Qiong Cheng

Global analysis of Drosophila Cys2-His2 zinc finger proteins reveals a multitude of novel recognition motifs and binding determinants

Cys2-His2 zinc finger proteins (ZFPs) are the largest group of transcription factors in higher metazoans. A complete characterization of these ZFPs and their associated target sequences is pivotal to fully annotate transcriptional regulatory networks in metazoan genomes. As a first step in this process, we have characterized the DNA-binding specificities of 129 zinc finger sets from Drosophila using a bacterial one-hybrid system. This data set contains the DNA-binding specificities for at least one encoded ZFP from 70 unique genes and 23 alternate splice isoforms representing the largest set of characterized ZFPs from any organism described to date. These recognition motifs can be used to predict genomic binding sites for these factors within the fruit fly genome. Subsets of fingers from these ZFPs were characterized to define their orientation and register on their recognition sequences, thereby allowing us to define the recognition diversity within this finger set. We find that the characterized fingers can specify 47 of the 64 possible DNA triplets. To confirm the utility of our finger recognition models, we employed subsets of Drosophila fingers in combination with an existing archive of artificial zinc finger modules to create ZFPs with novel DNA-binding specificity. These hybrids of natural and artificial fingers can be used to create functional zinc finger nucleases for editing vertebrate genomes.

Fast Alignments of Metabolic Networks

Network alignments are extensively used for comparing, exploring, and predicting biological networks. Existing alignment tools are mostly based on isomorphic and homeomorphic embedding and require solving a problem that is NP-complete even when searching a match for a tree in acyclic networks. On the other hand, if the mapping of different nodes from the query network (pattern) into the same node from the text network is allowed, then trees can be optimally mapped into arbitrary networks in polynomial time.In this paper we present the first polynomial-time algorithm for finding the best matching pair consisting of a subtree in a given tree pattern and a subgraph in a given text (represented by an arbitrary network) when both insertions and deletions of degree-2 vertices are allowed on any path. Our dynamic programming algorithm is an order of magnitude faster than the previous network alignment algorithm when deletions are forbidden. The algorithm has been also generalized to pattern networks with cycles: with a modest increase in runtime it can handle patterns with the limited vertex feedback set.We have applied our algorithm to matching metabolic pathways of four organisms (E. coli, S. cerevisiae, B. subtilis and T. thermophilus species) and found a reasonably large set of statistically significant alignments. We show advantages of allowing pattern vertex deletions and give an example validating biological relevance of the pathway alignment.

Homomorphisms of Multisource Trees into Networks with Applications to Metabolic Pathways

Network mapping is a convenient tool for comparing and exploring biological networks; it can be used for predicting unknown pathways, fast and meaningful searching of databases, and potentially establishing evolutionary relations. Unfortunately, existing tools for mapping paths into general networks (PathBlast) or trees into tree networks allowing gaps (MetaPathwayHunter) cannot handle large query pathways or complex networks. In this paper we consider homomorphisms, i.e., mappings allowing to map different enzymes from the query pathway into the same enzyme from the networks. Homomorphisms are more general than homeomorphism (allowing gaps) and easier to handle algorithmically. Our dynamic programming algorithm efficiently finds the minimum cost homomorphism from a multisource tree to directed acyclic graphs as well as general networks. We have performed pairwise mapping of all pathways for four organisms (E. coli, S. cerevisiae, B. subtilis and T. thermophilus species) and found a reasonably large set of statistically significant pathway similarities. Further analysis of our mappings identifies conserved pathways across examined species and indicates potential pathway holes in existing pathway descriptions.

Efficient Alignments of Metabolic Networks with Bounded Treewidth

The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks becomes computationally challenging. Alignment of the reconstructed networks can help to catch model inconsistencies and infer missing elements. We have formulated the network alignment problem which asks for the optimal vertex-to-vertex mapping allowing path contraction, vertex deletion, and vertex insertions. This paper gives the first efficient algorithm for optimal aligning of metabolic pathways with bounded tree width. In particular, the optimal alignment from pathway P to pathway T can be found in time O(|VP| |VT|^(a+1))

Inferring metabolic pathway activity levels from RNA-Seq data

, where VP and VT are the vertex sets of pathways and a is the tree width of P. This significantly improves alignment tools since the E.coli metabolic network has tree width 3 and more than 90% of pathways of several organisms are series-parallel. We have implemented the algorithm for alignment of metabolic pathways of tree width 2 with arbitrary metabolic networks. Our experiments show that allowing pattern vertex deletion significantly improves alignment. We also have applied the network alignment to identifying inconsistency, inferring missing enzymes, and finding potential candidates for filling the holes.

Combinatorial Optimization Algorithms for Metabolic Networks Alignments and Their Applications

BackgroundAssessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs.ResultsXPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium “Candidatus Endobugula sertula”. We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR).ConclusionsOur results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway.

iC2mpi: A Platform for Parallel Execution of Graph-Structured Iterative Computations

The accumulation of high-throughput genomic and proteomic data allows for reconstruction of large and complex metabolic networks. To analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks; finding similar networks is computationally challenging. Based on gene duplication and function sharing in biological networks, a network alignment problem is formulated that asks the optimal vertex-to-vertex mapping allowing path contraction, different types of vertex deletion, and vertex insertions. This paper presents fixed parameter tractable combinatorial optimization algorithms, which take into account the similarity of both the enzymes’ functions arbitrary network topologies. Results are evaluated by the randomized P-Value computation. The authors perform pairwise alignments of all pathways for four organisms and find a set of statistically significant pathway similarities. The network alignment is used to identify pathway holes that are the result of inconsistencies and missing enzymes. The authors propose a framework of filling pathway holes by including database searches for missing enzymes and proteins with the matching prosites and further finding potential candidates with high sequence similarity.

Inferring conflict-sensitive phosphorylation dynamics

Parallelization of sequential programs is often daunting because of the substantial development cost involved. Previous solutions have not always been successful, partly because many try to address all types of applications. We propose a platform for parallelization of a class of applications that have similar computational structure, namely graph-structured iterative applications. iC2mpi is a unique proof-of-concept prototype platform that provides relatively easy parallelization of existing sequential programs and facilitates experimentation with static partitioning and dynamic load balancing schemes. We demonstrate with various generic application graph topologies that our platform can produce good performance with very little effort. The iC2mpi platform has a good potential for further performance improvements and for extensions to related classes of application domains.

Routing Using Messengers in Sparse and Disconnected Mobile Sensor Networks

Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. It acts as switches in cell communications. One of the most important factors contributing to the dynamics is the binding competition, which refers to the existence of more than one protein that physically binds to the same or an overlapping residue on a protein. By integrating data from different sources on the HeLa cancer cells and all available Homo sapiens (human) cells and iteratively examining the phosphorylation interfaces, we found a number of conflicting interaction pairs. We extended the search into indirect conflicts over direct upstream cascades and further into the whole network and calculated a min-max conflict-sensitive decomposition of phosphorylation network by graph-theoretical methods. Further we used EGF-stimulation phosphoproteome data and obtained activation patterns of phosphorated proteins by soft clustering. By combining these two groupings, we calculated an optimal conflict-free activation patterns using maximum bipartite matching. Sorting the average peak time of the activation patterns brought forth an activation order of min-max conflict-sensitive decomposition subnetworks. We evaluated conflict-sensitive phosophorylation dynamics by analyzing the importance of the conflicting interactions in the whole networks, the distribution of serine/threonine/tyrosine phosphorylation, and the direct or indirect activation order of phosphorylated proteins. Compared with a previously published approach [15], our solution discovered conflict-sensitive dynamics that resolved conflicts and it inferred more practical causal effects consistent with EGFR signaling pathways.

Learning Condition-Dependent Dynamical PPI Networks from Conflict-Sensitive Phosphorylation Dynamics

Sparse mobile sensor networks, such as those in the applications of ecology forest and modern battlefield, can frequently disconnect. Unfortunately, most existing routing protocols in mobile wireless networks mainly address connected networks, either sparse or dense. In this paper, we study the specific problem for dynamic routing in the sparse and disconnected mobile sensor networks utilizing messengers. We propose two routing discovery protocols: Genetic Fuzzy Straight Line Moving of Messengers (GFSLMM) and Genetic Fuzzy Flexible Sharing Policy of Messengers (GFFSPM). A preliminary simulation shows the efficacy of our protocols.