered by UVC radiation. There was also some overlap with genes responding to an equitoxic dose of UVA radiation, although responses to this lower energy UV radiation were overall weaker. Signaling through growth factor receptors and other cytokine receptors was shown to have a major role in mediating UV radiation stress responses, as suramin, which inhibits such receptors, attenuated responses to UV radiation in nearly all the cases. Inhibition by suramin was greater for UVC than for UVB irradiation. This probably reflects the more prominent role in UVB damage response of signaling by reactive oxygen species, which would not be affected by suramin. Our results with suramin demonstrate the power of cDNA microarray hybridization to illuminate the global effects of a pharmacologic inhibitor on cell signaling.

Cell Line, Tumor↗
PubMed2004

Growing genetic regulatory networks from seed genes.

MOTIVATION: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. RESULTS: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. AVAILABILITY: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm

Algorithms↗
PubMed2004

External control in Markovian genetic regulatory networks: the imperfect information case.

Probabilistic Boolean Networks, which form a subclass of Markovian Genetic Regulatory Networks, have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. In an earlier paper, we introduced external control into Markovian Genetic Regulatory networks. More precisely, given a Markovian genetic regulatory network whose state transition probabilities depend on an external (control) variable, a Dynamic Programming-based procedure was developed by which one could choose the sequence of control actions that minimized a given performance index over a finite number of steps. The control algorithm of that paper, however, could be implemented only when one had perfect knowledge of the states of the Markov Chain. This paper presents a control strategy that can be implemented in the imperfect information case, and makes use of the available measurements which are assumed to be probabilistically related to the states of the underlying Markov Chain.

Algorithms↗
PubMed2003

Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease.

Multiple sclerosis (MS) and other T cell-mediated autoimmune diseases develop in individuals carrying a complex susceptibility trait, probably following exposure to various environmental triggers. Owing to the presumed weak influence of single genes on disease predisposition and the recognized genetic heterogeneity of autoimmune disorders in humans, candidate gene searches in MS have been difficult. In an attempt to identify molecular markers indicative of disease status rather than susceptibility genes for MS, we show that gene expression profiling of peripheral blood mononuclear cells by cDNA microarrays can distinguish MS patients from healthy controls. Our findings support the concept that the activation of autoreactive T cells is of primary importance for this complex organ-specific disorder and prompt further investigations on gene expression in peripheral blood cells aimed at characterizing disease phenotypes.

Adult↗
PubMed2003

Differential responses of stress genes to low dose-rate gamma irradiation.

In the past, most mechanistic studies of ionizing radiation response have employed very large doses, then extrapolated the results down to doses relevant to human exposure. It is becoming increasingly apparent, however, that this does not give an accurate or complete picture of the effects of most environmental exposures, which tend to be of low dose and protracted over time. We have initiated direct studies of low dose exposures, and using the relatively responsive ML-1 cell line, have shown that changes in gene expression can be triggered by doses of gamma-rays of 10 cGy and less in human cells. We have now extended these studies to investigate the effects on gene induction of reducing the rate of irradiation. In the ML-1 human myeloid leukemia cell line, we have found that reducing the dose rate over three orders of magnitude results in some protection against the induction of apoptosis, but still causes linear induction of the p53-regulated genes CDKN1A, GADD45A, and MDM2 between 2 and 50 cGy. Reducing the rate of exposure reduces the magnitude of induction of CDKN1A and GADD45A, but not the magnitude or duration of cell cycle delay. In contrast, MDM2 is induced to the same extent regardless of the rate of dose delivery. Microarray analysis has identified additional low dose-rate-inducible genes, and indicates the existence of two general classes of low dose-rate responders in ML-1. One group of genes is induced in a dose rate-dependent fashion, similar to GADD45A and CDKN1A. Functional annotation of this gene cluster indicates a preponderance of genes with known roles in apoptosis regulation. Similarly, a group of genes with dose rate-independent induction, such as seen for MDM2, was also identified. The majority of genes in this group are involved in cell cycle regulation. This apparent differential regulation of stress signaling pathways and outcomes in response to protracted radiation exposure has implications for carcinogenesis and risk assessment, and could not have been predicted from classical high dose studies.

Apoptosis↗
PubMed2002

Strong feature sets from small samples.

For small samples, classifier design algorithms typically suffer from overfitting. Given a set of features, a classifier must be designed and its error estimated. For small samples, an error estimator may be unbiased but, owing to a large variance, often give very optimistic estimates. This paper proposes mitigating the small-sample problem by designing classifiers from a probability distribution resulting from spreading the mass of the sample points to make classification more difficult, while maintaining sample geometry. The algorithm is parameterized by the variance of the spreading distribution. By increasing the spread, the algorithm finds gene sets whose classification accuracy remains strong relative to greater spreading of the sample. The error gives a measure of the strength of the feature set as a function of the spread. The algorithm yields feature sets that can distinguish the two classes, not only for the sample data, but for distributions spread beyond the sample data. For linear classifiers, the topic of the present paper, the classifiers are derived analytically from the model, thereby providing an enormous savings in computation time. The algorithm is applied to cancer classification via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the algorithm is used to find gene sets whose expressions can be used to classify BRCA1 and BRCA2 tumors.

Breast Neoplasms↗
PubMed2002

Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices.

MOTIVATION: The simplest level of statistical analysis of cancer associated gene expression matrices is aimed at finding consistently up- or down-regulated genes within a given set of tumor samples. Considering the high level of gene expression diversity detected in cancer, one needs to assess the probability that the consistent mis-regulation of a given gene is due to chance. Furthermore, it is important to determine the required sample number that will ensure the meaningful statistical analysis of massively parallel gene expression measurements. RESULTS: The probability of consistent mis-regulation is calculated in this paper for binarized gene expression data, using combinatorial considerations. For practical purposes, we also provide a set of accurate approximate formulas for determining the same probability in a computationally less intensive way. When the pool of mis-regulatable genes is restricted, the probability of consistent mis-regulation can be overestimated. We show, however, that this effect has little practical consequences for cancer associated gene expression measurements published in the literature. Finally, in order to aid experimental design, we have provided estimates on the required sample number that will ensure that the detected consistent mis-regulation is not due to chance. Our results suggest that less than 20 sufficiently diverse tumor samples may be enough to identify consistently mis-regulated genes in a statistically significant manner. AVAILABILITY: An implementation using Mathematica (tm) of the main equation of the paper, (4), is available at www.me.chalmers.se/~mwahde/bioinfo.html.

Algorithms↗
PubMed2002

Ratio statistics of gene expression levels and applications to microarray data analysis.

MOTIVATION: Expression-based analysis for large families of genes has recently become possible owing to the development of cDNA microarrays, which allow simultaneous measurement of transcript levels for thousands of genes. For each spot on a microarray, signals in two channels must be extracted from their backgrounds. This requires algorithms to extract signals arising from tagged mRNA hybridized to arrayed cDNA locations and algorithms to determine the significance of signal ratios. RESULTS: This paper focuses on estimation of signal ratios from the two channels, and the significance of those ratios. The key issue is the determination of whether a ratio is significantly high or low in order to conclude whether the gene is upregulated or downregulated. The paper builds on an earlier study that involved a hypothesis test based on a ratio statistic under the supposition that the measured fluorescent intensities subsequent to image processing can be assumed to reflect the signal intensities. Here, a refined hypothesis test is considered in which the measured intensities forming the ratio are assumed to be combinations of signal and background. The new method involves a signal-to-noise ratio, and for a high signal-to-noise ratio the new test reduces (with close approximation) to the original test. The effect of low signal-to-noise ratio on the ratio statistics constitutes the main theme of the paper. Finally, and in this vein, a quality metric is formulated for spots. This measure can be used to decide whether or not a spot ratio should be deleted, or to adjust various measurements to reflect confidence in the quality of the measurement. CONTACT: e-dougherty@tamu.edu

Cell Line↗
PubMed2002

Simulation of cDNA microarrays via a parameterized random signal model.

cDNA microarrays provide simultaneous expression measurements for thousands of genes that are the result of processing images to recover the average signal intensity from a spot composed of pixels covering the area upon which the cDNA detector has been put down. The accuracy of the signal measurement depends on using an appropriate algorithm to process the images. This includes determining spot locations and processing the data in such a way as to take into account spot geometry, background noise, and various kinds of noise that degrade the signal. This paper presents a stochastic model for microarray images. There are over 20 model parameters, each governed by a probability distribution, that control the signal intensity, spot geometry, spot drift, background effects, and the many kinds of noise that affect microarray images owing to the manner in which they are formed. The model can be used to analyze the performance of image algorithms designed to measure the true signal intensity because the ground truth (signal intensity) for each spot is known. The levels of foreground noise, background noise, and spot distortion can be set, and algorithms can be evaluated under varying conditions.

Algorithms↗
PubMedDate not supplied

Noise factor analysis for cDNA microarrays.

A microarray-image model is used that takes into account many factors, including spot morphology, signal strength, background fluorescent noise, and shape and surface degradation. The model yields synthetic images whose appearance and quality reflect that of real microarray images. The model is used to link noise factors to the fidelity of signal extraction with respect to a standard image-extraction algorithm. Of particular interest is the identification of the noise factors and their interactions that significantly degrade the ability to accurately detect the true gene-expression signal. This study uses statistical criteria in conjunction with the simulation of various noise conditions to better understand the noise influence on signal extraction for cDNA microarray images. It proposes a paradigm that is implemented in software. It specifically considers certain kinds of noise in the noise model and sets these at certain levels; however, one can choose other types of noise or use different noise levels. In sum, it develops a statistical package that can work in conjunction with the existing image simulation toolbox.

Algorithms↗