21 research outputs found
Short adjacent repeat identification based on chemical reaction optimization
IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)The analysis of short tandem repeats (STRs) in DNA sequences has become an attractive method for determining the genetic profile of an individual. Here we focus on a more general and practical issue named short adjacent repeats identification problem (SARIP), which is extended from STR by allowing short gaps between neighboring units. Presently, the best available solution to SARIP is BASARD, which uses Markov chain Monte Carlo algorithms to determine the posterior estimate. However, the computational complexity and the tendency to get stuck in a local mode lower the efficiency of BASARD and impede its wide application. In this paper, we prove that SARIP is NP-hard, and we also solve it with Chemical Reaction Optimization (CRO), a recently developed metaheuristic approach. CRO mimics the interactions of molecules in a chemical reaction and it can explore the solution space efficiently to find the optimal or near optimal solution(s). We test the CRO algorithm with both synthetic and real data, and compare its performance in mode searching with BASARD. Simulation results show that CRO enjoys dozens of times, or even a hundred times shorter computational time compared with BASARD. It is also demonstrated that CRO can obtain the global optima most of the time. Moreover, CRO is more stable in different runs, which is of great importance in practical use. Thus, CRO is by far the best method on SARIP. © 2012 IEEE.published_or_final_versio
Systems and Synthetic Biology Approaches to Engineer Fungi for Fine Chemical Production
Since the advent of systems and synthetic biology, many studies have sought to harness microbes as cell factories through genetic and metabolic engineering approaches. Yeast and filamentous fungi have been successfully harnessed to produce fine and high value-added chemical products. In this review, we present some of the most promising advances from recent years in the use of fungi for this purpose, focusing on the manipulation of fungal strains using systems and synthetic biology tools to improve metabolic flow and the flow of secondary metabolites by pathway redesign. We also review the roles of bioinformatics analysis and predictions in synthetic circuits, highlighting in silico systemic approaches to improve the efficiency of synthetic modules
Detection and analysis of RNA methylation [version 1; peer review: 2 approved]
Our understanding of the expanded genetic alphabet has been growing rapidly over the last two decades, and many of these developments came more than 80 years after the original discovery of a modified guanine in tuberculosis DNA. These new understandings, leading to the field of epigenetics, have led to exciting new fundamental and applied knowledge and to the development of novel classes of drugs exploiting this new biology. The number of methyl modifications to RNA is about seven times greater than those found on DNA, and our ability to interrogate these enigmatic nucleobases has lagged significantly until recent years as an explosion in technologies and understanding has revealed the roles and regulation of RNA methylation in several fundamental and disease-associated biological processes. Here, we outline how the technology has evolved and which strategies are commonly used in the modern epitranscriptomics revolution and give a foundation in the understanding and application of the rich variety of these methods to novel biological questions
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Collective analysis of multiple high-throughput gene expression datasets
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonModern technologies have resulted in the production of numerous high-throughput biological datasets. However, the pace of development of capable computational methods does not cope with the pace of generation of new high-throughput datasets. Amongst the most popular biological high-throughput datasets are gene expression datasets (e.g. microarray datasets). This work targets this aspect by proposing a suite of computational methods which can analyse multiple gene expression datasets collectively. The focal method in this suite is the unification of clustering results from multiple datasets using external specifications (UNCLES). This method applies clustering to multiple heterogeneous datasets which measure the expression of the same set of genes separately and then combines the resulting partitions in accordance to one of two types of external specifications; type A identifies the subsets of genes that are consistently co-expressed in all of the given datasets while type B identifies the subsets of genes that are consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets. This contributes to the types of questions which can addressed by computational methods because existing clustering, consensus clustering, and biclustering methods are inapplicable to address the aforementioned objectives. Moreover, in order to assist in setting some of the parameters required by UNCLES, the M-N scatter plots technique is proposed. These methods, and less mature versions of them, have been validated and applied to numerous real datasets from the biological contexts of budding yeast, bacteria, human red blood cells, and malaria. While collaborating with biologists, these applications have led to various biological insights. In yeast, the role of the poorly-understood gene CMR1 in the yeast cell-cycle has been further elucidated. Also, a novel subset of poorly understood yeast genes has been discovered with an expression profile consistently negatively correlated with the well-known ribosome biogenesis genes. Bacterial data analysis has identified two clusters of negatively correlated genes. Analysis of data from human red blood cells has produced some hypotheses regarding the regulation of the pathways producing such cells. On the other hand, malarial data analysis is still at a preliminary stage. Taken together, this thesis provides an original integrative suite of computational methods which scrutinise multiple gene expression datasets collectively to address previously unresolved questions, and provides the results and findings of many applications of these methods to real biological datasets from multiple contexts.National Institute for Health Research (NIHR) and the Brunel College of Engineering, Design and Physical Science
Investigating Novel Methods of Interaction with Pharmaceutically Relevant Enzymes
Metalloproteins requiring one or more metal ions for normal function make up 30% of all known proteins, and many critical biological pathways contain at least one metallo-enzyme. Di-nuclear metallo-proteins constitute a large class of these proteins yet we currently lack effective methods of inhibiting these enzymes for the development of new medical therapies, particularly for the discovery of new antibiotics. Our work has focused on developing novel functionalities that selectively interact with di-nuclear catalytic centers, and we are targeting three separate di-zinc-metallo-enzymes that are unique to bacteria and play key roles in their growth and development. These enzymes are DapE, AiiA, and NDM-1. DapE is involved in biosynthesis of lysine and meso-diaminopimelic acid, essential precursors in the production of bacterial cell walls. AiiA is a di-Zn-dependent lactonase involved in bacterial cell-cell communication, and NDM-1 is a di-metallo-beta-lactamase capable of deactivating the most commonly administered antibiotics, gaining international attention for this enzyme as a clinically-relevant pharmaceutical target, yet drug development efforts have proven ineffective due to a lack of effective inhibitors.
As part of our ongoing studies to functionally annotate the Gcn5-related N-acetyltransferase (GNAT) PA4794 from Pseudomonas aeruginosa with unknown functions, we have used PA4794 as a model system for exploring efficient formation of bisubstrate complexes to enhance our success rate in obtaining co-crystal structures of GNATs with ligands bound in their acceptor sites. We have synthesized and tested substrate analogs of the previously identified N-phenylacetyl glycine lysine (NPAcGK) enabling two separate three-dimensional structures of PA4794 with NPAcGK analog-derived bisubstrates formed through direct reaction with CoA—the first through direct alkylation with a reactive substrate, and the second through X-ray induced radical-mediated process. We have also performed docking and molecular dynamics simulations of the reverse reaction pathway from the NPAcGK product back to formation of the tetrahedral intermediate/transition state to complement our structural work and to explore the key ligand-protein interactions within the active site of PA4794, guiding mutant synthesis and kinetics to explore the role of key residues in the active site
Paradoxes of interactivity: perspectives for media theory, human-computer interaction, and artistic investigations
Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. "Paradoxes of Interactivity" brings together reflections on "interactivity" from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound
Paradoxes of Interactivity
Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. »Paradoxes of Interactivity« brings together reflections on »interactivity« from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound
ABC Transporters in Human Diseases
Mammalian ATP-binding cassette (ABC) transporters constitute a superfamily of proteins involved in many essential cellular processes. Most of these transporters are transmembrane proteins and allow the active transport of solutes, small molecules, and lipids across biological membranes. On the one hand, some of these transporters are involved in drug resistance (also referred to as MDR or multidrug resistance), a process known to be a major brake in most anticancer treatments, and the medical challenge is thus to specifically inhibit their function. On the other hand, molecular defects in some of these ABC transporters are correlated with several rare human diseases, the most well-documented of which being cystic fibrosis, which is caused by genetic variations in ABCC7/CFTR (cystic fibrosis transmembrane conductance regulator). In the latter case, the goal is to rescue the function of the deficient transporters using various means, such as targeted pharmacotherapies and cell or gene therapy. The aim of this Special Issue, “ABC Transporters in Human Diseases”, is to present, through original articles and reviews, the state-of-the-art of our current knowledge about the role of ABC transporters in human diseases and the proposed therapeutic options based on studies ranging from cell and animal models to patients
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp