Ilya Gluhovsky

Bio: Ilya Gluhovsky is an academic researcher from Sun Microsystems Laboratories. The author has contributed to research in topics: Markov chain & Cache. The author has an hindex of 6, co-authored 13 publications receiving 263 citations.

Steady-state analysis of genetic regulatory networks modelled by probabilistic boolean networks.

Abstract: Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes.

Modeling overlapping of memory references in a queueing system model

Abstract: One embodiment of the present invention provides a system that facilitates modeling the effects of overlapping of memory references in a queueing system model. The system receives a memory reference during execution of a queueing system model. Upon receiving the memory reference, the system determines if the memory reference generates a cache miss. If so, the system models the cache miss in a manner that accounts for possible overlapping of the cache miss with other memory references and other processor operations.

Comprehensive multiprocessor cache miss rate generation using multivariate models

Abstract: This article presents a technique for taking a sparse set of cache simulation data and fitting a multivariate model to fill in the missing points over a broad region of cache configurations. We extend previous work by its applicability to multiple miss rate components and its ability to model a wide range of cache parameters, including size, associativity and sharing. Miss rate models are useful for broad design exploration in which many cache configurations cannot be simulated directly due to limitations of trace collection setups or available resources. We show the effectiveness of the technique by applying it to two commercial workloads and presenting miss rate data for a broad design space with cache size, associativity, sharing and number of processors as variables. The fitted data match the simulation data very well. The various curves show how a miss rate model is useful for not only estimating the performance of specific configurations, but also for providing insight into miss rate trends. Furthermore, this modeling methodology is robust in the presence of corrupted simulation data and variations in simulation data from multiple sources.

Constrained Multivariate Extrapolation Models With Application to Computer Cache Rates

Abstract: In this article we propose an approach to building multivariate regression models for prediction beyond the range of the data. The extrapolation model attempts to accurately estimate the high-level trend of the data, which can be extended in a natural way. The constraints of monotonicity and convexity/concavity play an important role in restricting the choice of the high level-trend, which otherwise would remain rather arbitrary. Our extrapolation model incorporates these constraints in multiple dimensions. We describe the trend as a nonnegative linear combination of twice-integrated multivariate B-splines and their variations. The specific basis functions in our approach are chosen so that any such combination is a plausible a priori model. As a result, basis function coefficients can be optimized to best fit the data without losing control over the high-level trend of the extrapolation model. Our approach also allows the use of standard model selection techniques. We illustrate this by applying cross-va...

Modeling and simulation of genetic regulatory systems: a literature review.

Abstract: The spatiotemporal expression of genes in an organism is determined by regulatory systems that involve a large number of genes connected through a complex network of interactions. As an intuitive understanding of the behavior of these systems is hard to obtain, computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This report reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, ordinary and partial differential equations, stochastic equations, Boolean networks and their generalizations, qualitative differential equations, and rule-based formalisms. In addition, the report discusses how these formalisms have been used in the modeling and simulation of regulatory systems.

Modelling and analysis of gene regulatory networks

Abstract: Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.

ESTs, cDNA microarrays, and gene expression profiling: tools for dissecting plant physiology and development.

Abstract: Gene expression profiling holds tremendous promise for dissecting the regulatory mechanisms and transcriptional networks that underlie biological processes. Here we provide details of approaches used by others and ourselves for gene expression profiling in plants with emphasis on cDNA microarrays and discussion of both experimental design and downstream analysis. We focus on methods and techniques emphasizing fabrication of cDNA microarrays, fluorescent labeling, cDNA hybridization, experimental design, and data processing. We include specific examples that demonstrate how this technology can be used to further our understanding of plant physiology and development (specifically fruit development and ripening) and for comparative genomics by comparing transcriptome activity in tomato and pepper fruit.

Methods and apparatus of stacking DRAMs

Abstract: Large capacity memory systems are constructed using stacked memory integrated circuits or chips. The stacked memory chips are constructed in such a way that eliminates problems such as signal integrity while still meeting current and future memory standards.

Semiparametric regression during 2003–2007

Abstract: Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.