PubMed Health⌕ Search

Biomedical subjects

Ilya Shmulevich

Publications and source records attributed to Ilya Shmulevich.

11 recordsLinked to original sources
PubMed2003

The role of certain Post classes in Boolean network models of genetic networks.

A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks.

Computational Biology↗
PubMed2003

Data extraction from composite oligonucleotide microarrays.

Microarray or DNA chip technology is revolutionizing biology by empowering researchers in the collection of broad-scope gene information. It is well known that microarray-based measurements exhibit a substantial amount of variability due to a number of possible sources, ranging from hybridization conditions to image capture and analysis. In order to make reliable inferences and carry out quantitative analysis with microarray data, it is generally advisable to have more than one measurement of each gene. The availability of both between-array and within-array replicate measurements is essential for this purpose. Although statistical considerations call for increasing the number of replicates of both types, the latter is particularly challenging in practice due to a number of limiting factors, especially for in-house spotting facilities. We propose a novel approach to design so-called composite microarrays, which allow more replicates to be obtained without increasing the number of printed spots.

Gene Expression Profiling↗
PubMed2003

Microarray reveals differences in both tumors and vascular specific gene expression in de novo CD5+ and CD5- diffuse large B-cell lymphomas.

Malignant lymphoma is a heterogeneous disease with different clinical features. Among diffuse large B-cell lymphomas (DLBCLs), a unique subtype has been identified recently based on cell surface marker CD5 and clinicopathological features. These de novo CD5(+) DLBCLs account for approximately 10% of all of the DLBCLs and have poorer prognosis. To additionally understand this subtype of DLBCLs at the molecular level and to find genes that are differentially expressed in de novo CD5(+) DLBCLs, CD5(-) DLBCLs, and mantle cell lymphomas, which also have poor prognosis, we performed gene expression profiling using cDNA microarray technology. Data from a total of 9 samples of CD5(-) DLBCLs, 11 samples of de novo CD5(+) DLBCLs, and 10 samples of mantle cell lymphomas were acquired. A series of genes were identified that distinguish these three types of lymphomas. Among DLBCL cases, integrin beta1 and/or CD36 adhesion molecules were overexpressed in most cases of CD5(+) DLBCL. An immunohistochemical confirmation study revealed that integrin beta1 was expressed on lymphoma cells, which may account for the high extranodal involvement and poor prognosis of CD5(+) DLBCLs. In contrast, CD36 was overexpressed on vascular endothelia in CD5(+) DLBCLs, although there was no difference in vascularity detected by von Wilbrand factor antibody between CD5(+) and CD5(-) DLBCLs. Those results suggest that CD5(+) and CD5(-) DLBCLs have different gene expression signatures in both tumor cells and their vascular systems.

Aged↗
PubMed2003

Identification of signature genes by microarray for acute myeloid leukemia without maturation and acute promyelocytic leukemia with t(15;17)(q22;q12)(PML/RARalpha).

Acute myeloid leukemia (AML) has distinct subgroups characterized by different maturation and specific chromosomal translocation. In order to gain insight into the gene expression activities in AML, we carried out a gene expression profiling study with 21 AML samples using cDNA microarrays, focusing on acute promyelocytic leukemia with specific translocation t(15;17)(q22;q12) [French-American-British or FAB-M3 with t(15;17)] and AML without maturation (FAB-M1) characterized by morphologically and phenotypically immature AML blasts and no recurrent chromosomal abnormalities. Using a multivariate sigma-classifier algorithm, we identified 33 strong feature genes that distinguish FAB-M3 with t(15;17) from other AML samples, and 24 strong feature genes that classify FAB-M1. A direct comparison between FAB-M3 with t(15;17) and FAB-M1 led to selection of 13 strong feature genes. Those genes include some known to be related to leukemogenesis and cell differentiation. RIN1, a gene in the ras pathway, was up-regulated in FAB-M3 with t(15;17). Growth factor-binding protein 2 gene was down-regulated in FAB-M1. Huntingtin gene was up-regulated in FAB-M1. Others include syndecan 4, interleukin-2 receptor beta, folate receptor beta, low affinity immunoglobulin gamma, Fc receptor IIC precursor, insulin-like growth factor binding protein 2, and myeloperoxidase, which are involved in cell differentiation. Overexpression of myeloperoxidase in FAB-M3 cells with t(15;17) compared to FAB-M1 cells is consistent with the conventional cytochemical staining pattern. Thus, the study revealed that a morphologically-defined FAB-M1 subtype has a distinct gene expression signature that contributes to its cell differentiation and proliferation as well as FAB-M3 with a recurrent cytogenetic abnormality t(15;17)(q22;q12).

Algorithms↗
PubMed2002

Tumor specific gene expression profiles in human leiomyosarcoma: an evaluation of intratumor heterogeneity.

Leiomyosarcomas are malignant smooth muscle tumors characterized by a spectrum of histopathologic features and clinical behavior. Gene expression profiling of leiomyosarcomas may identify differential gene signatures that may allow for the clinical stratification of the tumors. Typically, surgical specimens from these tumors are large and manifest a variegated macroscopic appearance. Because of their large size at the time of diagnosis, sufficient tissue is available for regional and clonal heterogeneity assessment. However, if the gene expression profiles of samples taken from different locations in the tumors are drastically different, biologic classification on the basis of random sample analysis may not be adequate. Therefore, to assess intertumor and intratumor heterogeneity, the authors performed a gene expression study using leiomyosarcoma specimens from three excised sarcomas from an equal number of different patients. Comparisons between tumor and normal samples from the three patients as well as between carefully mapped peripheral and core specimens from the same tumor (excised from one of the patients), were performed. Analysis of the expression profiles demonstrated minimal intratumor variations compared with intertissue variations, indicating homogeneous tumor specific gene expression profiles. The authors also identified genes that are expressed differentially in tumor and normal tissue.

Biomarkers, Tumor↗
PubMed2002

Gene perturbation and intervention in probabilistic Boolean networks.

MOTIVATION: A major objective of gene regulatory network modeling, in addition to gaining a deeper understanding of genetic regulation and control, is the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. We consider the general question of the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene perturbation as well as intervention in order to elicit desired network behavior. RESULTS: Using a recently introduced class of models, called Probabilistic Boolean Networks (PBNs), this paper develops a model for random gene perturbations and derives an explicit formula for the transition probabilities in the new PBN model. This result provides a building block for performing simulations and deriving other results concerning network dynamics. An example is provided to show how the gene perturbation model can be used to compute long-term influences of genes on other genes. Following this, the problem of intervention is addressed via the development of several computational tools based on first-passage times in Markov chains. The consequence is a methodology for finding the best gene with which to intervene in order to most likely achieve desirable network behavior. The ideas are illustrated with several examples in which the goal is to induce the network to transition into a desired state, or set of states. The corresponding issue of avoiding undesirable states is also addressed. Finally, the paper turns to the important problem of assessing the effect of gene perturbations on long-run network behavior. A bound on the steady-state probabilities is derived in terms of the perturbation probability. The result demonstrates that states of the network that are more 'easily reachable' from other states are more stable in the presence of gene perturbations. Consequently, these are hypothesized to correspond to cellular functional states. AVAILABILITY: A library of functions written in MATLAB for simulating PBNs, constructing state-transition matrices, computing steady-state distributions, computing influences, modeling random gene perturbations, and finding optimal intervention targets, as described in this paper, is available on request from is@ieee.org.

Chromosome Mapping↗
PubMed2002

Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks.

MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.

Cell Cycle↗
PubMed2002

Binary analysis and optimization-based normalization of gene expression data.

MOTIVATION: Most approaches to gene expression analysis use real-valued expression data, produced by high-throughput screening technologies, such as microarrays. Often, some measure of similarity must be computed in order to extract meaningful information from the observed data. The choice of this similarity measure frequently has a profound effect on the results of the analysis, yet no standards exist to guide the researcher. RESULTS: To address this issue, we propose to analyse gene expression data entirely in the binary domain. The natural measure of similarity becomes the Hamming distance and reflects the notion of similarity used by biologists. We also develop a novel data-dependent optimization-based method, based on Genetic Algorithms (GAs), for normalizing gene expression data. This is a necessary step before quantizing gene expression data into the binary domain and generally, for comparing data between different arrays. We then present an algorithm for binarizing gene expression data and illustrate the use of the above methods on two different sets of data. Using Multidimensional Scaling, we show that a reasonable degree of separation between different tumor types in each data set can be achieved by working solely in the binary domain. The binary approach offers several advantages, such as noise resilience and computational efficiency, making it a viable approach to extracting meaningful biological information from gene expression data.

Algorithms↗
PubMed2002

An inhibition-based stochastic countable-time decision model.

A new stochastic model to account for reaction-time fluctuation in prolonged work tasks is presented. Transition probabilities from work periods to distraction periods and vice versa are dependent on inhibition, which increases during work and decreases during distractions. The model presented here differs from all other inhibition-based models in that transitions can take place only at certain random points in time, and is referred to as a countable-time decision model. It is argued that the proposed model is a more plausible alternative to other existing inhibition-based models, while at the same time being highly flexible in that it is able to approximate other models arbitrarily well. This model is compared to an existing inhibition-based continuous-time decision model and the probability distribution functions for work and distraction periods are derived.

Decision Making↗
PubMed2002

Identification of combination gene sets for glioma classification.

One goal for the gene expression profiling of cancer tissues is to identify signature genes that robustly distinguish different types or grades of tumors. Such signature genes would ideally provide a molecular basis for classification and also yield insight into the molecular events underlying different cancer phenotypes. This study applies a recently developed algorithm to identify not only single classifier genes but also gene sets (combinations) for use as glioma classifiers. Classifier genes identified by this algorithm are shown to be strong features by conservatively and collectively considering the misclassification errors of the feature sets. Applying this approach to a test set of 25 patients, we have identified the best single genes and two- to three-gene combinations for distinguishing four types of glioma: (a) oligodendroglioma; (b) anaplastic oligodendroglioma; (c) anaplastic astrocytoma; and (d) glioblastoma multiforme. Some of the identified genes, such as insulin-like growth factor-binding protein 2, have been confirmed to be associated with one of the tumor types. Using combinations of genes, the classification error rate can be significantly lowered. In many instances, neither of the individual genes of a two-gene set performs well as an accurate classifier, but the combination of the two genes forms a robust classifier with a small error rate. Two-gene and three-gene combinations thus provide robust classifiers possessing the potential to translate expression microarray results into diagnostic histopathological assays for clinical utilization.

Algorithms↗