Transcriptional Regulation of Chicken Apolipoprotein A-I Gene Expression

Deregulation of a set of critical cellular genes has long been speculated as a cause of the oncogenic transformation induced by v-Jun oncoprotein. In the past several years, extensive efforts have been made to identify such genes. Several target genes have been identified that are specifically associated with the v-Jun induced transformation phenotype in chicken embryo fibroblasts (CEF). We have undertaken the objective to identify and characterize the genes that become deregulated in response to, or as a consequence of, Jun-induced transformation. By exploiting the difference in oncogenic potential between v-Jun and c-Jun in CEF, the chicken apolipoprotein A-I (apoA-I) gene has been identified in our laboratory as one of the target genes whose expression is repressed in response to v-Jun overexpression in CEF. The overall objective of this study is to investigate the underlying molecular mechanisms by which the apoA-I gene expression is regulated, especially by the v-Jun oncoprotein. In this study, we mapped the v-Jun responsive elements within nucleotides −311 to +19 upstream of the apoA-I transcription start site. Biochemical analysis of functional domains of v-Jun indicates that DNA binding specificity of vJun and its ability to heterodimerize with diverse partners are absolutely required for repression of apoA-I transcription. Further, the sequences between amino acid residues 108 to 128 in the amino terminus of v-Jun proteins that contains the acidic region III of its transactivation domain are important for its repressor activity. In addition, an enhancer located between −6.8 kb to −6.0 kb upstream of the transcription start site of the apoA-I gene was identified and characterized. The enhancer is capable of stimulating transcription from the apoA-I promoter in a distance- and orientation-independent manner and is CEF-specific. Sequence information of the enhancer revealed that it is 791-by long and contains many putative binding sites for known transcription factors. Deletion analysis of the enhancer activity indicates that there are many modules, both positive and negative, located within the 791-by enhancer region. Nuclear transcription factors Sp1, C/EBP, HNF-3, and an unknown factor were found that might interact with the cis-acting elements of the enhancer

An Object-Oriented Algorithmic Laboratory for Ordering Sparse Matrices

We focus on two known NP-hard problems that have applications in sparse matrix computations: the envelope/wavefront reduction problem and the fill reduction problem. Envelope/wavefront reducing orderings have a wide range of applications including profile and frontal solvers, incomplete factorization preconditioning, graph reordering for cache performance, gene sequencing, and spatial databases. Fill reducing orderings are generally limited to—but an inextricable part of—sparse matrix factorization. Our major contribution to this field is the design of new and improved heuristics for these NP-hard problems and their efficient implementation in a robust, cross-platform, object-oriented software package. In this body of research, we (1) examine current ordering algorithms, analyze their asymptotic complexity, and characterize their behavior in model problems, (2) introduce new and improved algorithms that address deficiencies found in previous heuristics, (3) implement an object-oriented library of these algorithms in a robust, modular fashion without significant loss of efficiency, and (4) extend our algorithms and software to address both generalized and constrained problems. We stress that the major contribution is the algorithms and the implementation; the whole being greater than the sum of its parts. The initial motivation for implementing our algorithms in object-oriented software was to manage the inherent complexity. During our research came the realization that the object-oriented implementation enabled new possibilities for augmented algorithms that would not have been as natural to generalize from a procedural implementation. Some extensions are constructed from a family of related algorithmic components, thereby creating a poly-algorithm that can adapt its strategy to the properties of the specific problem instance dynamically. Other algorithms are tailored for special constraints by aggregating algorithmic components and having them collaboratively generate the global ordering. Our software laboratory, “Spindle,” implements state-of-the-art ordering algorithms for sparse matrices and graphs. We have used it to examine and augment the behavior of existing algorithms and test new ones. Its 40,000+ lines of C++ code includes a base library test drivers, sample applications, and interfaces to C, C++, Matlab, and PETSc. Spindle is freely available and can be built on a variety of UNIX platforms as well as WindowsNT

Evaluating Zoltan for Static Load Balancing on BlueGene Architectures

The purpose of this TechBase was to evaluate the Zoltan load-balancing library from Sandia National Laboratories as a possible replacement for ParMetis, which had been the load balancer of choice for nearly a decade but does not scale to the full 64,000 processors of BlueGene/L. This evaluation was successful in producing a clear result, but the result was unfortunately negative. Although Zoltan presents a collection load-balancing algorithms, none were able to meet or exceed the combined scalability and quality of ParMetis on representative datasets

How to Implement a Protocol for Babel RMI

RMI support in Babel has two main goals: transparency & flexibility. Transparency meaning that the new RMI features are entirely transparent to existing Babelized code; flexibility meaning the RMI capability should also be flexible enough to support a variety of RMI transport implementations. Babel RMI is a big success in both areas. Babel RMI is completely transparent to already Babelized implementation code, allowing painless upgrade, and only very minor setup changes are required in client code to take advantage of RMI. The Babel RMI transport mechanism is also extremely flexible. Any protocol that implements Babel's minimal, but complete, interface may be used as a Babel RMI protocol. The Babel RMI API allows users to select the best protocol and connection model for their application, whether that means a WebServices-like client-server model for use over a WAP, or a faster binary peer-to-peer protocol for use on different nodes in a leadership-class supercomputer. Users can even change protocols without recompiling their code. The goal of this paper is to give network researchers and protocol implementors the information they need to develop new protocols for Babel RMI. This paper will cover both the high-level interfaces in the Babel RMI API, and the low level details about how Babel RMI handles RMI objects

Scientific Software Component Technology

We are developing new software component technology for high-performance parallel scientific computing to address issues of complexity, re-use, and interoperability for laboratory software. Component technology enables cross-project code re-use, reduces software development costs, and provides additional simulation capabilities for massively parallel laboratory application codes. The success of our approach will be measured by its impact on DOE mathematical and scientific software efforts. Thus, we are collaborating closely with library developers and application scientists in the Common Component Architecture forum, the Equation Solver Interface forum, and other DOE mathematical software groups to gather requirements, write and adopt a variety of design specifications, and develop demonstration projects to validate our approach. Numerical simulation is essential to the science mission at the laboratory. However, it is becoming increasingly difficult to manage the complexity of modern simulation software. Computational scientists develop complex, three-dimensional, massively parallel, full-physics simulations that require the integration of diverse software packages written by outside development teams. Currently, the integration of a new software package, such as a new linear solver library, can require several months of effort. Current industry component technologies such as CORBA, JavaBeans, and COM have all been used successfully in the business domain to reduce software development costs and increase software quality. However, these existing industry component infrastructures will not scale to support massively parallel applications in science and engineering. In particular, they do not address issues related to high-performance parallel computing on ASCI-class machines, such as fast in-process connections between components, language interoperability for scientific languages such as Fortran, parallel data redistribution between components, and massively parallel components. While industrial component systems do not directly address scientific computing issues, we leverage existing industry technologies and design concepts whenever possible

Unravelling small world networks

New classes of random graphs have recently been shown to exhibit the small world phenomenon - they are clustered like regular lattices and yet have small average pathlengths like traditional random graphs. Small world behaviour has been observed in a number of real life networks, and hence these random graphs represent a useful modelling tool. In particular, Grindrod [Phys. Rev. E 66 (2002) 066702-1] has proposed a class of range dependent random graphs for modelling proteome networks in bioinformatics. A property of these graphs is that, when suitably ordered, most edges in the graph are short-range, in the sense that they connect near-neighbours, and relatively few are long-range. Grindrod also looked at an inverse problem - given a graph that is known to be an instance of a range dependent random graph, but with vertices in arbitrary order, can we reorder the vertices so that the short-range/long-range connectivity structure is apparent? When the graph is viewed in terms of its adjacency matrix, this becomes a problem in sparse matrix theory: find a symmetric row/column reordering that places most nonzeros close to the diagonal. Algorithms of this general nature have been proposed for other purposes, most notably for reordering to reduce fill-in and for clustering large data sets. Here, we investigate their use in the small world reordering problem. Our numerical results suggest that a spectral reordering algorithm is extremely promising, and we give some theoretical justification for this observation via the maximum likelihood principle

How the Common Component Architecture Advances Compuational Science

Computational chemists are using Common Component Architecture (CCA) technology to increase the parallel scalability of their application ten-fold. Combustion researchers are publishing science faster because the CCA manages software complexity for them. Both the solver and meshing communities in SciDAC are converging on community interface standards as a direct response to the novel level of interoperability that CCA presents. Yet, there is much more to do before component technology becomes mainstream computational science. This paper highlights the impact that the CCA has made on scientific applications, conveys some lessons learned from five years of the SciDAC program, and previews where applications could go with the additional capabilities that the CCA has planned for SciDAC 2

A Component Architecture for High-Performance Scientific Computing

The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry