Dazhang Gu

A Bioinformatics Approach to the Identification, Classification, and Analysis of Hydroxyproline-Rich Glycoproteins

Hydroxyproline-rich glycoproteins (HRGPs) are a superfamily of plant cell wall proteins that function in diverse aspects of plant growth and development. This superfamily consists of three members: hyperglycosylated arabinogalactan proteins (AGPs), moderately glycosylated extensins (EXTs), and lightly glycosylated proline-rich proteins (PRPs). Hybrid and chimeric versions of HRGP molecules also exist. In order to “mine” genomic databases for HRGPs and to facilitate and guide research in the field, the BIO OHIO software program was developed that identifies and classifies AGPs, EXTs, PRPs, hybrid HRGPs, and chimeric HRGPs from proteins predicted from DNA sequence data. This bioinformatics program is based on searching for biased amino acid compositions and for particular protein motifs associated with known HRGPs. HRGPs identified by the program are subsequently analyzed to elucidate the following: (1) repeating amino acid sequences, (2) signal peptide and glycosylphosphatidylinositol lipid anchor addition sequences, (3) similar HRGPs via Basic Local Alignment Search Tool, (4) expression patterns of their genes, (5) other HRGPs, glycosyl transferase, prolyl 4-hydroxylase, and peroxidase genes coexpressed with their genes, and (6) gene structure and whether genetic mutants exist in their genes. The program was used to identify and classify 166 HRGPs from Arabidopsis (Arabidopsis thaliana) as follows: 85 AGPs (including classical AGPs, lysine-rich AGPs, arabinogalactan peptides, fasciclin-like AGPs, plastocyanin AGPs, and other chimeric AGPs), 59 EXTs (including SP5 EXTs, SP5/SP4 EXTs, SP4 EXTs, SP4/SP3 EXTs, a SP3 EXT, “short” EXTs, leucine-rich repeat-EXTs, proline-rich extensin-like receptor kinases, and other chimeric EXTs), 18 PRPs (including PRPs and chimeric PRPs), and AGP/EXT hybrid HRGPs.

A predictive, decentralized load balancing approach

The growth of load balancing system raises the issue of scalability, and decentralized load balancing architecture has been proposed to address this issue. In this paper, we investigate how a load balancing architecture can be built on decentralized policies based on CORBA and enhanced by predictive algorithm. The L/sub 2/E predictive filtering model was used to supply workstations with robust cluster load information, which allows them to make more accurate independent allocation decisions. Experimental results showed that our decentralized load balancing approach was able to suppress thrashing and oscillations compared to other load monitoring and prediction techniques, and it was able to achieve a highly balanced system than Sun Grid Engine.

Robust Task Allocation for Dynamic Distributed Real-Time Systems Subject to Multiple Environmental Parameters

Some distributed real-time systems interact with external environments that change dynamically, and it is necessary to take the external variables into account when performing task allocation. We developed an approximation algorithm for task allocation, and it finds allocations that are maximally robust against dynamic changes in multiple external variables. Such an algorithm will help to reduce expensive reallocations triggered by changes in unpredictable environments. The algorithm has a polynomial running time, and its robustness optimality is given by an approximation ratio, which equals 2.41 asymptotically, when workloads are large and workload independent utilization of tasks is insignificant

Characterizing robustness in dynamic real-time systems

The problem of robust task allocation is motivated by the need to deploy real-time systems in dynamic operational environments. Existing robust allocation approaches employ coarse robustness metrics, which can result in poor allocations. This paper proposes a metric that accurately characterizes a systems robustness within feasible allocation regions. An allocation algorithm is provided to find allocations that are both feasible and robust; the robustness as measured by the metric is shown to have theoretical bounds. Experiments demonstrate that the algorithm produces good and scalable performance compared with several heuristic algorithms.

Stable allocations in distributed real-time systems with multiple environmental parameters and replicable applications

This paper extends the previous work on the maximal allowable workload (MAW) problem [D. Juedes et al., (2004)] by investigating a resource allocation problem for distributed real-time systems that contain replicable applications. The systems may use multiple resources of a single type and be affected by multiple environmental factors. The approach searches for a feasible allocation that maximizes a user defined metric of stability. Several algorithms were developed and experiments were conducted to demonstrate the relative strength of these algorithms. The results showed that simulated annealing provides results that are the closest to the optimal for maximizing environmental parameter settings. In addition modified greedy first fit is shown to be the best performing algorithm for finding feasible allocations.

Construction of Genomic Regulatory Encyclopedias: Strategies and Case Studies

Encyclopedias of regulatory genomic elements provide a foundation for research in areas such as disease diagnosis, disease treatment, and crop enhancement. The construction of complete encyclopedias of organism-specific genomic elements involved in gene regulation remains a significant challenge. To address this problem, the authors present novel bioinformatics strategies for exploring the word landscapes of putative regulatory regions of genomes. The methods are incorporated into the WordSeeker software tool, which is available at http://word-seeker.org. The effectiveness of these strategies is demonstrated through several case studies.

Robust Allocation and Scheduling Heuristics for Dynamic, Distributed Real-Time Systems

A challenge facing real-time computing is the need to deploy real-time systems in dynamic operational environments. The systems have explicit deadline requirements, but their execution times are often affected by unpredictable environmental inputs that cannot be known a priori and have no worst-case estimates. As a result, traditional real-time task allocation and scheduling techniques do not apply.

SiteSeeker - A Motif Discovery Tool

In this paper we describe some utilizing conditions of a recently published tool that offers two basic functions for the classical problem of discovering motifs in a set of promoter sequences. For the first it is assumed that not necessarily all of the sequences possess a common motif of given length l. In this case, CHECKPROMOTER allows an exact identification of maximal subsets of related promoters. The purpose of this program is to recognize putatively co-regulated genes. The second, CHECKMOTIF, solves the problem of checking if the given promoters have a common motif. It uses a fast approximation algorithm for which we were able to derive non-trivial low performance bounds (defined as the ratio of Hamming distance of the obtained solution to that of a theoretically best solution) for the computed outputs. Both programs use a novel weighted Hamming distance paradigm for evaluating the similarity of sets of l-mers, and we are able to compute performance bounds for the proposed motifs. A set of At promoters were used as a benchmark for a comparative test against five known tools. It could be verified that SiteSeeker significantly outperformed these tools.

Approximation algorithm for periodic real-time tasks with workload-dependent running-time functions

This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times (WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size (workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks (e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically. Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems.