Modeling and Solving a Resource Allocation Problem with Soft Constraint Techniques

We study a resource allocation problem, which is a central piece of a real-world crew scheduling problem. We first formulate the problem as a hybrid soft constraint satisfaction and optimization problem and show that its worst-case complexity is NP-complete. We then propose and study a set of decision and optimization modeling schemes for the problem. We consider the expressiveness of these modeling schemes for the problem. We consider the expressiveness of these modeling methods. Specifically, we experimentally investigate how these modeling schemes interplay with the best existing systematic search and local search methods. Our experimental results show that soft constraint techniques can be effective on large resource allocation problem instances, and an optimization approach is more efficient than a model checking approach based on decision models

Phase Transitions and Backbones of Constraint Minimization Problems

Many real-world problems involve constraints that cannot be all satisfied. The goal toward an overconstrained problem is to find solutions minimizing the total number of constraints violated. We call such a problem constraint minimization problem (CMP). We study the behavior of the phase transitions and backbones of CMP. We first investigate the relationship between the phase transitions of Boolean satisfiability, or precisely 3-SAT (a well-studied NP-complete decision problem), and the phase transitions of MAX 3-SAT (an NP-hard optimization problem). To bridge the gap between the easy-hard-easy phase transitions of 3-SAT, in which solutions of bounded quality, e.g., solutions with at most a constant number of constraints violated, are sufficient. We show that phase transitions are persistent in bounded 3-SAT and are similar to that of 3-SAT. We then study backbones of MAX 3-SAT, which are critically constrained variables that have fixed values in all optimal solutions. Our experimental results show that backbones of MAX 3-SAT emerge abruptly and experience sharp transitions from nonexistence when underconstrained to almost complete when overconstrained. More interestingly, the phase transitions of MAX 3-SAT backbones surprisingly concur with the phase transitions of satisfiability of 3-SAT. Specifically, the backbone of MAX 3-SAT with size 0.5 approximately collocates with the 0.5 satisfiablity of 3-SAT, and hte backbone and satisfiability seems to follow a linear correlation near this 0.5-0.5 collocation

A meta-Analysis revealed insights into the sources, conservation and impact of microRNA 5′-isoforms in four model species

MicroRNA (miRNA) 5′-isoforms, or 5′-isomiRs, are small-RNA species that originate from the same genomic loci as the major miRNAs with their 5′ ends shifted from the 5′ ends of the miRNAs by a few nucleotides. Although 5′-isomiRs have been reported, their origins, properties and potential functions remain to be examined. We systematically studied 5′-isomiRs in human, mouse, fruitfly and worm by analysing a large collection of small non-coding RNA and mRNA profiling data. The results revealed a broad existence of 5′-isomiRs in the four species, many of which were conserved and could arise from genomic loci of canonical and non-canonical miRNAs. The well-conserved 5′-isomiRs have several features, including a preference of the 3p over the 5p arms of hairpins of conserved mammalian miRNAs, altered 5′-isomiRs across species and across tissues, and association with structural variations of miRNA hairpins. Importantly, 5′-isomiRs and their major miRNAs may have different mRNA targets and thus potentially play distinct roles of gene regulation, as shown by an integrative analysis combining miRNA and mRNA profiling data from psoriatic and normal human skin and from murine miRNA knockout assays. Indeed, 18 5′-isomiRs had aberrant expression in psoriatic human skin, suggesting their potential function in psoriasis pathogenesis. The results of the current study deepened our understanding of the diversity and conservation of miRNAs, their plasticity in gene regulation and potential broad function in complex diseases

Discovering weak community structures in large biological networks

Identifying intrinsic structures in large networks is a fundamental problem in many fields, such as biology, engineering and social sciences. Motivated by biology applications, in this paper we are concerned with identifying community structures, which are densely connected sub-graphs, in large biological networks. We address several critical issues for finding community structures. First, biological networks directly constructed from experimental data often contain spurious edges and may also miss genuine connections. As a result, community structures in biological networks are often weak. We introduce simple operations to capture local neighborhood structures for identifying weak communities. Second, we consider the issue of automatically determining the most appropriate number of communities, a crucial problem for all clustering methods. This requires to properly evaluate the quality of community structures. We extend an existing work of a modularity function for evaluating community structures to weighted graphs. Third, we propose a spectral clustering algorithm to optimize the modularity function, and a greedy partitioning method to approximate the first algorithm with much reduced running time. We evaluate our methods on many networks of known structures, and apply them to three real-world networks that have different types of network communities: a yeast protein-protein interaction network, a co-expression network of yeast cell-cycle genes, and a collaboration network of bioinformaticians. The results show that our methods can find superb community structures and the correct numbers of communities. Our results reveal several interesting network structures that have not been reported previously

Discovering Functional Modules by Clustering Gene Co-expression Networks

Identification of groups of functionally related genes from high throughput gene expression data is an important step towards elucidating gene functions at a global scale. Most existing approaches treat gene expression data as points in a metric space, and apply conventional clustering algorithms to identify sets of genes that are close to each other in the metric space. However, they usually ignore the topology of the underlying biological networks. In this paper, we propose a network-based clustering method that is biologically more realistic. Given a gene expression data set, we apply a rank-based transformation to obtain a sparse co-expression network, and use a novel spectral clustering algorithm to identify natural community structures in the network, which correspond to gene functional modules. We have tested the method on two large-scale gene expression data sets in yeast and Arabidopsis, respectively. The results show that the clusters identified by our method on these datasets are functionally richer and more coherent than the clusters from the standard k-means clustering algorithm

An Iterative Loop Matching Approach to the Prediction of RNA Secondary Structures with Pseudoknots

Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to the difficulty in modeling and complexity in computing. Although several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching (Tabaska et. al, Bioinformatics, 14:691-9, 1998), an algorithm for pseudoknot prediction with comparative analysis, suffers from low prediction accuracy in many cases. Here we present an algorithm, iterative loop matching, for predict-ing RNA secondary structures including pseudoknots reliably and efficiently. The method can utilize either thermodynamic or comparative information or both, thus is able to predict for both aligned sequences and individual sequences. Results: We have tested the algorithm on a number of RNA families, including both structures with and without pseudoknots. Using 8–12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots. Furthermore, it produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that our algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, our algorithm has high prediction accuracy on individual sequences, comparable to the PKNOTS algorithm (Rivas & Eddy, J Mol Biol, 285:2053-68, 1999), while using much less computational resources. Availability: The program has been implemented in ANSI C and is freely available for academic use at http://www.cse.wustl.edu/˜zhang/projects/rna/ilm/

DNA repair in incipient Alzheimer\u27s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder currently with no cure. Understanding the pathogenesis in the early stages of late-onset AD can help gain important mechanistic insights into this disease as well as aid in effective drug development. The analysis of incipient AD is steeped in difficulties due to its slight pathological and genetic differences from normal ageing. The difficulty also lies in the choice of analysis techniques as statistical power to analyse incipient AD with a small sample size, as is common in pilot studies, can be low if the proper analytical tool is not employed. In this study, we propose the use of a new method of significant genes selection, multiple linear regression, which uses the cognitive index (MiniMental Status Examination (MMSE)) and pathological characteristic (neurofibrillary tangles (NFT)), along with gene expression profiles, to select genes. The data consists of 7 incipient AD affected subjects and 9 age-matched normal controls. The analysis resulted in 686 significant genes with a false discovery rate of 0.2. Among the various biological processes previously known to be associated with AD, we discovered a set of 14 DNA repair genes that had statistically elevated or lowered levels of mRNA expression. Many key players involved in the defense against DNA damage were present in this list of 14 genes. In this article we report the status of DNA repair activity in incipient AD. From this study we conclude that the much observed apoptosis in AD may also be due to the activity of DNA repair genes. These findings have not been previously reported with respect to incipient AD and may shed new light onto its pathogenesis. This is the first study that has incorporated multiple clinical phenotypes of AD affected individuals in order to select statistically significant genes. It is also the first in analysing DNA repair genes in the context of AD via microarray gene expression analysis