Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

PubMed Central

Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

2012-01-01

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits. PMID:22536398

Advanced systems biology methods in drug discovery and translational biomedicine.

PubMed

Zou, Jun; Zheng, Ming-Wu; Li, Gen; Su, Zhi-Guang

2013-01-01

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

Science Teachers and the Dissection Debate: Perspectives on Animal Dissection and Alternatives

ERIC Educational Resources Information Center

Oakley, Jan

2012-01-01

This study investigated Ontario science and biology teachers' practices and attitudes toward animal dissection and dissection alternatives. The data was collected through a mixed methods approach involving online surveys (n = 153) and subsequent telephone interviews (n = 9) with secondary school science and biology teachers. The findings indicate…

DEF: an automated dead-end filling approach based on quasi-endosymbiosis.

PubMed

Liu, Lili; Zhang, Zijun; Sheng, Taotao; Chen, Ming

2017-02-01

Gap filling for the reconstruction of metabolic networks is to restore the connectivity of metabolites via finding high-confidence reactions that could be missed in target organism. Current methods for gap filling either fall into the network topology or have limited capability in finding missing reactions that are indirectly related to dead-end metabolites but of biological importance to the target model. We present an automated dead-end filling (DEF) approach, which is derived from the wisdom of endosymbiosis theory, to fill gaps by finding the most efficient dead-end utilization paths in a constructed quasi-endosymbiosis model. The recalls of reactions and dead ends of DEF reach around 73% and 86%, respectively. This method is capable of finding indirectly dead-end-related reactions with biological importance for the target organism and is applicable to any given metabolic model. In the E. coli iJR904 model, for instance, about 42% of the dead-end metabolites were fixed by our proposed method. DEF is publicly available at http://bis.zju.edu.cn/DEF/. mchen@zju.edu.cn Supplementary data are available at Bioinformatics online.

Optical spectrum of proflavine and its ions

NASA Astrophysics Data System (ADS)

Bonaca, A.; Bilalbegović, G.

2010-06-01

Motivated by possible astrophysical and biological applications we calculate visible and near UV spectral lines of proflavine (C13H11N3, 3,6-diaminoacridine) in vacuum, as well as its anion, cation, and dication. The pseudopotential density functional and time-dependent density functional methods are used. We find a good agreement in spectral line positions calculated by two real-time propagation methods and the Lanczos chain method. Spectra of proflavine and its ions show characteristic UV lines which are good candidates for a detection of these molecules in interstellar space and various biological processes.

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

Background One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments. PMID:23134636

Pathway Distiller - multisource biological pathway consolidation.

PubMed

Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

A study on the application of topic models to motif finding algorithms.

PubMed

Basha Gutierrez, Josep; Nakai, Kenta

2016-12-22

Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation. Here we present two methods that make use of topic models for motif finding. First, we developed an algorithm in which first a set of biological sequences are treated as text documents, and the k-mers contained in them as words, to then build a correlated topic model (CTM) and iteratively reduce its perplexity. We also used the perplexity measurement of CTMs to improve our previous algorithm based on a genetic algorithm and several statistical coefficients. The algorithms were tested with 56 data sets from four different species and compared to 14 other methods by the use of several coefficients both at nucleotide and site level. The results of our first approach showed a performance comparable to the other methods studied, especially at site level and in sensitivity scores, in which it scored better than any of the 14 existing tools. In the case of our previous algorithm, the new approach with the addition of the perplexity measurement clearly outperformed all of the other methods in sensitivity, both at nucleotide and site level, and in overall performance at site level. The statistics obtained show that the performance of a motif finding method based on the use of a CTM is satisfying enough to conclude that the application of topic models is a valid method for developing motif finding algorithms. Moreover, the addition of topic models to a previously developed method dramatically increased its performance, suggesting that this combined algorithm can be a useful tool to successfully predict motifs in different kinds of sets of DNA sequences.

Optimal network alignment with graphlet degree vectors.

PubMed

Milenković, Tijana; Ng, Weng Leong; Hayes, Wayne; Przulj, Natasa

2010-06-30

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

7th Annual Systems Biology Symposium: Systems Biology and Engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Galitski, Timothy P.

2008-04-01

Systems biology recognizes the complex multi-scale organization of biological systems, from molecules to ecosystems. The International Symposium on Systems Biology has been hosted by the Institute for Systems Biology in Seattle, Washington, since 2002. The annual two-day event gathers the most influential researchers transforming biology into an integrative discipline investingating complex systems. Engineering and application of new technology is a central element of systems biology. Genome-scale, or very small-scale, biological questions drive the enigneering of new technologies, which enable new modes of experimentation and computational analysis, leading to new biological insights and questions. Concepts and analytical methods in engineering aremore » now finding direct applications in biology. Therefore, the 2008 Symposium, funded in partnership with the Department of Energy, featured global leaders in "Systems Biology and Engineering."« less

Detecting subnetwork-level dynamic correlations.

PubMed

Yan, Yan; Qiu, Shangzhao; Jin, Zhuxuan; Gong, Sihong; Bai, Yun; Lu, Jianwei; Yu, Tianwei

2017-01-15

The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them. Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups. The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Are Prompts Provided by Electronic Books as Effective for Teaching Preschoolers a Biological Concept as Those Provided by Adults?

ERIC Educational Resources Information Center

Strouse, Gabrielle A.; Ganea, Patricia A.

2016-01-01

Research Findings: Prior research indicates that shared book reading is an effective method for teaching biological concepts to young children. Adult questioning during reading enhances children's comprehension. We investigated whether adult prompting during the reading of an electronic book enhanced children's understanding of a biological…

Path finding methods accounting for stoichiometry in metabolic networks

PubMed Central

2011-01-01

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

Counting motifs in dynamic networks.

PubMed

Mukherjee, Kingshuk; Hasan, Md Mahmudul; Boucher, Christina; Kahveci, Tamer

2018-04-11

A network motif is a sub-network that occurs frequently in a given network. Detection of such motifs is important since they uncover functions and local properties of the given biological network. Finding motifs is however a computationally challenging task as it requires solving the costly subgraph isomorphism problem. Moreover, the topology of biological networks change over time. These changing networks are called dynamic biological networks. As the network evolves, frequency of each motif in the network also changes. Computing the frequency of a given motif from scratch in a dynamic network as the network topology evolves is infeasible, particularly for large and fast evolving networks. In this article, we design and develop a scalable method for counting the number of motifs in a dynamic biological network. Our method incrementally updates the frequency of each motif as the underlying network's topology evolves. Our experiments demonstrate that our method can update the frequency of each motif in orders of magnitude faster than counting the motif embeddings every time the network changes. If the network evolves more frequently, the margin with which our method outperforms the existing static methods, increases. We evaluated our method extensively using synthetic and real datasets, and show that our method is highly accurate(≥ 96%) and that it can be scaled to large dense networks. The results on real data demonstrate the utility of our method in revealing interesting insights on the evolution of biological processes.

Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains†

PubMed Central

Bonney, Kevin M.

2015-01-01

Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses. PMID:25949753

Case study teaching method improves student performance and perceptions of learning gains.

PubMed

Bonney, Kevin M

2015-05-01

Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

A COMPENDIUM OF CHEMICAL, PHYSICAL AND BIOLOGICAL METHODS FOR ASSESSING AND MONITORING THE REMEDIATION OF CONTAMINATED SEDIMENT SITES

EPA Science Inventory

Considering the many organizations which have published methods for monitoring contaminated sediments and the large number of documents on this subject, it can be a formidable task for a superfund project manager to find methods appropriate for his or her contaminated sediment si...

PROPER: global protein interaction network alignment through percolation matching.

PubMed

Kazemi, Ehsan; Hassani, Hamed; Grossglauser, Matthias; Pezeshgi Modarres, Hassan

2016-12-12

The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch .

Insect herbivory stimulates allelopathic exudation by an invasive plant and the suppression of natives

Treesearch

Giles C. Thelen; Jorge M. Vivanco; Beth Newingham; William Good; Harsh P. Bais; Peter Landres; Anthony Caesar; Ragan M. Callaway

2005-01-01

Exotic invasive plants are often subjected to attack from imported insects as a method of biological control. A fundamental, but rarely explicitly tested, assumption of biological control is that damaged plants are less fit and compete poorly. In contrast, we find that one of the most destructive invasive plants in North America, Centaurea maculosa,...

ProMotE: an efficient algorithm for counting independent motifs in uncertain network topologies.

PubMed

Ren, Yuanfang; Sarkar, Aisharjya; Kahveci, Tamer

2018-06-26

Identifying motifs in biological networks is essential in uncovering key functions served by these networks. Finding non-overlapping motif instances is however a computationally challenging task. The fact that biological interactions are uncertain events further complicates the problem, as it makes the existence of an embedding of a given motif an uncertain event as well. In this paper, we develop a novel method, ProMotE (Probabilistic Motif Embedding), to count non-overlapping embeddings of a given motif in probabilistic networks. We utilize a polynomial model to capture the uncertainty. We develop three strategies to scale our algorithm to large networks. Our experiments demonstrate that our method scales to large networks in practical time with high accuracy where existing methods fail. Moreover, our experiments on cancer and degenerative disease networks show that our method helps in uncovering key functional characteristics of biological networks.

Neuro-symbolic representation learning on biological knowledge graphs.

PubMed

Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert

2017-09-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. https://github.com/bio-ontology-research-group/walking-rdf-and-owl. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

PubMed

Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.

GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways.

PubMed

Gamalielsson, Jonas; Olsson, Bjorn

2008-01-01

We present a new method for semantic comparison of biological pathways, aiming to discover evolutionary conservation of pathways between species. Our method uses all three sub-ontologies of Gene Ontology (GO) and a measure of semantic similarity to calculate match scores between gene products. These scores are used for finding local pairwise pathway alignments. This approach has the advantage of being applicable to all types of pathways where nodes are gene products, e.g., regulatory pathways, signalling pathways and metabolic enzyme-to-enzyme pathways. We demonstrate the usefulness of the method using regulatory and metabolic pathways from E. coli and S. cerevisiae as examples.

Confidence limits for Neyman type A-distributed events.

PubMed

Morand, Josselin; Deperas-Standylo, Joanna; Urbanik, Witold; Moss, Raymond; Hachem, Sabet; Sauerwein, Wolfgang; Wojcik, Andrzej

2008-01-01

The Neyman type A distribution, a generalised, 'contagious' Poisson distribution, finds application in a number of disciplines such as biology, physics and economy. In radiation biology, it best describes the distribution of chromosomal aberrations in cells that were exposed to neutrons, alpha radiations or heavy ions. Intriguingly, no method has been developed for the calculation of confidence limits (CLs) of Neyman type A-distributed events. Here, an algorithm to calculate the 95% CL of Neyman type A-distributed events is presented. Although it has been developed in response to the requirements of radiation biology, it can find application in other fields of research. The algorithm has been implemented in a PC-based computer program that can be downloaded, free of charge, from www.pu.kielce.pl/ibiol/neta.

Efficient Mining of Interesting Patterns in Large Biological Sequences

PubMed Central

Rashid, Md. Mamunur; Karim, Md. Rezaul; Jeong, Byeong-Soo

2012-01-01

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time. PMID:23105928

Efficient mining of interesting patterns in large biological sequences.

PubMed

Rashid, Md Mamunur; Karim, Md Rezaul; Jeong, Byeong-Soo; Choi, Ho-Jin

2012-03-01

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

Structural biology computing: Lessons for the biomedical research sciences.

PubMed

Morin, Andrew; Sliz, Piotr

2013-11-01

The field of structural biology, whose aim is to elucidate the molecular and atomic structures of biological macromolecules, has long been at the forefront of biomedical sciences in adopting and developing computational research methods. Operating at the intersection between biophysics, biochemistry, and molecular biology, structural biology's growth into a foundational framework on which many concepts and findings of molecular biology are interpreted1 has depended largely on parallel advancements in computational tools and techniques. Without these computing advances, modern structural biology would likely have remained an exclusive pursuit practiced by few, and not become the widely practiced, foundational field it is today. As other areas of biomedical research increasingly embrace research computing techniques, the successes, failures and lessons of structural biology computing can serve as a useful guide to progress in other biomedically related research fields. Copyright © 2013 Wiley Periodicals, Inc.

An efficient method to identify differentially expressed genes in microarray experiments

PubMed Central

Qin, Huaizhen; Feng, Tao; Harding, Scott A.; Tsai, Chung-Jui; Zhang, Shuanglin

2013-01-01

Motivation Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. Results We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. Availability The C++ code to implement the proposed method is available upon request for academic use. PMID:18453554

The Role of Involvement and Emotional Well-Being for Preschool Children's Scientific Observation Competency in Biology

ERIC Educational Resources Information Center

Klemm, Janina; Neuhaus, Birgit J.

2017-01-01

Observation is one of the basic methods in science. It is not only an epistemological method itself, but also an important competence for other methods like experimenting or comparing. However, there is little knowledge about the relation with affective factors of this inquiry method. In our study, we would like to find out about the relations of…

A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

PubMed

Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

PubMed Central

Seok, Junhee; Davis, Ronald W.; Xiao, Wenzhong

2015-01-01

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge. PMID:25933378

Compressed learning and its applications to subcellular localization.

PubMed

Zheng, Zhong-Long; Guo, Li; Jia, Jiong; Xie, Chen-Mao; Zeng, Wen-Cai; Yang, Jie

2011-09-01

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

Use of Bayesian Networks to Probabilistically Model and Improve the Likelihood of Validation of Microarray Findings by RT-PCR

PubMed Central

English, Sangeeta B.; Shih, Shou-Ching; Ramoni, Marco F.; Smith, Lois E.; Butte, Atul J.

2014-01-01

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation. PMID:18790084

Testing and Validation of Computational Methods for Mass Spectrometry.

PubMed

Gatto, Laurent; Hansen, Kasper D; Hoopmann, Michael R; Hermjakob, Henning; Kohlbacher, Oliver; Beyer, Andreas

2016-03-04

High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets ( http://compms.org/RefData ) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

Identification of common coexpression modules based on quantitative network comparison.

PubMed

Jo, Yousang; Kim, Sanghyeon; Lee, Doheon

2018-06-13

Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntington's disease and brain aging by the new method. We proposed two similarity measures for quantitative comparison of coexpression networks. Then, we performed experiments using known coexpression networks. We showed the validity of two measures and evaluated threshold values for similar coexpression network pairs from experiments. Using these similarity measures and thresholds, we quantitatively measured the similarity between disease-specific and aging-related coexpression modules and found similar Huntington's disease-aging coexpression module pairs. We identified similar Huntington's disease-aging coexpression module pairs and found that these modules are related to brain development, cell death, and immune response. It suggests that up-regulated cell signalling related cell death and immune/ inflammation response may be the common molecular mechanisms in the pathophysiology of HD and normal brain aging in the frontal cortex.

Resilience in mental health: linking psychological and neurobiological perspectives

PubMed Central

Rutten, B P F; Hammels, C; Geschwind, N; Menne-Lothmann, C; Pishva, E; Schruers, K; van den Hove, D; Kenis, G; van Os, J; Wichers, M

2013-01-01

Objective To review the literature on psychological and biological findings on resilience (i.e. the successful adaptation and swift recovery after experiencing life adversities) at the level of the individual, and to integrate findings from animal and human studies. Method Electronic and manual literature search of MEDLINE, EMBASE and PSYCHINFO, using a range of search terms around biological and psychological factors influencing resilience as observed in human and experimental animal studies, complemented by review articles and cross-references. Results The term resilience is used in the literature for different phenomena ranging from prevention of mental health disturbance to successful adaptation and swift recovery after experiencing life adversities, and may also include post-traumatic psychological growth. Secure attachment, experiencing positive emotions and having a purpose in life are three important psychological building blocks of resilience. Overlap between psychological and biological findings on resilience in the literature is most apparent for the topic of stress sensitivity, although recent results suggest a crucial role for reward experience in resilience. Conclusion Improving the understanding of the links between genetic endowment, environmental impact and gene–environment interactions with developmental psychology and biology is crucial for elucidating the neurobiological and psychological underpinnings of resilience. PMID:23488807

DLocalMotif: a discriminative approach for discovering local motifs in protein sequences.

PubMed

Mehdi, Ahmed M; Sehgal, Muhammad Shoaib B; Kobe, Bostjan; Bailey, Timothy L; Bodén, Mikael

2013-01-01

Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences. DLocalMotif combines three scoring functions, measuring degrees of motif over-representation, entropy and spatial confinement, specifically designed to discriminatively exploit the availability of negative data. The method is shown to outperform current methods that use only a subset of these motif characteristics. We apply the method to several biological datasets. The analysis of peroxisomal targeting signals uncovers several novel motifs that occur immediately upstream of the dominant peroxisomal targeting signal-1 signal. The analysis of proline-tyrosine nuclear localization signals uncovers multiple novel motifs that overlap with C2H2 zinc finger domains. We also evaluate the method on classical nuclear localization signals and endoplasmic reticulum retention signals and find that DLocalMotif successfully recovers biologically relevant sequence properties. http://bioinf.scmb.uq.edu.au/dlocalmotif/

Progeny Clustering: A Method to Identify Biological Phenotypes

PubMed Central

Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

2015-01-01

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

Fungal hallucinogens psilocin, ibotenic acid, and muscimol: analytical methods and biologic activities.

PubMed

Stebelska, Katarzyna

2013-08-01

Psychoactive drugs of fungal origin, psilocin, ibotenic acid, and muscimol among them have been proposed for recreational use and popularized since the 1960s, XX century. Despite their well-documented neurotoxicity, they reached reputation of being safe and nonaddictive. Scientific efforts to find any medical application for these hallucinogens in psychiatry, psychotherapy, and even for religious rituals support are highly controversial. Even if they show any healing potential, their usage in psychotherapy is in some cases inadequate and may additionally harm seriously suffering patients. Hallucinogens are thought to reduce cognitive functions. However, in case of indolealkylamines, such as psilocin, some recent findings suggest their ability to improve perception and mental skills, what would motivate the consumption of "magic mushrooms." The present article offers an opportunity to find out what are the main symptoms of intoxication with mushrooms containing psilocybin/psilocin, muscimol, and ibotenic acid. The progress in analytical methods for detection of them in fungal material, food, and body fluids is reviewed. Findings on the mechanisms of their biologic activity are summarized. Additionally, therapeutic potential of these fungal psychoactive compounds and health risk associated with their abuse are discussed.

Object-based attentional modulation of biological motion processing: spatiotemporal dynamics using functional magnetic resonance imaging and electroencephalography.

PubMed

Safford, Ashley S; Hussey, Elizabeth A; Parasuraman, Raja; Thompson, James C

2010-07-07

Although it is well documented that the ability to perceive biological motion is mediated by the lateral temporal cortex, whether and when neural activity in this brain region is modulated by attention is unknown. In particular, it is unclear whether the processing of biological motion requires attention or whether such stimuli are processed preattentively. Here, we used functional magnetic resonance imaging, high-density electroencephalography, and cortically constrained source estimation methods to investigate the spatiotemporal effects of attention on the processing of biological motion. Directing attention to tool motion in overlapping movies of biological motion and tool motion suppressed the blood oxygenation level-dependent (BOLD) response of the right superior temporal sulcus (STS)/middle temporal gyrus (MTG), while directing attention to biological motion suppressed the BOLD response of the left inferior temporal sulcus (ITS)/MTG. Similarly, category-based modulation of the cortical current source density estimates from the right STS/MTG and left ITS was observed beginning at approximately 450 ms following stimulus onset. Our results indicate that the cortical processing of biological motion is strongly modulated by attention. These findings argue against preattentive processing of biological motion in the presence of stimuli that compete for attention. Our findings also suggest that the attention-based segregation of motion category-specific responses only emerges relatively late (several hundred milliseconds) in processing.

Relating Diseases by Integrating Gene Associations and Information Flow through Protein Interaction Network

PubMed Central

Hamaneh, Mehdi Bagheri; Yu, Yi-Kuo

2014-01-01

Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/. PMID:25360770

Relating diseases by integrating gene associations and information flow through protein interaction network.

PubMed

Hamaneh, Mehdi Bagheri; Yu, Yi-Kuo

2014-01-01

Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/.

Nucleic Acid-Based Nanodevices in Biological Imaging.

PubMed

Chakraborty, Kasturi; Veetil, Aneesh T; Jaffrey, Samie R; Krishnan, Yamuna

2016-06-02

The nanoscale engineering of nucleic acids has led to exciting molecular technologies for high-end biological imaging. The predictable base pairing, high programmability, and superior new chemical and biological methods used to access nucleic acids with diverse lengths and in high purity, coupled with computational tools for their design, have allowed the creation of a stunning diversity of nucleic acid-based nanodevices. Given their biological origin, such synthetic devices have a tremendous capacity to interface with the biological world, and this capacity lies at the heart of several nucleic acid-based technologies that are finding applications in biological systems. We discuss these diverse applications and emphasize the advantage, in terms of physicochemical properties, that the nucleic acid scaffold brings to these contexts. As our ability to engineer this versatile scaffold increases, its applications in structural, cellular, and organismal biology are clearly poised to massively expand.

3D molecular models of whole HIV-1 virions generated with cellPACK

PubMed Central

Goodsell, David S.; Autin, Ludovic; Forli, Stefano; Sanner, Michel F.; Olson, Arthur J.

2014-01-01

As knowledge of individual biological processes grows, it becomes increasingly useful to frame new findings within their larger biological contexts in order to generate new systems-scale hypotheses. This report highlights two major iterations of a whole virus model of HIV-1, generated with the cellPACK software. cellPACK integrates structural and systems biology data with packing algorithms to assemble comprehensive 3D models of cell-scale structures in molecular detail. This report describes the biological data, modeling parameters and cellPACK methods used to specify and construct editable models for HIV-1. Anticipating that cellPACK interfaces under development will enable researchers from diverse backgrounds to critique and improve the biological models, we discuss how cellPACK can be used as a framework to unify different types of data across all scales of biology. PMID:25253262

Near-optimal experimental design for model selection in systems biology.

PubMed

Busetto, Alberto Giovanni; Hauser, Alain; Krummenacher, Gabriel; Sunnåker, Mikael; Dimopoulos, Sotiris; Ong, Cheng Soon; Stelling, Jörg; Buhmann, Joachim M

2013-10-15

Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).

Prediction of EST functional relationships via literature mining with user-specified parameters.

PubMed

Wang, Hei-Chia; Huang, Tian-Hsiang

2009-04-01

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

Physarum solver: A biologically inspired method of road-network navigation

NASA Astrophysics Data System (ADS)

Tero, Atsushi; Kobayashi, Ryo; Nakagaki, Toshiyuki

2006-04-01

We have proposed a mathematical model for the adaptive dynamics of the transport network in an amoeba-like organism, the true slime mold Physarum polycephalum. The model is based on physiological observations of this species, but can also be used for path-finding in the complicated networks of mazes and road maps. In this paper, we describe the physiological basis and the formulation of the model, as well as the results of simulations of some complicated networks. The path-finding method used by Physarum is a good example of cellular computation.

Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms.

PubMed

Boland, Mary Regina; Jacunski, Alexandra; Lorberbaum, Tal; Romano, Joseph D; Moskovitch, Robert; Tatonetti, Nicholas P

2016-01-01

Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and postmarket surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, whereas postmarket surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work. © 2015 Wiley Periodicals, Inc.

FIND: difFerential chromatin INteractions Detection using a spatial Poisson process

PubMed Central

Chen, Yang; Zhang, Michael Q.

2018-01-01

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio. PMID:29440282

Cellular Engineering with Membrane Fusogenic Liposomes to Produce Functionalized Extracellular Vesicles.

PubMed

Lee, Junsung; Lee, Hyoungjin; Goh, Unbyeol; Kim, Jiyoung; Jeong, Moonkyoung; Lee, Jean; Park, Ji-Ho

2016-03-23

Engineering of extracellular vesicles (EVs) without affecting biological functions remains a challenge, limiting the broad applications of EVs in biomedicine. Here, we report a method to equip EVs with various functional agents, including fluorophores, drugs, lipids, and bio-orthogonal chemicals, in an efficient and controlled manner by engineering parental cells with membrane fusogenic liposomes, while keeping the EVs intact. As a demonstration of how this method can be applied, we prepared EVs containing azide-lipids, and conjugated them with targeting peptides using copper-free click chemistry to enhance targeting efficacy to cancer cells. We believe that this liposome-based cellular engineering method will find utility in studying the biological roles of EVs and delivering therapeutic agents through their innate pathway.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

NASA Astrophysics Data System (ADS)

Mirel, Barbara; Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-02-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students' visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students' successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

PubMed Central

Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-01-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students’ visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students’ successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules. PMID:26877625

Evaluation of Specific Absorption Rate as a Dosimetric Quantity for Electromagnetic Fields Bioeffects

PubMed Central

Panagopoulos, Dimitris J.; Johansson, Olle; Carlo, George L.

2013-01-01

Purpose To evaluate SAR as a dosimetric quantity for EMF bioeffects, and identify ways for increasing the precision in EMF dosimetry and bioactivity assessment. Methods We discuss the interaction of man-made electromagnetic waves with biological matter and calculate the energy transferred to a single free ion within a cell. We analyze the physics and biology of SAR and evaluate the methods of its estimation. We discuss the experimentally observed non-linearity between electromagnetic exposure and biological effect. Results We find that: a) The energy absorbed by living matter during exposure to environmentally accounted EMFs is normally well below the thermal level. b) All existing methods for SAR estimation, especially those based upon tissue conductivity and internal electric field, have serious deficiencies. c) The only method to estimate SAR without large error is by measuring temperature increases within biological tissue, which normally are negligible for environmental EMF intensities, and thus cannot be measured. Conclusions SAR actually refers to thermal effects, while the vast majority of the recorded biological effects from man-made non-ionizing environmental radiation are non-thermal. Even if SAR could be accurately estimated for a whole tissue, organ, or body, the biological/health effect is determined by tiny amounts of energy/power absorbed by specific biomolecules, which cannot be calculated. Moreover, it depends upon field parameters not taken into account in SAR calculation. Thus, SAR should not be used as the primary dosimetric quantity, but used only as a complementary measure, always reporting the estimating method and the corresponding error. Radiation/field intensity along with additional physical parameters (such as frequency, modulation etc) which can be directly and in any case more accurately measured on the surface of biological tissues, should constitute the primary measure for EMF exposures, in spite of similar uncertainty to predict the biological effect due to non-linearity. PMID:23750202

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

Ludwig von Bertalanffy's organismic view on the theory of evolution.

PubMed

Drack, Manfred

2015-03-01

Ludwig von Bertalanffy was a key figure in the advancement of theoretical biology. His early considerations already led him to recognize the necessity of considering the organism as a system, as an organization of parts and processes. He termed the resulting research program organismic biology, which he extended to all basic questions of biology and almost all areas of biology, hence also to the theory of evolution. This article begins by outlining the rather unknown (because often written in German) research of Bertalanffy in the field of theoretical biology. The basics of the organismic approach are then described. This is followed by Bertalanffy's considerations on the theory of evolution, in which he used methods from theoretical biology and then introduced his own, organismic, view on evolution, leading to the demand for finding laws of evolution. Finally, his view on the concept of homology is presented. © 2015 Wiley Periodicals, Inc.

Predicting activity approach based on new atoms similarity kernel function.

PubMed

Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

2015-07-01

Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. Copyright © 2015 Elsevier Inc. All rights reserved.

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Controversy in Biology Classrooms-Citizen Science Approaches to Evolution and Applications to Climate Change Discussions.

PubMed

Yoho, Rachel A; Vanmali, Binaben H

2016-03-01

The biological sciences encompass topics considered controversial by the American public, such as evolution and climate change. We believe that the development of climate change education in the biology classroom is better informed by an understanding of the history of the teaching of evolution. A common goal for science educators should be to engender a greater respect for and appreciation of science among students while teaching specific content knowledge. Citizen science has emerged as a viable yet underdeveloped method for engaging students of all ages in key scientific issues that impact society through authentic data-driven scientific research. Where successful, citizen science may open avenues of communication and engagement with the scientific process that would otherwise be more difficult to achieve. Citizen science projects demonstrate versatility in education and the ability to test hypotheses by collecting large amounts of often publishable data. We find a great possibility for science education research in the incorporation of citizen science projects in curriculum, especially with respect to "hot topics" of socioscientific debate based on our review of the findings of other authors. Journal of Microbiology & Biology Education.

Retrieving the optical parameters of biological tissues using diffuse reflectance spectroscopy and Fourier series expansions. I. theory and application.

PubMed

Muñoz Morales, Aarón A; Vázquez Y Montiel, Sergio

2012-10-01

The determination of optical parameters of biological tissues is essential for the application of optical techniques in the diagnosis and treatment of diseases. Diffuse Reflection Spectroscopy is a widely used technique to analyze the optical characteristics of biological tissues. In this paper we show that by using diffuse reflectance spectra and a new mathematical model we can retrieve the optical parameters by applying an adjustment of the data with nonlinear least squares. In our model we represent the spectra using a Fourier series expansion finding mathematical relations between the polynomial coefficients and the optical parameters. In this first paper we use spectra generated by the Monte Carlo Multilayered Technique to simulate the propagation of photons in turbid media. Using these spectra we determine the behavior of Fourier series coefficients when varying the optical parameters of the medium under study. With this procedure we find mathematical relations between Fourier series coefficients and optical parameters. Finally, the results show that our method can retrieve the optical parameters of biological tissues with accuracy that is adequate for medical applications.

Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods.

PubMed

Nouri, Dorra; Lucas, Yves; Treuillet, Sylvie

2016-12-01

Hyperspectral imaging is an emerging technology recently introduced in medical applications inasmuch as it provides a powerful tool for noninvasive tissue characterization. In this context, a new system was designed to be easily integrated in the operating room in order to detect anatomical tissues hardly noticed by the surgeon's naked eye. Our LCTF-based spectral imaging system is operative over visible, near- and middle-infrared spectral ranges (400-1700 nm). It is dedicated to enhance critical biological tissues such as the ureter and the facial nerve. We aim to find the best three relevant bands to create a RGB image to display during the intervention with maximal contrast between the target tissue and its surroundings. A comparative study is carried out between band selection methods and band transformation methods. Combined band selection methods are proposed. All methods are compared using different evaluation criteria. Experimental results show that the proposed combined band selection methods provide the best performance with rich information, high tissue separability and short computational time. These methods yield a significant discrimination between biological tissues. We developed a hyperspectral imaging system in order to enhance some biological tissue visualization. The proposed methods provided an acceptable trade-off between the evaluation criteria especially in SWIR spectral band that outperforms the naked eye's capacities.

Separation and sorting of cells in microsystems using physical principles

NASA Astrophysics Data System (ADS)

Lee, Gi-Hun; Kim, Sung-Hwan; Ahn, Kihoon; Lee, Sang-Hoon; Park, Joong Yull

2016-01-01

In the last decade, microfabrication techniques have been combined with microfluidics and applied to cell biology. Utilizing such new techniques, various cell studies have been performed for the research of stem cells, immune cells, cancer, neurons, etc. Among the various biological applications of microtechnology-based platforms, cell separation technology has been highly regarded in biological and clinical fields for sorting different types of cells, finding circulating tumor cells (CTCs), and blood cell separation, amongst other things. Many cell separation methods have been created using various physical principles. Representatively, these include hydrodynamic, acoustic, dielectrophoretic, magnetic, optical, and filtering methods. In this review, each of these methods will be introduced, and their physical principles and sample applications described. Each physical principle has its own advantages and disadvantages. The engineers who design the systems and the biologists who use them should understand the pros and cons of each method or principle, to broaden the use of microsystems for cell separation. Continuous development of microsystems for cell separation will lead to new opportunities for diagnosing CTCs and cancer metastasis, as well as other elements in the bloodstream.

Systems Approaches to Cancer Biology.

PubMed

Archer, Tenley C; Fertig, Elana J; Gosline, Sara J C; Hafner, Marc; Hughes, Shannon K; Joughin, Brian A; Meyer, Aaron S; Piccolo, Stephen R; Shajahan-Haq, Ayesha N

2016-12-01

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR. ©2016 American Association for Cancer Research.

Stories of staying and leaving: A mixed methods analysis of biology undergraduate choice, persistence, and departure

NASA Astrophysics Data System (ADS)

Lang, Sarah Adrienne

Using a sequential, explanatory mixed methods design, this dissertation study compared students who persist in the biology major (persisters) with students who leave the biology major (switchers) in terms of how their pre-college experiences, college biology experiences, and biology performance figured into their choice of biology and their persistence in or departure from the biology major. This study combined (1) quantitative comparisons of biology persisters and switchers via a questionnaire developed for the study and survival analysis of a larger population of biology freshmen with (2) qualitative comparison of biology switchers and persisters via semi-structured life story interviews and homogenous focus groups. 319 students (207 persisters and 112 switchers) participated in the questionnaire and 36 students (20 persisters and 16 switchers) participated in life story and focus group interviews. All participants were undergraduates who entered The University of Texas at Austin as biology freshmen in the fall semesters of 2000 through 2004. Findings of this study suggest: (1) Regardless of eventual major, biology students enter college with generally the same suite of experiences, sources of personal encouragement, and reasons for choosing the biology major; (2) Despite the fact that they have also had poor experiences in the major, biology persisters do not actively decide to stay in the biology major; they simply do not leave; (3) Based upon survival analysis, biology students are most at-risk of leaving the biology major during the first two years of college and if they are African-American or Latino, women, or seeking a Bachelor of Arts degree (rather than a Bachelor of Science); (4) Biology switchers do not leave biology due to preference for other disciplines; they leave due to difficulties or dissatisfaction with aspects of the biology major, including their courses, faculty, and peers; (5) Biology performance has a differential effect on persistence in the biology major, depending on how well students perform in comparison to other courses or other students.

How to integrate biological research into society and exclude errors in biomedical publications? Progress in theoretical and systems biology releases pressure on experimental research.

PubMed

Volkov, Vadim

2014-01-01

This brief opinion proposes measures to increase efficiency and exclude errors in biomedical research under the existing dynamic situation. Rapid changes in biology began with the description of the three dimensional structure of DNA 60 years ago; today biology has progressed by interacting with computer science and nanoscience together with the introduction of robotic stations for the acquisition of large-scale arrays of data. These changes have had an increasing influence on the entire research and scientific community. Future advance demands short-term measures to ensure error-proof and efficient development. They can include the fast publishing of negative results, publishing detailed methodical papers and excluding a strict connection between career progression and publication activity, especially for younger researchers. Further development of theoretical and systems biology together with the use of multiple experimental methods for biological experiments could also be helpful in the context of years and decades. With regards to the links between science and society, it is reasonable to compare both these systems, to find and describe specific features for biology and to integrate it into the existing stream of social life and financial fluxes. It will increase the level of scientific research and have mutual positive effects for both biology and society. Several examples are given for further discussion.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

PubMed

Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

2017-12-11

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

A Comparison of Different Methods for Evaluating Diet, Physical Activity, and Long-Term Weight Gain in 3 Prospective Cohort Studies123

PubMed Central

Smith, Jessica D; Hou, Tao; Hu, Frank B; Rimm, Eric B; Spiegelman, Donna; Willett, Walter C; Mozaffarian, Dariush

2015-01-01

Background: The insidious pace of long-term weight gain (∼1 lb/y or 0.45 kg/y) makes it difficult to study in trials; long-term prospective cohorts provide crucial evidence on its key contributors. Most previous studies have evaluated how prevalent lifestyle habits relate to future weight gain rather than to lifestyle changes, which may be more temporally and physiologically relevant. Objective: Our objective was to evaluate and compare different methodological approaches for investigating diet, physical activity (PA), and long-term weight gain. Methods: In 3 prospective cohorts (total n = 117,992), we assessed how lifestyle relates to long-term weight change (up to 24 y of follow-up) in 4-y periods by comparing 3 analytic approaches: 1) prevalent diet and PA and 4-y weight change (prevalent analysis); 2) 4-y changes in diet and PA with a 4-y weight change (change analysis); and 3) 4-y change in diet and PA with weight change in the subsequent 4 y (lagged-change analysis). We compared these approaches and evaluated the consistency across cohorts, magnitudes of associations, and biological plausibility of findings. Results: Across the 3 methods, consistent, robust, and biologically plausible associations were seen only for the change analysis. Results for prevalent or lagged-change analyses were less consistent across cohorts, smaller in magnitude, and biologically implausible. For example, for each serving of a sugar-sweetened beverage, the observed weight gain was 0.01 lb (95% CI: −0.08, 0.10) [0.005 kg (95% CI: −0.04, 0.05)] based on prevalent analysis; 0.99 lb (95% CI: 0.83, 1.16) [0.45 kg (95% CI: 0.38, 0.53)] based on change analysis; and 0.05 lb (95% CI: −0.10, 0.21) [0.02 kg (95% CI: −0.05, 0.10)] based on lagged-change analysis. Findings were similar for other foods and PA. Conclusions: Robust, consistent, and biologically plausible relations between lifestyle and long-term weight gain are seen when evaluating lifestyle changes and weight changes in discrete periods rather than in prevalent lifestyle or lagged changes. These findings inform the optimal methods for evaluating lifestyle and long-term weight gain and the potential for bias when other methods are used. PMID:26377763

Identifying cooperative transcriptional regulations using protein–protein interactions

PubMed Central

Nagamine, Nobuyoshi; Kawada, Yuji; Sakakibara, Yasubumi

2005-01-01

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein–protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein–protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein–protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy. PMID:16126847

Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

PubMed Central

Arias, Carlos Roberto; Yeh, Hsiang-Yuan; Soo, Von-Wun

2012-01-01

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well. PMID:22654636

A private DNA motif finding algorithm.

PubMed

Chen, Rui; Peng, Yun; Choi, Byron; Xu, Jianliang; Hu, Haibo

2014-08-01

With the increasing availability of genomic sequence data, numerous methods have been proposed for finding DNA motifs. The discovery of DNA motifs serves a critical step in many biological applications. However, the privacy implication of DNA analysis is normally neglected in the existing methods. In this work, we propose a private DNA motif finding algorithm in which a DNA owner's privacy is protected by a rigorous privacy model, known as ∊-differential privacy. It provides provable privacy guarantees that are independent of adversaries' background knowledge. Our algorithm makes use of the n-gram model and is optimized for processing large-scale DNA sequences. We evaluate the performance of our algorithm over real-life genomic data and demonstrate the promise of integrating privacy into DNA motif finding. Copyright © 2014 Elsevier Inc. All rights reserved.

FIND: difFerential chromatin INteractions Detection using a spatial Poisson process.

PubMed

Djekidel, Mohamed Nadhir; Chen, Yang; Zhang, Michael Q

2018-02-12

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio. © 2018 Djekidel et al.; Published by Cold Spring Harbor Laboratory Press.

A false start in the race against doping in sport: concerns with cycling's biological passport.

PubMed

Hailey, Nicholas

2011-11-01

Professional cycling has suffered from a number of doping scandals. The sport's governing bodies have responded by implementing an aggressive new antidoping program known as the biological passport. Cycling's biological passport marks a departure from traditional antidoping efforts, which have focused on directly detecting prohibited substances in a cyclist's system. Instead, the biological passport tracks biological variables in a cyclist's blood and urine over time, monitoring for fluctuations that are thought to indirectly reveal the effects of doping. Although this method of indirect detection is promising, it also raises serious legal and scientific concerns. Since its introduction, the cycling community has debated the reliability of indirect biological-passport evidence and the clarity, consistency, and transparency of its use in proving doping violations. Such uncertainty undermines the legitimacy of finding cyclists guilty of doping based on this indirect evidence alone. Antidoping authorities should address these important concerns before continuing to pursue doping sanctions against cyclists solely on the basis of their biological passports.

Decompositions of large-scale biological systems based on dynamical properties.

PubMed

Soranzo, Nicola; Ramezani, Fahimeh; Iacono, Giovanni; Altafini, Claudio

2012-01-01

Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties. The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems. Original heuristics for the methods investigated are described in the article. altafini@sissa.it

The influence of biological sex, sexuality and gender role on interpersonal distance.

PubMed

Uzzell, David; Horne, Nathalie

2006-09-01

This research reports on a conceptually and methodologically innovative study, which sought to measure the influence of gender on interpersonal distance. In so doing, we argue for an important distinction to be made between biological sex, gender role, and sexuality. To date, however, progress in the study of interpersonal distance (IPD) has been inhibited by poor operational definitions and inadequate measurement methodologies. For our own investigation, we innovated on methodology by devising the digital video-recording IPD method (DiVRID) that records interpersonal spatial relationships using high quality digital video equipment. The findings highlighted not only the validity of our innovative method of investigation, but also that a more sophisticated conceptualization of the impact of gender on IPD is warranted than can be accounted for by biological sex differences. In this study, we found that gender role accounts for more of the variation in IPD than the conventionally reported gender variable, sex.

Insects as model systems in cell biology.

PubMed

Keil, Thomas A; Steinbrecht, R Alexander

2010-01-01

For almost 100 years, insects have been favorable "model systems" in biology. Just to mention a few examples: fruit flies in genetics and developmental biology; bugs and caterpillars in hormone research; houseflies, blowflies, and locusts in neurobiology; silk moths in pheromone research; honeybees and crickets in neuroethology. For more than 50 years the electron microscope (EM) has been a valuable tool in analyzing the structure of cells and organs of these creatures. However, progress in specimen preparation was relatively slow compared with mammalian material and, in 1970, it was taken for granted that insects were much more difficult to fix than mammals. Since then, methods have dramatically improved, and satisfactory results can now be obtained routinely with chemical as well as cryofixation. In this chapter we briefly demonstrate what can be achieved with insect material, and help the researcher to find the most appropriate method for her/his systems and scientific questions. Copyright © 2010 Elsevier Inc. All rights reserved.

Lesbian Parenting: Issues, Strengths and Challenges.

ERIC Educational Resources Information Center

McNair, Ruth; Dempsey, Deborah; Wise, Sarah; Perlesz, Amaryll

2002-01-01

This study examined family relationships, conception methods, involvement of biological fathers in their children's lives, and the use of social and support networks by Australian lesbian and bisexual parents/prospective parents. Findings indicated that lesbians achieved parenthood most commonly within a heterosexual relationship or through self-…

Bioinformatics and the Undergraduate Curriculum

ERIC Educational Resources Information Center

Maloney, Mark; Parker, Jeffrey; LeBlanc, Mark; Woodard, Craig T.; Glackin, Mary; Hanrahan, Michael

2010-01-01

Recent advances involving high-throughput techniques for data generation and analysis have made familiarity with basic bioinformatics concepts and programs a necessity in the biological sciences. Undergraduate students increasingly need training in methods related to finding and retrieving information stored in vast databases. The rapid rise of…

Fuzzy method of recognition of high molecular substances in evidence-based biology

NASA Astrophysics Data System (ADS)

Olevskyi, V. I.; Smetanin, V. T.; Olevska, Yu. B.

2017-10-01

Nowadays modern requirements to achieving reliable results along with high quality of researches put mathematical analysis methods of results at the forefront. Because of this, evidence-based methods of processing experimental data have become increasingly popular in the biological sciences and medicine. Their basis is meta-analysis, a method of quantitative generalization of a large number of randomized trails contributing to a same special problem, which are often contradictory and performed by different authors. It allows identifying the most important trends and quantitative indicators of the data, verification of advanced hypotheses and discovering new effects in the population genotype. The existing methods of recognizing high molecular substances by gel electrophoresis of proteins under denaturing conditions are based on approximate methods for comparing the contrast of electrophoregrams with a standard solution of known substances. We propose a fuzzy method for modeling experimental data to increase the accuracy and validity of the findings of the detection of new proteins.

Anti-friction performance of FeS nanoparticle synthesized by biological method

NASA Astrophysics Data System (ADS)

Zhou, Lu Hai; Wei, Xi Cheng; Ma, Zi Jian; Mei, Bin

2017-06-01

FeS nanoparticle is prepared by a biological method. The size, morphology and structure of the FeS nanoparticle are characterized by the means of X-ray diffraction and transmission electron microscopy. The anti-friction behavior of the FeS nanoparticle as a lubricating oil additive is evaluated in the engine oil by using a face-to-face contact mode. The worn surface is characterized by using the scanning electron microscopy and secondary ion mass spectroscopy in order to find the reasons resulting in the reduction of friction coefficient due to the addition of the FeS nanoparticle. The anti-friction mechanism of the FeS nanoparticle is elucidated based on the experimental results.

Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine.

PubMed

Li, Peng; Chen, Jianxin; Zhang, Wuxia; Fu, Bangze; Wang, Wei

2017-01-04

Herbal medicine is a concoction of numerous chemical ingredients, and it exhibits polypharmacological effects to act on multiple pharmacological targets, regulating different biological mechanisms and treating a variety of diseases. Thus, this complexity is impossible to deconvolute by the reductionist method of extracting one active ingredient acting on one biological target. To dissect the polypharmacological effects of herbal medicines and their underling pharmacological targets as well as their corresponding active ingredients. We propose a system-biology strategy that combines omics and bioinformatical methodologies for exploring the polypharmacology of herbal mixtures. The myocardial ischemia model was induced by Ameroid constriction of the left anterior descending coronary in Ba-Ma miniature pigs. RNA-seq analysis was utilized to find the differential genes induced by myocardial ischemia in pigs treated with formula QSKL. A transcriptome-based inference method was used to find the landmark drugs with similar mechanisms to QSKL. Gene-level analysis of RNA-seq data in QSKL-treated cases versus control animals yields 279 differential genes. Transcriptome-based inference methods identified 80 landmark drugs that covered nearly all drug classes. Then, based on the landmark drugs, 155 potential pharmacological targets and 57 indications were identified for QSKL. Our results demonstrate the power of a combined approach for exploring the pharmacological target and chemical space of herbal medicines. We hope that our method could enhance our understanding of the molecular mechanisms of herbal systems and further accelerate the exploration of the value of traditional herbal medicine systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Evaluating performance in three-dimensional fluorescence microscopy

PubMed Central

MURRAY, JOHN M; APPLETON, PAUL L; SWEDLOW, JASON R; WATERS, JENNIFER C

2007-01-01

In biological fluorescence microscopy, image contrast is often degraded by a high background arising from out of focus regions of the specimen. This background can be greatly reduced or eliminated by several modes of thick specimen microscopy, including techniques such as 3-D deconvolution and confocal. There has been a great deal of interest and some confusion about which of these methods is ‘better’, in principle or in practice. The motivation for the experiments reported here is to establish some rough guidelines for choosing the most appropriate method of microscopy for a given biological specimen. The approach is to compare the efficiency of photon collection, the image contrast and the signal-to-noise ratio achieved by the different methods at equivalent illumination, using a specimen in which the amount of out of focus background is adjustable over the range encountered with biological samples. We compared spot scanning confocal, spinning disk confocal and wide-field/deconvolution (WFD) microscopes and find that the ratio of out of focus background to in-focus signal can be used to predict which method of microscopy will provide the most useful image. We also find that the precision of measurements of net fluorescence yield is very much lower than expected for all modes of microscopy. Our analysis enabled a clear, quantitative delineation of the appropriate use of different imaging modes relative to the ratio of out-of-focus background to in-focus signal, and defines an upper limit to the useful range of the three most common modes of imaging. PMID:18045334

Comparative Network-Based Recovery Analysis and Proteomic Profiling of Neurological Changes in Valproic Acid-Treated Mice

PubMed Central

2013-01-01

Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. PMID:23557376

A Review of the Clinical Implications of Breast Cancer Biology

PubMed Central

Parsa, Yekta; Mirmalek, Seyed Abbas; Kani, Fatemeh Elham; Aidun, Amir; Salimi-Tabatabaee, Seyed Alireza; Yadollah-Damavandi, Soheila; Jangholi, Ehsan; Parsa, Tina; Shahverdi, Ehsan

2016-01-01

Background Histologically similar tumors may have different prognoses and responses to treatment. These differences are due to molecular differences. Hence, in this review, the biological interaction of breast cancer in several different areas is discussed. In addition, the performance and clinical application of the most widely-recognized biomarkers, metastasis, and recurrences from a biological perspective and current global advances in these areas are addressed. Objective This review provides the performance and clinical application of the most widely-recognized biomarkers, metastasis, and recurrences from the biological perspective and current global advances in these areas. Methods PubMed, Scopus, and Google Scholar were searched comprehensively with combinations of the following keywords: “breast cancer,” “biological markers,” and “clinical.” The definition of breast cancer, diagnostic methods, biological markers, and available treatment approaches were extracted from the literature. Results Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), and Ki-67 are the most well-known biological markers that have important roles in prognosis and response to therapeutic methods. Some studies showed the response of ER-positive and PR-negative tumors to anti-estrogenic treatment to be lower than ER-positive and PR-positive tumors. Patients with high expression of HER-2 and Ki-67 had a poor prognosis. In addition, recent investigations indicated the roles of new biomarkers, such as VEGF, IGF, P53 and P21, which are associated with many factors, such as age, race, and histological features. Conclusion The objective of scientists, from establishing a relationship between cancer biology infrastructures with clinical manifestations, is to find new ways of prevention and progression inhibition and then possible introduction of less dangerous and better treatments to resolve this dilemma of human society. PMID:27382453

Systems Biology, Neuroimaging, Neuropsychology, Neuroconnectivity and Traumatic Brain Injury

PubMed Central

Bigler, Erin D.

2016-01-01

The patient who sustains a traumatic brain injury (TBI) typically undergoes neuroimaging studies, usually in the form of computed tomography (CT) and magnetic resonance imaging (MRI). In most cases the neuroimaging findings are clinically assessed with descriptive statements that provide qualitative information about the presence/absence of visually identifiable abnormalities; though little if any of the potential information in a scan is analyzed in any quantitative manner, except in research settings. Fortunately, major advances have been made, especially during the last decade, in regards to image quantification techniques, especially those that involve automated image analysis methods. This review argues that a systems biology approach to understanding quantitative neuroimaging findings in TBI provides an appropriate framework for better utilizing the information derived from quantitative neuroimaging and its relation with neuropsychological outcome. Different image analysis methods are reviewed in an attempt to integrate quantitative neuroimaging methods with neuropsychological outcome measures and to illustrate how different neuroimaging techniques tap different aspects of TBI-related neuropathology. Likewise, how different neuropathologies may relate to neuropsychological outcome is explored by examining how damage influences brain connectivity and neural networks. Emphasis is placed on the dynamic changes that occur following TBI and how best to capture those pathologies via different neuroimaging methods. However, traditional clinical neuropsychological techniques are not well suited for interpretation based on contemporary and advanced neuroimaging methods and network analyses. Significant improvements need to be made in the cognitive and behavioral assessment of the brain injured individual to better interface with advances in neuroimaging-based network analyses. By viewing both neuroimaging and neuropsychological processes within a systems biology perspective could represent a significant advancement for the field. PMID:27555810

Undergraduate Students’ Difficulties in Reading and Constructing Phylogenetic Tree

NASA Astrophysics Data System (ADS)

Sa'adah, S.; Tapilouw, F. S.; Hidayat, T.

2017-02-01

Representation is a very important communication tool to communicate scientific concepts. Biologists produce phylogenetic representation to express their understanding of evolutionary relationships. The phylogenetic tree is visual representation depict a hypothesis about the evolutionary relationship and widely used in the biological sciences. Phylogenetic tree currently growing for many disciplines in biology. Consequently, learning about phylogenetic tree become an important part of biological education and an interesting area for biology education research. However, research showed many students often struggle with interpreting the information that phylogenetic trees depict. The purpose of this study was to investigate undergraduate students’ difficulties in reading and constructing a phylogenetic tree. The method of this study is a descriptive method. In this study, we used questionnaires, interviews, multiple choice and open-ended questions, reflective journals and observations. The findings showed students experiencing difficulties, especially in constructing a phylogenetic tree. The students’ responds indicated that main reasons for difficulties in constructing a phylogenetic tree are difficult to placing taxa in a phylogenetic tree based on the data provided so that the phylogenetic tree constructed does not describe the actual evolutionary relationship (incorrect relatedness). Students also have difficulties in determining the sister group, character synapomorphy, autapomorphy from data provided (character table) and comparing among phylogenetic tree. According to them building the phylogenetic tree is more difficult than reading the phylogenetic tree. Finding this studies provide information to undergraduate instructor and students to overcome learning difficulties of reading and constructing phylogenetic tree.

Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data

PubMed Central

Deckard, Anastasia; Anafi, Ron C.; Hogenesch, John B.; Haase, Steven B.; Harer, John

2013-01-01

Motivation: To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and biological data. Synthetic data were used to evaluate how each algorithm responds to different curve shapes, periods, phase shifts, noise levels and sampling rates. The biological datasets we tested represent a variety of periodic processes from different organisms, including the cell cycle and metabolic cycle in Saccharomyces cerevisiae, circadian rhythms in Mus musculus and the root clock in Arabidopsis thaliana. Results: From these results, we discovered that each algorithm had different strengths. Based on our findings, we make recommendations for selecting and applying these methods depending on the nature of the data and the periodic patterns of interest. Additionally, these results can also be used to inform the design of large-scale biological rhythm experiments so that the resulting data can be used with these algorithms to detect periodic signals more effectively. Contact: anastasia.deckard@duke.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24058056

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

PubMed Central

2012-01-01

Background Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. Results We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. Conclusions CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression. PMID:22443377

Biology teachers' dissection practices and the influences that lead to their adoption: An exploratory research

NASA Astrophysics Data System (ADS)

Milano, Regina Nicole

The lack of resolution in the on-going animal dissection debate inspired this mixed methods study to identify Connecticut secondary biology teachers' dissection practices and the influences that lead to their adoption. Qualitative findings indicate past experiences, managing objections to dissection, school culture, goals of biology teaching and ethics as major influences on dissection practices with 58.4% (n=7) of the sample dissecting and 41.6% not dissecting (n=5). Quantitative findings reveal gender, standards and curriculum, advantages of dissection and experiences as a student as major influences on dissection practices with 71.9% (n=92) of the sample dissecting and 28.1% (n=36) not dissecting. The study concludes that dissection policies are necessary and imminent in Connecticut school districts. Furthermore, it advises teacher-initiated, qualitative and quantitative assessments to expose disparities between student dissection perspectives and their own, prior to conducting dissection. Finally, it provides suggestions for addressing potential differences including administrative involvement.

G =  MAT: linking transcription factor expression and DNA binding data.

PubMed

Tretyakov, Konstantin; Laur, Sven; Vilo, Jaak

2011-01-31

Transcription factors are proteins that bind to motifs on the DNA and thus affect gene expression regulation. The qualitative description of the corresponding processes is therefore important for a better understanding of essential biological mechanisms. However, wet lab experiments targeted at the discovery of the regulatory interplay between transcription factors and binding sites are expensive. We propose a new, purely computational method for finding putative associations between transcription factors and motifs. This method is based on a linear model that combines sequence information with expression data. We present various methods for model parameter estimation and show, via experiments on simulated data, that these methods are reliable. Finally, we examine the performance of this model on biological data and conclude that it can indeed be used to discover meaningful associations. The developed software is available as a web tool and Scilab source code at http://biit.cs.ut.ee/gmat/.

G = MAT: Linking Transcription Factor Expression and DNA Binding Data

PubMed Central

Tretyakov, Konstantin; Laur, Sven; Vilo, Jaak

2011-01-01

Transcription factors are proteins that bind to motifs on the DNA and thus affect gene expression regulation. The qualitative description of the corresponding processes is therefore important for a better understanding of essential biological mechanisms. However, wet lab experiments targeted at the discovery of the regulatory interplay between transcription factors and binding sites are expensive. We propose a new, purely computational method for finding putative associations between transcription factors and motifs. This method is based on a linear model that combines sequence information with expression data. We present various methods for model parameter estimation and show, via experiments on simulated data, that these methods are reliable. Finally, we examine the performance of this model on biological data and conclude that it can indeed be used to discover meaningful associations. The developed software is available as a web tool and Scilab source code at http://biit.cs.ut.ee/gmat/. PMID:21297945

Toward a Biosignature for Suicide

PubMed Central

Oquendo, Maria A.; Sullivan, Gregory M.; Sudol, Katherin; Baca-Garcia, Enrique; Stanley, Barbara H.; Sublette, M. Elizabeth; Mann, J. John

2015-01-01

Objective Suicide, a major cause of death worldwide, has distinct biological underpinnings. The authors review and synthesize the research literature on biomarkers of suicide, with the aim of using the findings of these studies to develop a coherent model for the biological diathesis for suicide. Method The authors examined studies covering a large range of neurobiological systems implicated in suicide. They provide succinct descriptions of each system to provide a context for interpreting the meaning of findings in suicide. Results Several lines of evidence implicate dysregulation in stress response systems, especially the hypothalamic-pituitary-adrenal axis, as a diathesis for suicide. Additional findings related to neuroinflammatory indices, glutamatergic function, and neuronal plasticity at the cellular and circuitry level may reflect downstream effects of such dysregulation. Whether serotonergic abnormalities observed in individuals who have died by suicide are independent of stress response abnormalities is an unresolved question. Conclusions The most compelling biomarkers for suicide are linked to altered stress responses and their downstream effects, and to abnormalities in the serotonergic system. Studying these systems in parallel and in the same populations may elucidate the role of each and their interplay, possibly leading to identification of new treatment targets and biological predictors. PMID:25263730

DNA nanotechnology: Bringing lipid bilayers into shape

NASA Astrophysics Data System (ADS)

Howorka, Stefan

2017-07-01

Lipid bilayers form the thin and floppy membranes that define the boundary of compartments such as cells. Now, a method to control the shape and size of bilayers using DNA nanoscaffolds has been developed. Such designer materials advance synthetic biology and could find use in membrane research.

Separating intrinsic from extrinsic fluctuations in dynamic biological systems

PubMed Central

Paulsson, Johan

2011-01-01

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems. PMID:21730172

Separating intrinsic from extrinsic fluctuations in dynamic biological systems.

PubMed

Hilfinger, Andreas; Paulsson, Johan

2011-07-19

From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems.

Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites

PubMed Central

Meinicke, Peter; Tech, Maike; Morgenstern, Burkhard; Merkl, Rainer

2004-01-01

Background Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon. Conclusions We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems. PMID:15511290

An efficient and sensitive method for preparing cDNA libraries from scarce biological samples

PubMed Central

Sterling, Catherine H.; Veksler-Lublinsky, Isana; Ambros, Victor

2015-01-01

The preparation and high-throughput sequencing of cDNA libraries from samples of small RNA is a powerful tool to quantify known small RNAs (such as microRNAs) and to discover novel RNA species. Interest in identifying the small RNA repertoire present in tissues and in biofluids has grown substantially with the findings that small RNAs can serve as indicators of biological conditions and disease states. Here we describe a novel and straightforward method to clone cDNA libraries from small quantities of input RNA. This method permits the generation of cDNA libraries from sub-picogram quantities of RNA robustly, efficiently and reproducibly. We demonstrate that the method provides a significant improvement in sensitivity compared to previous cloning methods while maintaining reproducible identification of diverse small RNA species. This method should have widespread applications in a variety of contexts, including biomarker discovery from scarce samples of human tissue or body fluids. PMID:25056322

Estimation of biological variation and reference change value of glycated hemoglobin (HbA(1c)) when two analytical methods are used.

PubMed

Ucar, Fatma; Erden, Gonul; Ginis, Zeynep; Ozturk, Gulfer; Sezer, Sevilay; Gurler, Mukaddes; Guneyk, Ahmet

2013-10-01

Available data on biological variation of HbA1c revealed marked heterogeneity. We therefore investigated and estimated the components of biological variation for HbA1c in a group of healthy individuals by applying a recommended and strictly designed study protocol using two different assay methods. Each month, samples were derived on the same day, for three months. Four EDTA whole blood samples were collected from each individual (20 women, 9 men; 20-45 years of age) and stored at -80°C until analysis. HbA1c values were measured by both high performance liquid chromatography (HPLC) (Shimadzu, Prominence, Japan) and boronate affinity chromatography methods (Trinity Biotech, Premier Hb9210, Ireland). All samples were assayed in duplicate in a single batch for each assay method. Estimations were calculated according to the formulas described by Fraser and Harris. The within subject (CV(I))-between subject (CV(G)) biological variations were 1.17% and 5.58%, respectively for HPLC. The calculated CV(I) and CV(G) were 2.15% and 4.03%, respectively for boronate affinity chromatography. Reference change value (RCV) for HPLC and boronate affinity chromatography was 5.4% and 10.4% respectively and individuality index of HbA(1c) was 0.35 and 0.93 respectively. This study for the first time described the components of biological variation for HbA1c in healthy individuals by two different assay methods. Obtained findings showed that the difference between CV(A) values of the methods might considerably affect RCV. These data regarding biological variation of HbA(1c) could be useful for a better evaluation of HbA(1c) test results in clinical interpretation. Copyright © 2013 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Automatic extraction of angiogenesis bioprocess from text

PubMed Central

Wang, Xinglong; McKendrick, Iain; Barrett, Ian; Dix, Ian; French, Tim; Tsujii, Jun'ichi; Ananiadou, Sophia

2011-01-01

Motivation: Understanding key biological processes (bioprocesses) and their relationships with constituent biological entities and pharmaceutical agents is crucial for drug design and discovery. One way to harvest such information is searching the literature. However, bioprocesses are difficult to capture because they may occur in text in a variety of textual expressions. Moreover, a bioprocess is often composed of a series of bioevents, where a bioevent denotes changes to one or a group of cells involved in the bioprocess. Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find. Results: This article presents a range of methods for finding bioprocess terms and events. To facilitate the study, we built a gold standard corpus in which terms and events related to angiogenesis, a key biological process of the growth of new blood vessels, were annotated. Statistics of the annotated corpus revealed that over 36% of the text expressions that referred to angiogenesis appeared as events. The proposed methods respectively employed domain-specific vocabularies, a manually annotated corpus and unstructured domain-specific documents. Evaluation results showed that, while a supervised machine-learning model yielded the best precision, recall and F1 scores, the other methods achieved reasonable performance and less cost to develop. Availability: The angiogenesis vocabularies, gold standard corpus, annotation guidelines and software described in this article are available at http://text0.mib.man.ac.uk/~mbassxw2/angiogenesis/ Contact: xinglong.wang@gmail.com PMID:21821664

Chemical biology based on target-selective degradation of proteins and carbohydrates using light-activatable organic molecules.

PubMed

Toshima, Kazunobu

2013-05-01

Proteins and carbohydrates play crucial roles in a wide range of biological processes, including serious diseases. The development of novel and innovative methods for selective control of specific proteins and carbohydrates functions has attracted much attention in the field of chemical biology. In this account article, the development of novel chemical tools, which can degrade target proteins and carbohydrates by irradiation with a specific wavelength of light under mild conditions without any additives, is introduced. This novel class of photochemical agents promise bright prospects for finding not only molecular-targeted bioprobes for understanding of the structure-activity relationships of proteins and carbohydrates but also novel therapeutic drugs targeting proteins and carbohydrates.

Mining relational paths in integrated biomedical data.

PubMed

He, Bing; Tang, Jie; Ding, Ying; Wang, Huijun; Sun, Yuyin; Shin, Jae Hong; Chen, Bin; Moorthy, Ganesh; Qiu, Judy; Desai, Pankaj; Wild, David J

2011-01-01

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies.

Pathway analysis of high-throughput biological data within a Bayesian network framework.

PubMed

Isci, Senol; Ozturk, Cengizhan; Jones, Jon; Otu, Hasan H

2011-06-15

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC. Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

Cell biology of the Koji mold Aspergillus oryzae.

PubMed

Kitamoto, Katsuhiko

2015-01-01

Koji mold, Aspergillus oryzae, has been used for the production of sake, miso, and soy sauce for more than one thousand years in Japan. Due to the importance, A. oryzae has been designated as the national micro-organism of Japan (Koku-kin). A. oryzae has been intensively studied in the past century, with most investigations focusing on breeding techniques and developing methods for Koji making for sake brewing. However, the understanding of fundamental biology of A. oryzae remains relatively limited compared with the yeast Saccharomyces cerevisiae. Therefore, we have focused on studying the cell biology including live cell imaging of organelles, protein vesicular trafficking, autophagy, and Woronin body functions using the available genomic information. In this review, I describe essential findings of cell biology of A. oryzae obtained in our study for a quarter of century. Understanding of the basic biology will be critical for not its biotechnological application, but also for an understanding of the fundamental biology of other filamentous fungi.

Regulatory observations in bioanalytical determinations.

PubMed

Viswanathan, C T

2010-07-01

The concept of measuring analytes in biological media is a long-established area of the quantitative sciences that is employed in many sectors. While academic research and R&D units of private firms have been in the forefront of developing complex methodologies, it is the regulatory environment that has brought the focus and rigor to the quality control of the quantitative determination of drug concentration in biological samples. In this article, the author examines the regulatory findings discovered during the course of several years of auditing bioanalytical work. The outcomes of these findings underscore the importance of quality method validation to ensure the reliability of the data generated. The failure to ensure the reliability of these data can lead to potential risks in the health management of millions of people in the USA.

Controversy in Biology Classrooms—Citizen Science Approaches to Evolution and Applications to Climate Change Discussions

PubMed Central

Yoho, Rachel A.; Vanmali, Binaben H.

2016-01-01

The biological sciences encompass topics considered controversial by the American public, such as evolution and climate change. We believe that the development of climate change education in the biology classroom is better informed by an understanding of the history of the teaching of evolution. A common goal for science educators should be to engender a greater respect for and appreciation of science among students while teaching specific content knowledge. Citizen science has emerged as a viable yet underdeveloped method for engaging students of all ages in key scientific issues that impact society through authentic data-driven scientific research. Where successful, citizen science may open avenues of communication and engagement with the scientific process that would otherwise be more difficult to achieve. Citizen science projects demonstrate versatility in education and the ability to test hypotheses by collecting large amounts of often publishable data. We find a great possibility for science education research in the incorporation of citizen science projects in curriculum, especially with respect to “hot topics” of socioscientific debate based on our review of the findings of other authors. Journal of Microbiology & Biology Education PMID:27047604

Non-spherical micro- and nanoparticles: fabrication, characterization and drug delivery applications.

PubMed

Mathaes, Roman; Winter, Gerhard; Besheer, Ahmed; Engert, Julia

2015-03-01

Micro- and nanoparticles in drug and vaccine delivery have opened up new possibilities in pharmaceutics. In the past, researchers focused mainly on particle size, surface chemistry and the use of various materials to control particle characteristics and functions. Lately, shape has been acknowledged as an important design parameter having an impact on the interaction with biological systems. In this review, we report on the latest developments in fabrication methods to tailor particle geometry, summarize analytical techniques for non-spherical particles and highlight the most important findings regarding their interaction with biological systems and their potential applications in drug delivery. The impact of shape on particle internalization into different cell types and particle biodistribution has been extensively studied in the past. Current research focuses on shape-dependent uptake mechanisms and applications for tumour therapy and vaccination. Different fabrication methods can be used to produce a variety of different particle types and shapes. Key challenges will be the transfer of new non-spherical particle fabrication methods from lab-scale to industrial large-scale production. Not all techniques may be scalable for the production of high quantities of particles. It will also be challenging to transfer the promising in vitro findings to suitable in vivo models.

A synthetic biology approach to the development of transcriptional regulatory models and custom enhancer design☆,☆☆

PubMed Central

Martinez, Carlos A.; Barr, Kenneth; Kim, Ah-Ram; Reinitz, John

2013-01-01

Synthetic biology offers novel opportunities for elucidating transcriptional regulatory mechanisms and enhancer logic. Complex cis-regulatory sequences—like the ones driving expression of the Drosophila even-skipped gene—have proven difficult to design from existing knowledge, presumably due to the large number of protein-protein interactions needed to drive the correct expression patterns of genes in multicellular organisms. This work discusses two novel computational methods for the custom design of enhancers that employ a sophisticated, empirically validated transcriptional model, optimization algorithms, and synthetic biology. These synthetic elements have both utilitarian and academic value, including improving existing regulatory models as well as evolutionary questions. The first method involves the use of simulated annealing to explore the sequence space for synthetic enhancers whose expression output fit a given search criterion. The second method uses a novel optimization algorithm to find functionally accessible pathways between two enhancer sequences. These paths describe a set of mutations wherein the predicted expression pattern does not significantly vary at any point along the path. Both methods rely on a predictive mathematical framework that maps the enhancer sequence space to functional output. PMID:23732772

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PubMed Central

Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

PubMed

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

PubMed Central

2014-01-01

Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies. PMID:25350277

Bioinformatics for spermatogenesis: annotation of male reproduction based on proteomics

PubMed Central

Zhou, Tao; Zhou, Zuo-Min; Guo, Xue-Jiang

2013-01-01

Proteomics strategies have been widely used in the field of male reproduction, both in basic and clinical research. Bioinformatics methods are indispensable in proteomics-based studies and are used for data presentation, database construction and functional annotation. In the present review, we focus on the functional annotation of gene lists obtained through qualitative or quantitative methods, summarizing the common and male reproduction specialized proteomics databases. We introduce several integrated tools used to find the hidden biological significance from the data obtained. We further describe in detail the information on male reproduction derived from Gene Ontology analyses, pathway analyses and biomedical analyses. We provide an overview of bioinformatics annotations in spermatogenesis, from gene function to biological function and from biological function to clinical application. On the basis of recently published proteomics studies and associated data, we show that bioinformatics methods help us to discover drug targets for sperm motility and to scan for cancer-testis genes. In addition, we summarize the online resources relevant to male reproduction research for the exploration of the regulation of spermatogenesis. PMID:23852026

MONOMIALS AND BASIN CYLINDERS FOR NETWORK DYNAMICS.

PubMed

Austin, Daniel; Dinwoodie, Ian H

We describe methods to identify cylinder sets inside a basin of attraction for Boolean dynamics of biological networks. Such sets are used for designing regulatory interventions that make the system evolve towards a chosen attractor, for example initiating apoptosis in a cancer cell. We describe two algebraic methods for identifying cylinders inside a basin of attraction, one based on the Groebner fan that finds monomials that define cylinders and the other on primary decomposition. Both methods are applied to current examples of gene networks.

MONOMIALS AND BASIN CYLINDERS FOR NETWORK DYNAMICS

PubMed Central

AUSTIN, DANIEL; DINWOODIE, IAN H

2014-01-01

We describe methods to identify cylinder sets inside a basin of attraction for Boolean dynamics of biological networks. Such sets are used for designing regulatory interventions that make the system evolve towards a chosen attractor, for example initiating apoptosis in a cancer cell. We describe two algebraic methods for identifying cylinders inside a basin of attraction, one based on the Groebner fan that finds monomials that define cylinders and the other on primary decomposition. Both methods are applied to current examples of gene networks. PMID:25620893

Comparing conventional Descriptive Analysis and Napping®-UFP against physiochemical measurements: a case study using apples.

PubMed

Pickup, William; Bremer, Phil; Peng, Mei

2018-03-01

The extensive time and cost associated with conventional sensory profiling methods has spurred sensory researchers to develop rapid method alternatives, such as Napping® with Ultra-Flash Profiling (UFP). Napping®-UFP generates sensory maps by requiring untrained panellists to separate samples based on perceived sensory similarities. Evaluations of this method have been restrained to manufactured/formulated food models, and predominantly structured on comparisons against the conventional descriptive method. The present study aims to extend the validation of Napping®-UFP (N = 72) to natural biological products; and to evaluate this method against Descriptive Analysis (DA; N = 8) with physiochemical measurements as an additional evaluative criterion. The results revealed that sample configurations generated by DA and Napping®-UFP were not significantly correlated (RV = 0.425, P = 0.077); however, they were both correlated with the product map generated based on the instrumental measures (P < 0.05). The finding also noted that sample characterisations from DA and Napping®-UFP were driven by different sensory attributes, indicating potential structural differences between these two methods in configuring samples. Overall, these findings lent support for the extended use of Napping®-UFP for evaluations of natural biological products. Although DA was shown to be a better method for establishing sensory-instrumental relationships, Napping®-UFP exhibited strengths in generating informative sample configurations based on holistic perception of products. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A Comparison of Different Methods for Evaluating Diet, Physical Activity, and Long-Term Weight Gain in 3 Prospective Cohort Studies.

PubMed

Smith, Jessica D; Hou, Tao; Hu, Frank B; Rimm, Eric B; Spiegelman, Donna; Willett, Walter C; Mozaffarian, Dariush

2015-11-01

The insidious pace of long-term weight gain (∼ 1 lb/y or 0.45 kg/y) makes it difficult to study in trials; long-term prospective cohorts provide crucial evidence on its key contributors. Most previous studies have evaluated how prevalent lifestyle habits relate to future weight gain rather than to lifestyle changes, which may be more temporally and physiologically relevant. Our objective was to evaluate and compare different methodological approaches for investigating diet, physical activity (PA), and long-term weight gain. In 3 prospective cohorts (total n = 117,992), we assessed how lifestyle relates to long-term weight change (up to 24 y of follow-up) in 4-y periods by comparing 3 analytic approaches: 1) prevalent diet and PA and 4-y weight change (prevalent analysis); 2) 4-y changes in diet and PA with a 4-y weight change (change analysis); and 3) 4-y change in diet and PA with weight change in the subsequent 4 y (lagged-change analysis). We compared these approaches and evaluated the consistency across cohorts, magnitudes of associations, and biological plausibility of findings. Across the 3 methods, consistent, robust, and biologically plausible associations were seen only for the change analysis. Results for prevalent or lagged-change analyses were less consistent across cohorts, smaller in magnitude, and biologically implausible. For example, for each serving of a sugar-sweetened beverage, the observed weight gain was 0.01 lb (95% CI: -0.08, 0.10) [0.005 kg (95% CI: -0.04, 0.05)] based on prevalent analysis; 0.99 lb (95% CI: 0.83, 1.16) [0.45 kg (95% CI: 0.38, 0.53)] based on change analysis; and 0.05 lb (95% CI: -0.10, 0.21) [0.02 kg (95% CI: -0.05, 0.10)] based on lagged-change analysis. Findings were similar for other foods and PA. Robust, consistent, and biologically plausible relations between lifestyle and long-term weight gain are seen when evaluating lifestyle changes and weight changes in discrete periods rather than in prevalent lifestyle or lagged changes. These findings inform the optimal methods for evaluating lifestyle and long-term weight gain and the potential for bias when other methods are used. © 2015 American Society for Nutrition.

Purification and biological characterization of soluble, recombinant mouse IFNβ expressed in insect cells.

PubMed

Stifter, Sebastian A; Gould, Jodee A; Mangan, Niamh E; Reid, Hugh H; Rossjohn, Jamie; Hertzog, Paul J; de Weerd, Nicole A

2014-02-01

Interferon β (IFNβ) is a member of the type I interferon family of cytokines widely recognised for their anti-viral, anti-proliferative and immunomodulatory properties. Recombinant, biologically active forms of this cytokine are used clinically for the treatment of multiple sclerosis and in laboratories to study the role of this cytokine in health and disease. Established methods for expression of IFNβ utilise either bacterial systems from which the insoluble recombinant proteins must be refolded, or mammalian expression systems in which large volumes of cell culture are required for recovery of acceptable yields. Utilising the baculovirus expression system and Trichoplusia ni (Cabbage Looper) BTI-TN-5B1-4 cell line, we report a reproducible method for production and purification of milligram/litre quantities of biologically active murine IFNβ. Due to the design of our construct and the eukaryotic nature of insect cells, the resulting soluble protein is secreted allowing purification of the Histidine-tagged natively-folded protein from the culture supernatant. The IFNβ purification method described is a two-step process employing immobilised metal-ion affinity chromatography (IMAC) and reverse-phase high performance liquid chromatography (RP-HPLC) that results in production of significantly more purified IFNβ than any other reported eukaryotic-based expression system. Recombinant murine IFNβ produced by this method was natively folded and demonstrated hallmark type I interferon biological effects including antiviral and anti-proliferative activities, and induced genes characteristic of IFNβ activity in vivo. Recombinant IFNβ also had specific activity levels exceeding that of the commercially available equivalent. Together, our findings provide a method for production of highly pure, biologically active murine IFNβ. Copyright © 2013 Elsevier Inc. All rights reserved.

A simple and low-cost chip bonding solution for high pressure, high temperature and biological applications.

PubMed

Serra, M; Pereiro, I; Yamada, A; Viovy, J-L; Descroix, S; Ferraro, D

2017-02-14

The sealing of microfluidic devices remains a complex and time-consuming process requiring specific equipment and protocols: a universal method is thus highly desirable. We propose here the use of a commercially available sealing tape as a robust, versatile, reversible solution, compatible with cell and molecular biology protocols, and requiring only the application of manually achievable pressures. The performance of the seal was tested with regards to the most commonly used chip materials. For most materials, the bonding resisted 5 bars at room temperature and 1 bar at 95 °C. This method should find numerous uses, ranging from fast prototyping in the laboratory to implementation in low technology environments or industrial production.

An ontology-based search engine for protein-protein interactions

PubMed Central

2010-01-01

Background Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. Results We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Conclusion Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology. PMID:20122195

An ontology-based search engine for protein-protein interactions.

PubMed

Park, Byungkyu; Han, Kyungsook

2010-01-18

Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

Findings

MedlinePlus

... Issue All Issues Explore Findings by Topic Cell Biology Cellular Structures, Functions, Processes, Imaging, Stress Response Chemistry ... Glycobiology, Synthesis, Natural Products, Chemical Reactions Computers in Biology Bioinformatics, Modeling, Systems Biology, Data Visualization Diseases Cancer, ...

The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods.

PubMed

Gabanyi, Margaret J; Adams, Paul D; Arnold, Konstantin; Bordoli, Lorenza; Carter, Lester G; Flippen-Andersen, Judith; Gifford, Lida; Haas, Juergen; Kouranov, Andrei; McLaughlin, William A; Micallef, David I; Minor, Wladek; Shah, Raship; Schwede, Torsten; Tao, Yi-Ping; Westbrook, John D; Zimmerman, Matthew; Berman, Helen M

2011-07-01

The Protein Structure Initiative's Structural Biology Knowledgebase (SBKB, URL: http://sbkb.org ) is an open web resource designed to turn the products of the structural genomics and structural biology efforts into knowledge that can be used by the biological community to understand living systems and disease. Here we will present examples on how to use the SBKB to enable biological research. For example, a protein sequence or Protein Data Bank (PDB) structure ID search will provide a list of related protein structures in the PDB, associated biological descriptions (annotations), homology models, structural genomics protein target status, experimental protocols, and the ability to order available DNA clones from the PSI:Biology-Materials Repository. A text search will find publication and technology reports resulting from the PSI's high-throughput research efforts. Web tools that aid in research, including a system that accepts protein structure requests from the community, will also be described. Created in collaboration with the Nature Publishing Group, the Structural Biology Knowledgebase monthly update also provides a research library, editorials about new research advances, news, and an events calendar to present a broader view of structural genomics and structural biology.

Prioritizing conservation activities using reserve site selection methods and population viability analysis.

PubMed

Newbold, Stephen C; Siikamäki, Juha

2009-10-01

In recent years a large literature on reserve site selection (RSS) has developed at the interface between ecology, operations research, and environmental economics. Reserve site selection models use numerical optimization techniques to select sites for a network of nature reserves for protecting biodiversity. In this paper, we develop a population viability analysis (PVA) model for salmon and incorporate it into an RSS framework for prioritizing conservation activities in upstream watersheds. We use spawner return data for three closely related salmon stocks in the upper Columbia River basin and estimates of the economic costs of watershed protection from NOAA to illustrate the framework. We compare the relative cost-effectiveness of five alternative watershed prioritization methods, based on various combinations of biological and economic information. Prioritization based on biological benefit-economic cost comparisons and accounting for spatial interdependencies among watersheds substantially outperforms other more heuristic methods. When using this best-performing prioritization method, spending 10% of the cost of protecting all upstream watersheds yields 79% of the biological benefits (increase in stock persistence) from protecting all watersheds, compared to between 20% and 64% for the alternative methods. We also find that prioritization based on either costs or benefits alone can lead to severe reductions in cost-effectiveness.

The Shortlist Method for fast computation of the Earth Mover's Distance and finding optimal solutions to transportation problems.

PubMed

Gottschlich, Carsten; Schuhmacher, Dominic

2014-01-01

Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method.

The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems

PubMed Central

Gottschlich, Carsten; Schuhmacher, Dominic

2014-01-01

Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method. PMID:25310106

Synthesis, biological targeting and photophysics of quantum dots

NASA Astrophysics Data System (ADS)

Clarke, Samuel Jon

Quantum dots (QDs) are inorganic nanoparticles that have exceptional optical properties. Currently, QDs have failed to reach their potential as fluorescent probes in live cells, due to the nontrivial requirements for biological interfacing. The goal of this thesis is to address technical hurdles related to the reproducible synthesis of QDs, strategies for the specific targeting of QDs to biological cells and to understanding and exploitation of the photophysical properties. High quality QDs of varying composition (CdSe, CdTe and core/shell CdSe/ZnS) were synthesized with an organometallic method. To prepare biocompatible QDs, three strategies were used. The simplest strategy used small mercaptocarboxylic acids, while performance improvements were realized with engineered-peptide and lipid-micelle coatings. For specific biological targeting of the QDs, conjugation strategies were devised to attach biomolecules, while spectroscopic characterization methods were developed to assess conjugation efficiencies. To target gram-negative bacterial cells, an electrostatic self-assembly method was used to attach an antibiotic selective for this class of bacteria, polymyxin B. To target dopamine neurotransmitter receptor, a covalent conjugation method was used to attach dopamine, the endogenous ligand for that receptor. It was shown that dopamine molecule enabled electron transfer to QDs and the photophysics was studied in detail. A novel conjugation and targeting strategy was explored to enable the selective binding of QDs to polyhistidine epitopes on membrane proteins. Epifluorescence microscopy was used to evaluate the biological activity of the three QD probes. Combined, they add to the QD 'toolkit' for live-cell imaging. Finally, due to its negative implications in biological imaging, the fluorescent intermittency (blinking) of CdTe QDs was investigated. It was shown that mercaptocarboxylic acids contribute to the blinking suppression of the QDs, results that may aid in the design of nonblinking QDs. Overall, these findings should be useful in the future design of QDs for biological imaging and biosensing applications.

Method for Finding Metabolic Properties Based on the General Growth Law. Liver Examples. A General Framework for Biological Modeling

PubMed Central

Shestopaloff, Yuri K.

2014-01-01

We propose a method for finding metabolic parameters of cells, organs and whole organisms, which is based on the earlier discovered general growth law. Based on the obtained results and analysis of available biological models, we propose a general framework for modeling biological phenomena and discuss how it can be used in Virtual Liver Network project. The foundational idea of the study is that growth of cells, organs, systems and whole organisms, besides biomolecular machinery, is influenced by biophysical mechanisms acting at different scale levels. In particular, the general growth law uniquely defines distribution of nutritional resources between maintenance needs and biomass synthesis at each phase of growth and at each scale level. We exemplify the approach considering metabolic properties of growing human and dog livers and liver transplants. A procedure for verification of obtained results has been introduced too. We found that two examined dogs have high metabolic rates consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per day, and verified this using the proposed verification procedure. We also evaluated consumption rate of nutrients in human livers, determining it to be about 0.088 gram of nutrients per cubic centimeter of liver per day for males, and about 0.098 for females. This noticeable difference can be explained by evolutionary development, which required females to have greater liver processing capacity to support pregnancy. We also found how much nutrients go to biomass synthesis and maintenance at each phase of liver and liver transplant growth. Obtained results demonstrate that the proposed approach can be used for finding metabolic characteristics of cells, organs, and whole organisms, which can further serve as important inputs and constraints for many applications in biology (such as protein expression), biotechnology (synthesis of substances), and medicine. PMID:24940740

Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules.

PubMed

Wassermann, Anne Mai; Lounkine, Eugen; Glick, Meir

2013-03-25

Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.

76 FR 8708 - Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-15

...] Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological Control Agent... are advising the public that a final environmental assessment and finding of no significant impact... review and analysis of environmental impacts associated with the proposed biological control program...

Using Test Data to Find Misconceptions in Secondary Science

ERIC Educational Resources Information Center

Fuchs, Travis T.; Arsenault, Mike

2017-01-01

Students, as well as teachers, often learn what makes sense to them, even when it is wrong. These misconceptions are a problem. The authors sought a quick, quantitative way of identifying student misconceptions in secondary science. Using the University of Toronto's National Biology Competition test data, this article presents a method of quickly…

Biological Aerosol Test Method and Personal Protective Equipment (PPE) Decon

DTIC Science & Technology

2011-05-01

supply to the porous tube diluter. This stops all air into the LSAT. 6. Power off the vacuum pump and the compressed air supply. 22 Distribution...Experiment from Template from the menu. 10. Scroll down the template list until you find APHL Flu Assay 04272009. 11. Highlight the test, then click

2nd Congress on applied synthetic biology in Europe (Málaga, Spain, November 2013).

PubMed

Vetter, Beatrice V; Pantidos, Nikolaos; Edmundson, Matthew

2014-05-25

The second meeting organised by the EFB on the advances of applied synthetic biology in Europe was held in Málaga, Spain in November 2013. The potential for the broad application of synthetic biology was reflected in the five sessions of this meeting: synthetic biology for healthcare applications, tools and technologies for synthetic biology, production of recombinant proteins, synthetic plant biology, and biofuels and other small molecules. Outcomes from the meeting were that synthetic biology offers methods for rapid development of new strains that will result in decreased production costs, sustainable chemical production and new medical applications. Additionally, it also introduced novel ways to produce sustainable energy and biofuels, to find new alternatives for bioremediation and resource recovery, and environmentally friendly foodstuff production. All the above-mentioned advances could enable biotechnology to solve some of the major problems of Society. However, while there are still limitations in terms of lacking tools, standardisation and suitable host organisms, this meeting has laid a foundation providing cutting-edge concepts and techniques to ultimately convert the potential of synthetic biology into practice. Copyright © 2014. Published by Elsevier B.V. All rights reserved.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks.

PubMed

He, Jieyue; Wang, Chunyan; Qiu, Kunpu; Zhong, Wei

2014-01-01

Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies.

Cross-Linking and Mass Spectrometry Methodologies to Facilitate Structural Biology: Finding a Path through the Maze

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merkley, Eric D.; Cort, John R.; Adkins, Joshua N.

2013-09-01

Multiprotein complexes, rather than individual proteins, make up a large part of the biological macromolecular machinery of a cell. Understanding the structure and organization of these complexes is critical to understanding cellular function. Chemical cross-linking coupled with mass spectrometry is emerging as a complementary technique to traditional structural biology methods and can provide low-resolution structural information for a multitude of purposes, such as distance constraints in computational modeling of protein complexes. In this review, we discuss the experimental considerations for successful application of chemical cross-linking-mass spectrometry in biological studies and highlight three examples of such studies from the recent literature.more » These examples (as well as many others) illustrate the utility of a chemical cross-linking-mass spectrometry approach in facilitating structural analysis of large and challenging complexes.« less

Corono-radicular biological restoration of maxillary central incisors by direct method.

PubMed

Aggarwal, Sonia; Sahoo, Sujit Ranjan; Pandharkar, Kartik

2014-11-01

This case report refers to the esthetic and functional restorations of extensively damaged maxillary central incisors with dental caries in a 32-year-old woman, with the use of posts and crowns made from natural extracted teeth. Proper restoration of such teeth with the use of natural teeth fragments are known as "biological restoration." Biological restorations can be done by using the fragments of the patients own tooth and if that is not available, tooth fragment can be obtained from an extracted tooth. These biological posts and crowns present a low cost option and an alternative technique for the morphofunctional recovery of extensively damaged teeth. There are limitations with the use of natural extracted teeth (homogenous bonding) for restoration such as the difficulty of finding teeth with a similar color and shape as that of the destroyed element, or patient may refuse to accept a tooth fragment from another patient, which prevents execution of the restoration.

Omics Profiling in Precision Oncology*

PubMed Central

Yu, Kun-Hsing; Snyder, Michael

2016-01-01

Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology. PMID:27099341

Inferring cardiac phase response curve in vivo

NASA Astrophysics Data System (ADS)

Pikovsky, Arkady; Kralemann, Bjoern; Fruehwirth, Matthias; Rosenblum, Michael; Kenner, Thomas; Schaefer, Jochen; Moser, Maximilian

2014-03-01

Characterizing properties of biological oscillators with phase response cirves (PRC) is one of main theoretical tools in neuroscience, cardio-respiratory physiology, and chronobiology. We present a technique that allows the extraction of the PRC from a non-invasive observation of a system consisting of two interacting oscillators, in this case heartbeat and respiration, in its natural environment and under free-running conditions. We use this method to obtain the phase coupling functions describing cardio-respiratory interactions and the phase response curve of 17 healthy humans. We show at which phase the cardiac beat is susceptible to respiratory drive and extract the respiratory-related component of heart rate variability. This non-invasive method of bivariate data analysis for the determination of phase response curves of coupled oscillators may find application in other biological and physical systems.

Exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data.

PubMed

Vinciotti, Veronica; Liu, Xiaohui; Turk, Rolf; de Meijer, Emile J; 't Hoen, Peter A C

2006-04-03

The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other. We validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections. The temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.

Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data

PubMed Central

Vinciotti, Veronica; Liu, Xiaohui; Turk, Rolf; de Meijer, Emile J; 't Hoen, Peter AC

2006-01-01

Background The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other. Results We validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections. Conclusion The temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523. PMID:16584545

Partitioning of functional gene expression data using principal points.

PubMed

Kim, Jaehee; Kim, Haseong

2017-10-12

DNA microarrays offer motivation and hope for the simultaneous study of variations in multiple genes. Gene expression is a temporal process that allows variations in expression levels with a characterized gene function over a period of time. Temporal gene expression curves can be treated as functional data since they are considered as independent realizations of a stochastic process. This process requires appropriate models to identify patterns of gene functions. The partitioning of the functional data can find homogeneous subgroups of entities for the massive genes within the inherent biological networks. Therefor it can be a useful technique for the analysis of time-course gene expression data. We propose a new self-consistent partitioning method of functional coefficients for individual expression profiles based on the orthonormal basis system. A principal points based functional partitioning method is proposed for time-course gene expression data. The method explores the relationship between genes using Legendre coefficients as principal points to extract the features of gene functions. Our proposed method provides high connectivity in connectedness after clustering for simulated data and finds a significant subsets of genes with the increased connectivity. Our approach has comparative advantages that fewer coefficients are used from the functional data and self-consistency of principal points for partitioning. As real data applications, we are able to find partitioned genes through the gene expressions found in budding yeast data and Escherichia coli data. The proposed method benefitted from the use of principal points, dimension reduction, and choice of orthogonal basis system as well as provides appropriately connected genes in the resulting subsets. We illustrate our method by applying with each set of cell-cycle-regulated time-course yeast genes and E. coli genes. The proposed method is able to identify highly connected genes and to explore the complex dynamics of biological systems in functional genomics.

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Pal, Ranadip; Ivanov, Ivan; Bittner, Michael; Dougherty, Edward R

2004-11-22

We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

Ludwig von Bertalanffy's Organismic View on the Theory of Evolution

PubMed Central

Drack, Manfred

2015-01-01

Ludwig von Bertalanffy was a key figure in the advancement of theoretical biology. His early considerations already led him to recognize the necessity of considering the organism as a system, as an organization of parts and processes. He termed the resulting research program organismic biology, which he extended to all basic questions of biology and almost all areas of biology, hence also to the theory of evolution. This article begins by outlining the rather unknown (because often written in German) research of Bertalanffy in the field of theoretical biology. The basics of the organismic approach are then described. This is followed by Bertalanffy's considerations on the theory of evolution, in which he used methods from theoretical biology and then introduced his own, organismic, view on evolution, leading to the demand for finding laws of evolution. Finally, his view on the concept of homology is presented. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 77–90, 2015. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals, Inc. PMID:25727202

Quantifying pCO2 in biological ocean acidification experiments: A comparison of four methods.

PubMed

Watson, Sue-Ann; Fabricius, Katharina E; Munday, Philip L

2017-01-01

Quantifying the amount of carbon dioxide (CO2) in seawater is an essential component of ocean acidification research; however, equipment for measuring CO2 directly can be costly and involve complex, bulky apparatus. Consequently, other parameters of the carbonate system, such as pH and total alkalinity (AT), are often measured and used to calculate the partial pressure of CO2 (pCO2) in seawater, especially in biological CO2-manipulation studies, including large ecological experiments and those conducted at field sites. Here we compare four methods of pCO2 determination that have been used in biological ocean acidification experiments: 1) Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) measurement of dissolved inorganic carbon (CT) and AT, 2) spectrophotometric measurement of pHT and AT, 3) electrode measurement of pHNBS and AT, and 4) the direct measurement of CO2 using a portable CO2 equilibrator with a non-dispersive infrared (NDIR) gas analyser. In this study, we found these four methods can produce very similar pCO2 estimates, and the three methods often suited to field-based application (spectrophotometric pHT, electrode pHNBS and CO2 equilibrator) produced estimated measurement uncertainties of 3.5-4.6% for pCO2. Importantly, we are not advocating the replacement of established methods to measure seawater carbonate chemistry, particularly for high-accuracy quantification of carbonate parameters in seawater such as open ocean chemistry, for real-time measures of ocean change, nor for the measurement of small changes in seawater pCO2. However, for biological CO2-manipulation experiments measuring differences of over 100 μatm pCO2 among treatments, we find the four methods described here can produce similar results with careful use.

Quantifying pCO2 in biological ocean acidification experiments: A comparison of four methods

PubMed Central

Fabricius, Katharina E.; Munday, Philip L.

2017-01-01

Quantifying the amount of carbon dioxide (CO2) in seawater is an essential component of ocean acidification research; however, equipment for measuring CO2 directly can be costly and involve complex, bulky apparatus. Consequently, other parameters of the carbonate system, such as pH and total alkalinity (AT), are often measured and used to calculate the partial pressure of CO2 (pCO2) in seawater, especially in biological CO2-manipulation studies, including large ecological experiments and those conducted at field sites. Here we compare four methods of pCO2 determination that have been used in biological ocean acidification experiments: 1) Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) measurement of dissolved inorganic carbon (CT) and AT, 2) spectrophotometric measurement of pHT and AT, 3) electrode measurement of pHNBS and AT, and 4) the direct measurement of CO2 using a portable CO2 equilibrator with a non-dispersive infrared (NDIR) gas analyser. In this study, we found these four methods can produce very similar pCO2 estimates, and the three methods often suited to field-based application (spectrophotometric pHT, electrode pHNBS and CO2 equilibrator) produced estimated measurement uncertainties of 3.5–4.6% for pCO2. Importantly, we are not advocating the replacement of established methods to measure seawater carbonate chemistry, particularly for high-accuracy quantification of carbonate parameters in seawater such as open ocean chemistry, for real-time measures of ocean change, nor for the measurement of small changes in seawater pCO2. However, for biological CO2-manipulation experiments measuring differences of over 100 μatm pCO2 among treatments, we find the four methods described here can produce similar results with careful use. PMID:28957378

Study Finds Association between Biological Marker and Susceptibility to the Common Cold

MedlinePlus

... W X Y Z Study Finds Association Between Biological Marker and Susceptibility to the Common Cold Share: © ... a cold caused by a particular rhinovirus. The biological marker identified in the study was the length ...

Ozone dosing alters the biological potential and therapeutic outcomes of plasma rich in growth factors.

PubMed

Anitua, E; Zalduendo, M M; Troya, M; Orive, G

2015-04-01

Until now, ozone has been used in a rather empirical way. This in-vitro study investigates, for the first time, whether different ozone treatments of plasma rich in growth factors (PRGF) alter the biological properties and outcomes of this autologous platelet-rich plasma. Human plasma rich in growth factors was treated with ozone using one of the following protocols: a continuous-flow method; or a syringe method in which constant volumes of ozone and PRGF were mixed. In both cases, ozone was added before, during and after the addition of calcium chloride. Three ozone concentrations, of the therapeutic range 20, 40 and 80 μg/mL, were tested. Fibrin clot properties, growth factor content and the proliferative effect on primary osteoblasts and gingival fibroblasts were evaluated. Ozone treatment of PRGF using the continuous flow protocol impaired formation of the fibrin scaffold, drastically reduced the levels of growth factors and significantly decreased the proliferative potential of PRGF on primary osteoblasts and gingival fibroblasts. In contrast, treatment of PRGF with ozone using the syringe method, before, during and after the coagulation process, did not alter the biological outcomes of the autologous therapy. These findings suggest that ozone dose and the way that ozone combines with PRGF may alter the biological potential and therapeutic outcomes of PRGF. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

THE CAUSAL ANALYSIS / DIAGNOSIS DECISION ...

EPA Pesticide Factsheets

CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based on the US EPA's Stressor Identification process which is a formal method for identifying causes of impairments in aquatic systems. CADDIS 2007 increases access to relevant information useful for causal analysis and provides methods and tools that practitioners can use to analyze their own data. The new Candidate Cause section provides overviews of commonly encountered causes of impairments to aquatic systems: metals, sediments, nutrients, flow alteration, temperature, ionic strength, and low dissolved oxygen. CADDIS includes new Conceptual Models that illustrate the relationships from sources to stressors to biological effects. An Interactive Conceptual Model for phosphorus links the diagram with supporting literature citations. The new Analyzing Data section helps practitioners analyze their data sets and interpret and use those results as evidence within the USEPA causal assessment process. Downloadable tools include a graphical user interface statistical package (CADStat), and programs for use with the freeware R statistical package, and a Microsoft Excel template. These tools can be used to quantify associations between causes and biological impairments using innovative methods such as species-sensitivity distributions, biological inferenc

Students' Personal Connection with Science: Investigating the Multidimensional Phenomenological Structure of Self-Relevance

ERIC Educational Resources Information Center

Hartwell, Matthew; Kaplan, Avi

2018-01-01

This paper presents findings from a two-phase mixed methods study investigating the phenomenological structure of self-relevance among ninth-grade junior high school biology students (Phase 1: N = 118; Phase 2: N = 139). We begin with a phenomenological multidimensional definition of self-relevance as comprising three dimensions: the academic…

Intelligence Community Forum

DTIC Science & Technology

2008-11-05

Description Operationally Feasible? EEG ms ms cm Measures electrical activity in the brain. Practical tool for applications - real time monitoring or...Cognitive Systems Device Development & Processing Methods Brain activity can be monitored in real-time in operational environments with EEG Brain...biological and cognitive findings about the user to customize the learning environment Neurofeedback • Present the user with real-time feedback

RECENT DEVELOPMENTS IN ELECTROCHEMICAL SENSORS FOR THE DETECTION OF NEUROTRANSMITTERS FOR APPLICATIONS IN BIOMEDICINE

PubMed Central

Özel, Rıfat Emrah; Hayat, Akhtar; Andreescu, Silvana

2015-01-01

Neurotransmitters are important biological molecules that are essential to many neurophysiological processes including memory, cognition, and behavioral states. The development of analytical methodologies to accurately detect neurotransmitters is of great importance in neurological and biological research. Specifically designed microelectrodes or microbiosensors have demonstrated potential for rapid, real-time measurements with high spatial resolution. Such devices can facilitate study of the role and mechanism of action of neurotransmitters and can find potential uses in biomedicine. This paper reviews the current status and recent advances in the development and application of electrochemical sensors for the detection of small-molecule neurotransmitters. Measurement challenges and opportunities of electroanalytical methods to advance study and understanding of neurotransmitters in various biological models and disease conditions are discussed. PMID:26973348

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies

PubMed Central

2013-01-01

Background The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the “correlation” of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. Results Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method’s performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. Conclusions The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network. PMID:23432934

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis.

PubMed

Nikdel, Ali; Braatz, Richard D; Budman, Hector M

2018-05-01

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).

Structure and Bonding in Heme-Nitrosyl Complexes and Implications for Biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehnert, Nicolai; Scheidt, W. Robert; Wolf, Matthew W.

This review summarizes our current understanding of the geometric and electronic structures of ferrous and ferric heme–nitrosyls, which are of key importance for the biological functions and transformations of NO. In-depth correlations are made between these properties and the reactivities of these species. Here, a focus is put on the discoveries that have been made in the last 10 years, but previous findings are also included as necessary. Besides this, ferrous heme–nitroxyl complexes are also considered, which have become of increasing interest recently due to their roles as intermediates in NO and multiheme nitrite reductases, and because of the potentialmore » role of HNO as a signaling molecule in mammals. In recent years, computational methods have received more attention as a means of investigating enzyme reaction mechanisms, and some important findings from these theoretical studies are also highlighted in this chapter.« less

Beyond the PhD: Putting the Right Tools in Your Research Toolbox

PubMed Central

Downs, Charles A.; Morrison, Helena W.

2013-01-01

Postdoctoral training is vital to a successful career for nurse researchers with a biological or biobehavioral focus. Such training provides structured time to devote to gaining substantive knowledge, expanding one’s biological-methods repertoire, and writing grants. However, for unknown reasons, relatively few nurses pursue postdoctoral training. A few plausible explanations include a near critical shortage of nursing faculty coupled with an aging population in need of health care, a lack of available mentoring for predoctoral students to pursue postdoctoral training, and the difficulty of navigating the process of finding and choosing the right match for a postdoctoral experience. The purposes of this article are to provide a rationale for choosing postdoctoral training, review common fellowship opportunities, and discuss the process of finding and choosing the right match for postdoctoral training. The authors provide two prospective plans for postdoctoral training and include a plan for staying on track during the postdoctoral experience. PMID:20026452

Beyond the PhD: putting the right tools in your research toolbox.

PubMed

Downs, Charles A; Morrison, Helena W

2011-01-01

Postdoctoral training is vital to a successful career for nurse researchers with a biological or biobehavioral focus. Such training provides structured time to devote to gaining substantive knowledge, expanding one's biological-methods repertoire, and writing grants. However, for unknown reasons, relatively few nurses pursue postdoctoral training. A few plausible explanations include a near critical shortage of nursing faculty coupled with an aging population in need of health care, a lack of available mentoring for predoctoral students to pursue postdoctoral training, and the difficulty of navigating the process of finding and choosing the right match for a postdoctoral experience. The purposes of this article are to provide a rationale for choosing postdoctoral training, review common fellowship opportunities, and discuss the process of finding and choosing the right match for postdoctoral training. The authors provide two prospective plans for postdoctoral training and include a plan for staying on track during the postdoctoral experience.

Compass: a hybrid method for clinical and biobank data mining.

PubMed

Krysiak-Baltyn, K; Nordahl Petersen, T; Audouze, K; Jørgensen, Niels; Angquist, L; Brunak, S

2014-02-01

We describe a new method for identification of confident associations within large clinical data sets. The method is a hybrid of two existing methods; Self-Organizing Maps and Association Mining. We utilize Self-Organizing Maps as the initial step to reduce the search space, and then apply Association Mining in order to find association rules. We demonstrate that this procedure has a number of advantages compared to traditional Association Mining; it allows for handling numerical variables without a priori binning and is able to generate variable groups which act as "hotspots" for statistically significant associations. We showcase the method on infertility-related data from Danish military conscripts. The clinical data we analyzed contained both categorical type questionnaire data and continuous variables generated from biological measurements, including missing values. From this data set, we successfully generated a number of interesting association rules, which relate an observation with a specific consequence and the p-value for that finding. Additionally, we demonstrate that the method can be used on non-clinical data containing chemical-disease associations in order to find associations between different phenotypes, such as prostate cancer and breast cancer. Copyright © 2013 Elsevier Inc. All rights reserved.

Ketosteroid Standardized Cissus quadrangularis L. Extract and its Anabolic Activity: Time to Look Beyond Ketosteroid?

PubMed

Jadhav, Atul N; Rafiq, Mohammed; Devanathan, Rajendran; Azeemuddin, Mohammed; Anturlikar, Suryakanth D; Ahmed, Akhil; Sundaram, Ramchandran; Babu, U V; Paramesh, Rangesh

2016-05-01

Cissus quadrangularis (CQ) L. reported to contain 3-ketosteroids and have bone health benefits. This study aimed at establishing the relationship between the ketosteroid content and anabolic as well as bone health-promoting activities of various Cissus extracts in well-established orchidectomized (ORX) rat model. Supercritical carbon dioxide, ethyl acetate, and aqueous extracts (AE) of CQ L. were prepared and standardized for ketosteroid content by two methods used in commerce. Moreover, ketosteroid standardized extracts of this plant were evaluated for anabolic activity in rats in well-established ORX rat model. The increase in the absolute weight was appreciable in the CQ-AE treated group. Similarly, with respect to bone parameters, a similar trend was seen. The mean bone density, strength, and calcium content were found to be highest in the group treated with CQ-AE compared to groups treated with other extracts. This study reveals for the first time that 3-ketosteroids are not linked to the beneficial activities on bone and highlights the need for extensive characterization of biological active principles from CQ L. In light of the above estimation studies, we believe that current standardization of Cissus extraction "3-ketosteroids" is incorrect. We also did not find any report suggesting the presence of androgenic steroids in this plant and hence the characterization based on "3-ketosteroids" is scientifically incorrect. This study highlights the insufficient understanding of biological active principles from CQ L. and underlines the need for extensive bioactivity guided studies. Cissus quadrangularis (CQ) L. reported to contain 3.ketosteroids and have bone health benefitsWe did not find correlation between ketosteroid content obtained by conventional methods and its biological effectStudies indicate that claims of ketosteroid content need not necessarily correlate to biological effects and hence warrants extensive phytochemical characterization of biological active principles from CQ L. Abbreviations used: CQ: Cissus quadrangularis, ORX: Orchidectomized, AE: Aqueous extract, EE: Ethyl acetate extract, SFE: Supercritical fluid extract.

Getting the most out of RNA-seq data analysis.

PubMed

Khang, Tsung Fei; Lau, Ching Yee

2015-01-01

Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods'. Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.

CAZyme discovery and design for sweet dreams.

PubMed

André, Isabelle; Potocki-Véronèse, Gabrielle; Barbe, Sophie; Moulis, Claire; Remaud-Siméon, Magali

2014-04-01

Development of synthetic routes to complex carbohydrates and glyco-conjugates is often hampered by the lack of enzymes with requisite properties or specificities. Indeed, assembly or degradation of carbohydrates requires carbohydrate-active enzymes (CAZymes) able to act on a vast range of glycosidic monomers, oligomers or polymers in a regio-specific or stereo-specific manner in order to produce the desired structure. Sequence-based analyses allow finding the most original enzymes. Novel screening methods have emerged that enable a more efficient exploitation of the CAZyme diversity found in the microbial world or generated by protein engineering. Computational biology methods also play a prominent role in the success of CAZyme design. Such progress allows circumventing current limitations of carbohydrate synthesis and opens new opportunities related to the synthetic biology field. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessing biological invasions in European Seas: Biological traits of the most widespread non-indigenous species

NASA Astrophysics Data System (ADS)

Cardeccia, Alice; Marchini, Agnese; Occhipinti-Ambrogi, Anna; Galil, Bella; Gollasch, Stephan; Minchin, Dan; Narščius, Aleksas; Olenin, Sergej; Ojaveer, Henn

2018-02-01

The biological traits of the sixty-eight most widespread multicellular non-indigenous species (MWNIS) in European Seas: Baltic Sea, Western European Margin of the Atlantic Ocean and the Mediterranean Sea were examined. Data for nine biological traits was analyzed, and a total of 41 separate categories were used to describe the biological and ecological functions of these NIS. Our findings show that high dispersal ability, high reproductive rate and ecological generalization are the biological traits commonly associated with MWNIS. The functional groups that describe most of the 68 MWNIS are: photoautotrophic, zoobenthic (both sessile and motile) and nektonic predatory species. However, these 'most widespread' species comprise a wide range of taxa and biological trait profiles; thereby a clear "identikit of a perfect invader" for marine and brackish environments is difficult to define. Some traits, for example: "life form", "feeding method" and "mobility", feature multiple behaviours and strategies. Even species introduced by a single pathway, e.g. vessels, feature diverse biological trait profiles. MWNIS likely to impact community organization, structure and diversity are often associated with brackish environments. For many traits ("life form", "sociability", "reproductive type", "reproductive frequency", "haploid and diploid dispersal" and "mobility"), the categories mostly expressed by the impact-causing MWNIS do not differ substantially from the whole set of MWNIS.

Development of ultrasound bioprobe for biological imaging

PubMed Central

Shekhawat, Gajendra S.; Dudek, Steven M.; Dravid, Vinayak P.

2017-01-01

We report the development of an ultrasound bioprobe for in vitro molecular imaging. In this method, the phase of the scattered ultrasound wave is mapped to provide in vitro and intracellular imaging with nanometer-scale resolution under physiological conditions. We demonstrated the technique by successfully imaging a magnetic core in silica core shells and the stiffness image of intracellular fibers in endothelial cells that were stimulated with thrombin. The findings demonstrate a significant advancement in high-resolution ultrasound imaging of biological systems with acoustics under physiological conditions. These will open up various applications in biomedical and molecular imaging with subsurface resolution down to the nanometer scale. PMID:29075667

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

PubMed

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

Effects of Analytical and Holistic Scoring Patterns on Scorer Reliability in Biology Essay Tests

ERIC Educational Resources Information Center

Ebuoh, Casmir N.

2018-01-01

Literature revealed that the patterns/methods of scoring essay tests had been criticized for not being reliable and this unreliability is more likely to be more in internal examinations than in the external examinations. The purpose of this study is to find out the effects of analytical and holistic scoring patterns on scorer reliability in…

Pattern Discovery in Biomolecular Data – Tools, Techniques, and Applications | Center for Cancer Research

Cancer.gov

Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. This book provides a summary of principal techniques. Each chapter describes techniques that are drawn from many fields, including graph

Better bioinformatics through usability analysis.

PubMed

Bolchini, Davide; Finkelstein, Anthony; Perrone, Vito; Nagl, Sylvia

2009-02-01

Improving the usability of bioinformatics resources enables researchers to find, interact with, share, compare and manipulate important information more effectively and efficiently. It thus enables researchers to gain improved insights into biological processes with the potential, ultimately, of yielding new scientific results. Usability 'barriers' can pose significant obstacles to a satisfactory user experience and force researchers to spend unnecessary time and effort to complete their tasks. The number of online biological databases available is growing and there is an expanding community of diverse users. In this context there is an increasing need to ensure the highest standards of usability. Using 'state-of-the-art' usability evaluation methods, we have identified and characterized a sample of usability issues potentially relevant to web bioinformatics resources, in general. These specifically concern the design of the navigation and search mechanisms available to the user. The usability issues we have discovered in our substantial case studies are undermining the ability of users to find the information they need in their daily research activities. In addition to characterizing these issues, specific recommendations for improvements are proposed leveraging proven practices from web and usability engineering. The methods and approach we exemplify can be readily adopted by the developers of bioinformatics resources.

Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows.

PubMed

Bordel, Sergio

2018-04-13

In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments.

Biological activity analysis of native and recombinant streptokinase using clot lysis and chromogenic substrate assay.

PubMed

Mahboubi, Arash; Sadjady, Seyyed Kazem; Mirzaei Saleh Abadi, Mohammad; Azadi, Saeed; Solaimanian, Roya

2012-01-01

DETERMINATION OF STREPTOKINASE ACTIVITY IS USUALLY ACCOMPLISHED THROUGH TWO ASSAY METHODS: a) Clot lysis, b) Chromogenic substrate assay. In this study the biological activity of two streptokinase products, namely Streptase®, which is a native product and Heberkinasa®, which is a recombinant product, was determined against the third international reference standard using the two forementioned assay methods. The results indicated that whilst the activity of Streptase® was found to be 101 ± 4% and 97 ± 5% of the label claim with Clot lysis and Chromogenic substrate assay respectively, for Heberkinasa® the potency values obtained were 42 ± 5% and 92.5 ± 2% of the label claim respectively. To shed some light on the reason for this finding, the n-terminal sequence of the streptokinase molecules present in the two products was determined. The results showed slight differences in the amino acid sequence of the recombinant product in comparison to the native one at the amino terminus. This finding supports those of other workers who found that n-terminal sequence of the streptokinase molecule can have significant effect on the activity of this protein.

Air pollution and allergic diseases

PubMed Central

Brandt, Eric B.; Biagini Myers, Jocelyn M.; Ryan, Patrick H.; Khurana Hershey, Gurjit K.

2015-01-01

Purpose of review Exposure to traffic-related air pollutants (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant because increasingly large segments of the population worldwide reside in zones that have high levels of TRAP (1), including children since schools are often located in high traffic pollution exposure areas. Recent findings Recent findings include epidemiologic and mechanistic studies that shed new light on the impact of traffic pollution on allergic diseases and the biology underlying this impact. In addition, new innovative methods to assess and quantify traffic pollution have been developed to assess exposure and identify vulnerable populations and individuals. Summary This review will summarize the most recent findings in each of these areas. These findings will have substantial impact on clinical practice and research by development of novel methods to quantify exposure and identify at-risk individuals, as well as mechanistic studies that identify new targets for intervention for individuals most adversely affected by TRAP exposure. PMID:26474340

Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization.

PubMed

Trescher, Saskia; Münchmeyer, Jannes; Leser, Ulf

2017-03-27

Gene regulation is one of the most important cellular processes, indispensable for the adaptability of organisms and closely interlinked with several classes of pathogenesis and their progression. Elucidation of regulatory mechanisms can be approached by a multitude of experimental methods, yet integration of the resulting heterogeneous, large, and noisy data sets into comprehensive and tissue or disease-specific cellular models requires rigorous computational methods. Recently, several algorithms have been proposed which model genome-wide gene regulation as sets of (linear) equations over the activity and relationships of transcription factors, genes and other factors. Subsequent optimization finds those parameters that minimize the divergence of predicted and measured expression intensities. In various settings, these methods produced promising results in terms of estimating transcription factor activity and identifying key biomarkers for specific phenotypes. However, despite their common root in mathematical optimization, they vastly differ in the types of experimental data being integrated, the background knowledge necessary for their application, the granularity of their regulatory model, the concrete paradigm used for solving the optimization problem and the data sets used for evaluation. Here, we review five recent methods of this class in detail and compare them with respect to several key properties. Furthermore, we quantitatively compare the results of four of the presented methods based on publicly available data sets. The results show that all methods seem to find biologically relevant information. However, we also observe that the mutual result overlaps are very low, which contradicts biological intuition. Our aim is to raise further awareness of the power of these methods, yet also to identify common shortcomings and necessary extensions enabling focused research on the critical points.

[Detection of protein-protein interactions by FRET and BRET methods].

PubMed

Matoulková, E; Vojtěšek, B

2014-01-01

Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

Modularization of biochemical networks based on classification of Petri net t-invariants

PubMed Central

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-01-01

Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. Results We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. Conclusion We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis. PMID:18257938

Biological markers for anxiety disorders, OCD and PTSD: A consensus statement. Part II: Neurochemistry, neurophysiology and neurocognition

PubMed Central

Bandelow, Borwin; Baldwin, David; Abelli, Marianna; Bolea-Alamanac, Blanca; Bourin, Michel; Chamberlain, Samuel R.; Cinosi, Eduardo; Davies, Simon; Domschke, Katharina; Fineberg, Naomi; Grünblatt, Edna; Jarema, Marek; Kim, Yong-Ku; Maron, Eduard; Masdrakis, Vasileios; Mikova, Olya; Nutt, David; Pallanti, Stefano; Pini, Stefano; Ströhle, Andreas; Thibaut, Florence; Vaghix, Matilde M.; Won, Eunsoo; Wedekind, Dirk; Wichniak, Adam; Woolley, Jade; Zwanzger, Peter; Riederer, Peter

2017-01-01

Objective Biomarkers are defined as anatomical, biochemical or physiological traits that are specific to certain disorders or syndromes. The objective of this paper is to summarise the current knowledge of biomarkers for anxiety disorders, obsessive-compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Methods Findings in biomarker research were reviewed by a task force of international experts in the field, consisting of members of the World Federation of Societies for Biological Psychiatry Task Force on Biological Markers and of the European College of Neuropsychopharmacology Anxiety Disorders Research Network. Results The present article (Part II) summarises findings on potential biomarkers in neurochemistry (neurotransmitters such as serotonin, norepinephrine, dopamine or GABA, neuropeptides such as cholecystokinin, neurokinins, atrial natriuretic peptide, or oxytocin, the HPA axis, neurotrophic factors such as NGF and BDNF, immunology and CO2 hypersensitivity), neurophysiology (EEG, heart rate variability) and neurocognition. The accompanying paper (Part I) focuses on neuroimaging and genetics. Conclusions Although at present, none of the putative biomarkers is sufficient and specific as a diagnostic tool, an abundance of high quality research has accumulated that should improve our understanding of the neurobiological causes of anxiety disorders, OCD and PTSD. PMID:27419272

The fractured landscape of RNA-seq alignment: the default in our STARs.

PubMed

Ballouz, Sara; Dobin, Alexander; Gingeras, Thomas R; Gillis, Jesse

2018-06-01

Many tools are available for RNA-seq alignment and expression quantification, with comparative value being hard to establish. Benchmarking assessments often highlight methods' good performance, but are focused on either model data or fail to explain variation in performance. This leaves us to ask, what is the most meaningful way to assess different alignment choices? And importantly, where is there room for progress? In this work, we explore the answers to these two questions by performing an exhaustive assessment of the STAR aligner. We assess STAR's performance across a range of alignment parameters using common metrics, and then on biologically focused tasks. We find technical metrics such as fraction mapping or expression profile correlation to be uninformative, capturing properties unlikely to have any role in biological discovery. Surprisingly, we find that changes in alignment parameters within a wide range have little impact on both technical and biological performance. Yet, when performance finally does break, it happens in difficult regions, such as X-Y paralogs and MHC genes. We believe improved reporting by developers will help establish where results are likely to be robust or fragile, providing a better baseline to establish where methodological progress can still occur.

A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

PubMed

Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua; Huang, Wei

2017-12-01

As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n 2 ) time complexity. Copyright © 2017. Published by Elsevier B.V.

Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.

PubMed

Bettenbühl, Mario; Rusconi, Marco; Engbert, Ralf; Holschneider, Matthias

2012-01-01

Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

PubMed Central

Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Sorger, Peter K.; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo

2010-01-01

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. PMID:20186320

Social and Behavioral Risk Marker Clustering Associated with Biological Risk Factors for Coronary Heart Disease: NHANES 2001–2004

PubMed Central

Everage, Nicholas J.; Linkletter, Crystal D.; Gjelsvik, Annie; McGarvey, Stephen T.; Loucks, Eric B.

2014-01-01

Background. Social and behavioral risk markers (e.g., physical activity, diet, smoking, and socioeconomic position) cluster; however, little is known whether clustering is associated with coronary heart disease (CHD) risk. Objectives were to determine if sociobehavioral clustering is associated with biological CHD risk factors (total cholesterol, HDL cholesterol, systolic blood pressure, body mass index, waist circumference, and diabetes) and whether associations are independent of individual clustering components. Methods. Participants included 4,305 males and 4,673 females aged ≥20 years from NHANES 2001–2004. Sociobehavioral Risk Marker Index (SRI) included a summary score of physical activity, fruit/vegetable consumption, smoking, and educational attainment. Regression analyses evaluated associations of SRI with aforementioned biological CHD risk factors. Receiver operator curve analyses assessed independent predictive ability of SRI. Results. Healthful clustering (SRI = 0) was associated with improved biological CHD risk factor levels in 5 of 6 risk factors in females and 2 of 6 risk factors in males. Adding SRI to models containing age, race, and individual SRI components did not improve C-statistics. Conclusions. Findings suggest that healthful sociobehavioral risk marker clustering is associated with favorable CHD risk factor levels, particularly in females. These findings should inform social ecological interventions that consider health impacts of addressing social and behavioral risk factors. PMID:24719858

Learning accurate and interpretable models based on regularized random forests regression

PubMed Central

2014-01-01

Background Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. Methods In this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features. Results We tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression. Conclusion It demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied. PMID:25350120

Exploring the MACH Model’s Potential as a Metacognitive Tool to Help Undergraduate Students Monitor Their Explanations of Biological Mechanisms

PubMed Central

Trujillo, Caleb M.; Anderson, Trevor R.; Pelaez, Nancy J.

2016-01-01

When undergraduate biology students learn to explain biological mechanisms, they face many challenges and may overestimate their understanding of living systems. Previously, we developed the MACH model of four components used by expert biologists to explain mechanisms: Methods, Analogies, Context, and How. This study explores the implementation of the model in an undergraduate biology classroom as an educational tool to address some of the known challenges. To find out how well students’ written explanations represent components of the MACH model before and after they were taught about it and why students think the MACH model was useful, we conducted an exploratory multiple case study with four interview participants. We characterize how two students explained biological mechanisms before and after a teaching intervention that used the MACH components. Inductive analysis of written explanations and interviews showed that MACH acted as an effective metacognitive tool for all four students by helping them to monitor their understanding, communicate explanations, and identify explanatory gaps. Further research, though, is needed to more fully substantiate the general usefulness of MACH for promoting students’ metacognition about their understanding of biological mechanisms. PMID:27252295

Metabolomic Tools to Assess the Chemistry and Bioactivity of Endophytic Aspergillus Strain.

PubMed

Tawfike, Ahmed F; Tate, Rothwelle; Abbott, Gráinne; Young, Louise; Viegelmann, Christina; Schumacher, Marc; Diederich, Marc; Edrada-Ebel, RuAngelie

2017-10-01

Endophytic fungi associated with medicinal plants are a potential source of novel chemistry and biology that may find applications as pharmaceutical and agrochemical drugs. In this study, a combination of metabolomics and bioactivity-guided approaches were employed to isolate secondary metabolites with cytotoxicity against cancer cells from an endophytic Aspergillus aculeatus. The endophyte was isolated from the Egyptian medicinal plant Terminalia laxiflora and identified using molecular biological methods. Metabolomics and dereplication studies were accomplished by utilizing the MZmine software coupled with the universal Dictionary of Natural Products database. Metabolic profiling, with aid of multivariate data analysis, was performed at different stages of the growth curve to choose the optimized method suitable for up-scaling. The optimized culture method yielded a crude extract abundant with biologically-active secondary metabolites. Crude extracts were fractionated using different high-throughput chromatographic techniques. Purified compounds were identified by HR-ESI-MS, 1D- and 2D-NMR. This study introduced a new method of dereplication utilizing both high-resolution mass spectrometry and NMR spectroscopy. The metabolites were putatively identified by applying a chemotaxonomic filter. We also present a short review on the diverse chemistry of terrestrial endophytic strains of Aspergillus, which has become a part of our dereplication work and this will be of wide interest to those working in this field. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Aligning Metabolic Pathways Exploiting Binary Relation of Reactions.

PubMed

Huang, Yiran; Zhong, Cheng; Lin, Hai Xiang; Huang, Jing

2016-01-01

Metabolic pathway alignment has been widely used to find one-to-one and/or one-to-many reaction mappings to identify the alternative pathways that have similar functions through different sets of reactions, which has important applications in reconstructing phylogeny and understanding metabolic functions. The existing alignment methods exhaustively search reaction sets, which may become infeasible for large pathways. To address this problem, we present an effective alignment method for accurately extracting reaction mappings between two metabolic pathways. We show that connected relation between reactions can be formalized as binary relation of reactions in metabolic pathways, and the multiplications of zero-one matrices for binary relations of reactions can be accomplished in finite steps. By utilizing the multiplications of zero-one matrices for binary relation of reactions, we efficiently obtain reaction sets in a small number of steps without exhaustive search, and accurately uncover biologically relevant reaction mappings. Furthermore, we introduce a measure of topological similarity of nodes (reactions) by comparing the structural similarity of the k-neighborhood subgraphs of the nodes in aligning metabolic pathways. We employ this similarity metric to improve the accuracy of the alignments. The experimental results on the KEGG database show that when compared with other state-of-the-art methods, in most cases, our method obtains better performance in the node correctness and edge correctness, and the number of the edges of the largest common connected subgraph for one-to-one reaction mappings, and the number of correct one-to-many reaction mappings. Our method is scalable in finding more reaction mappings with better biological relevance in large metabolic pathways.

Recent progress in structural biology: lessons from our research history.

PubMed

Nitta, Ryo; Imasaki, Tsuyoshi; Nitta, Eriko

2018-05-16

The recent 'resolution revolution' in structural analyses of cryo-electron microscopy (cryo-EM) has drastically changed the research strategy for structural biology. In addition to X-ray crystallography and nuclear magnetic resonance spectroscopy, cryo-EM has achieved the structural analysis of biological molecules at near-atomic resolution, resulting in the Nobel Prize in Chemistry 2017. The effect of this revolution has spread within the biology and medical science fields affecting everything from basic research to pharmaceutical development by visualizing atomic structure. As we have used cryo-EM as well as X-ray crystallography since 2000 to elucidate the molecular mechanisms of the fundamental phenomena in the cell, here we review our research history and summarize our findings. In the first half of the review, we describe the structural mechanisms of microtubule-based motility of molecular motor kinesin by using a joint cryo-EM and X-ray crystallography method. In the latter half, we summarize our structural studies on transcriptional regulation by X-ray crystallography of in vitro reconstitution of a multi-protein complex.

Data management and data enrichment for systems biology projects.

PubMed

Wittig, Ulrike; Rey, Maja; Weidemann, Andreas; Müller, Wolfgang

2017-11-10

Collecting, curating, interlinking, and sharing high quality data are central to de.NBI-SysBio, the systems biology data management service center within the de.NBI network (German Network for Bioinformatics Infrastructure). The work of the center is guided by the FAIR principles for scientific data management and stewardship. FAIR stands for the four foundational principles Findability, Accessibility, Interoperability, and Reusability which were established to enhance the ability of machines to automatically find, access, exchange and use data. Within this overview paper we describe three tools (SABIO-RK, Excemplify, SEEK) that exemplify the contribution of de.NBI-SysBio services to FAIR data, models, and experimental methods storage and exchange. The interconnectivity of the tools and the data workflow within systems biology projects will be explained. For many years we are the German partner in the FAIRDOM initiative (http://fair-dom.org) to establish a European data and model management service facility for systems biology. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

PubMed

Ben Abdallah, Emna; Folschette, Maxime; Roux, Olivier; Magnin, Morgan

2017-01-01

This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

PubMed

Chen, Ruoying; Zhang, Zhiwang; Wu, Di; Zhang, Peng; Zhang, Xinyang; Wang, Yong; Shi, Yong

2011-01-21

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids. Copyright © 2010 Elsevier Ltd. All rights reserved.

Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure.

PubMed

Pacini, Clare; Ajioka, James W; Micklem, Gos

2017-04-12

Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding. We develop an empirical Bayes approach to improve covariance estimates for gene expression, where we assume the covariance matrix takes a block diagonal form. Our method shows lower false discovery rates than existing methods on simulated data. Applied to a real data set from Bacillus subtilis we demonstrate it's ability to detecting known regulatory units and interactions between them. We demonstrate that, compared to existing methods, our method is able to find significant covariances and also to control false discovery rates, even when the sample size is small (n=10). The method can be used to find potential regulatory networks, and it may also be used as a pre-processing step for methods that calculate, for example, partial correlations, so enabling the inference of the causal and hierarchical structure of the networks.

76 FR 4859 - Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-27

...] Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological Control Agent... environmental assessment and finding of no significant impact relative to the control of Asian citrus psyllid... the control of ACP. \\1\\ To view the notice, environmental assessment, finding of no significant impact...

76 FR 42675 - Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-19

...] Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological Control Agent.... SUMMARY: We are advising the public that an environmental assessment and finding of no significant impact... woolly adelgid. Based on its finding of no significant impact, the Animal and Plant Health Inspection...

Participation in introductory biology laboratories: An integrated assessment based on surveys, behavioral observations, and qualitative interviews

NASA Astrophysics Data System (ADS)

Russell, Connie Adelle

Scope and method of study. The purpose of this study was to evaluate the effect of gender, major, and prior knowledge of and attitude toward biology on participation in introductory biology laboratories. Subjects for this study were 3,527 students enrolled in college-level introductory biology courses. During the study, three introductory courses were replaced with one mixed-majors course. The new course adopted a different pedagological approach from the previous courses in that an inquiry-based approach was used in lectures and laboratories. All subjects completed a survey that measured content knowledge using the NABT/NSTA High School Biology Examination Version 1990 and attitude using Russell and Hollander's Biology Attitude Scale. I used and discuss the merits of using ethological methods and data collection software, EthoScribeTM (Tima Scientific) to collect behavioral data from 145 students. I also evaluated participation using qualitative interviews of 30 students. I analyzed content knowledge and attitude data using ANOVA and Pearson correlation, and behavioral data using Contingency Table Analysis. I analyzed interviews following methods outlined by Rubin and Rubin. Findings. Course style and gender were the most useful variables in distinguishing differences among groups of students with regard to attitude, content knowledge, and participation in laboratories. Attitude toward biology and achievement measured by the surveys were found to be positively correlated; however, gender, major, class standing, course style and interactions between these variables also had effects on these variables. I found a positive association among attitude, achievement and participation in hands-on activities in laboratories. Differences in participation also were associated group type. In a traditional introductory biology course, females in single-gender groups, gender-equal, or groups in which females were the majority spent more time performing hands-on science-related activities than did females in groups in which they were the minority. Conversely, males in mixed-gender groups spent more time performing hands-on activities than did males in single-gender groups. Both sexes participated equally in laboratories taught in an inquiry-based style.

Methods of Collection of Biological Information for Fatigue Evaluation during Visual Display Terminals (VDT) Operation

NASA Astrophysics Data System (ADS)

Hachiya, Yuriko; Ogai, Harutoshi; Okazaki, Hiroko; Fujisaki, Takeshi; Uchida, Kazuhiko; Oda, Susumu; Wada, Futoshi; Mori, Koji

A method for the analysis of fatigue parameters has been rarely researched in VDT operation. Up to now, fatigue was evaluated by changing of biological information. If signals regarding fatigue are detected, fatigue can be measured. The purpose of this study proposed experiment and analysis method to extract parameters related to fatigue from the biological information during VDT operation using the Independent Component Analysis (ICA). An experiment had 11 subjects. As for the experiment were light loaded VDT operation and heavy loaded VDT operation. A measurement item were amount of work, a mistake number, subjective symptom, surface skin temperature (forehead and apex nasi), heart rate, skin blood flow of forearm and respiratory rate. In the heavy loaded operation group, mistake number and subjective symptom score were increased to compare with the other. And Two-factor ANOVA was used for analysis. The result of mistake number was confirmed that heavy loaded. After the moving averages of waveshape were calculated, it was made to extract independent components by using the ICA. The results of the ICA suggest that the independent components increase according to accumulation of fatigue. Thus, the independent components would be a possible parameter of fatigue. However, further experiments should continue in order to obtain the conclusive finding of our research.

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

PubMed

Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M

2018-03-15

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Accelerating Smith-Waterman Algorithm for Biological Database Search on CUDA-Compatible GPUs

NASA Astrophysics Data System (ADS)

Munekawa, Yuma; Ino, Fumihiko; Hagihara, Kenichi

This paper presents a fast method capable of accelerating the Smith-Waterman algorithm for biological database search on a cluster of graphics processing units (GPUs). Our method is implemented using compute unified device architecture (CUDA), which is available on the nVIDIA GPU. As compared with previous methods, our method has four major contributions. (1) The method efficiently uses on-chip shared memory to reduce the data amount being transferred between off-chip video memory and processing elements in the GPU. (2) It also reduces the number of data fetches by applying a data reuse technique to query and database sequences. (3) A pipelined method is also implemented to overlap GPU execution with database access. (4) Finally, a master/worker paradigm is employed to accelerate hundreds of database searches on a cluster system. In experiments, the peak performance on a GeForce GTX 280 card reaches 8.32 giga cell updates per second (GCUPS). We also find that our method reduces the amount of data fetches to 1/140, achieving approximately three times higher performance than a previous CUDA-based method. Our 32-node cluster version is approximately 28 times faster than a single GPU version. Furthermore, the effective performance reaches 75.6 giga instructions per second (GIPS) using 32 GeForce 8800 GTX cards.

Improving clustering with metabolic pathway data.

PubMed

Milone, Diego H; Stegmayer, Georgina; López, Mariana; Kamenetzky, Laura; Carrari, Fernando

2014-04-10

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.

Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

PubMed Central

Myint, Kyaw Z.; Xie, Xiang-Qun

2015-01-01

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

Prediction of enzymatic pathways by integrative pathway mapping

PubMed Central

Wichelecki, Daniel J; San Francisco, Brian; Zhao, Suwen; Rodionov, Dmitry A; Vetting, Matthew W; Al-Obaidi, Nawar F; Lin, Henry; O'Meara, Matthew J; Scott, David A; Morris, John H; Russel, Daniel; Almo, Steven C; Osterman, Andrei L

2018-01-01

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology. PMID:29377793

Secondary school biology teaching, 1983--2004: Objectives as stated in periodical literature

NASA Astrophysics Data System (ADS)

Russell, James W., Sr.

Purpose of the study. The major purpose of this study was to identify and to classify objectives for teaching biology in secondary school in the United States during the period 1983-2004. These objectives were identified by objective statements in articles from selected professional periodicals. Procedure. The 1983-2004 period was divided into four subperiods on the basis of major historical events. Selected professional periodicals were searched for statements of objectives of secondary school biology teaching. These statements were catalogued into Knowledge, Process, Product, Attitude and Interest, or Cultural Awareness categories. The resulting data were classified within and across the four subperiods according to frequency of occurrence, category, authorship, and year. Findings. The major findings of this investigation included the following: (1) Authorships in Higher Education produced the most articles and the most statements in each subperiod. Miscellaneous authors produced the least articles and statements. (2) Statements in the Attitude and Interest category were the most frequent in the four subperiods. (3) The "most important" objectives for secondary school biology teaching were Presents major facts, principles, or fundamentals (from the Knowledge category), Expresses scientific attitudes and appreciation, Identifies the nature of science and scientists, and Identifies scientific interest and career development (from the Attitude and Interest category), and Develops scientific method of thinking (from the Process category). Conclusions. Based on the findings of this investigation, the following conclusions were made: (1) The objectives for teaching secondary school biology were influenced by historical events, especially the publication of A Nation at Risk: The Imperative for Educational Reform in 1983, America 2000 in 1988, Goals 2000 in 1994, No Child Left Behind in 2000. The rapid growth and expansion of technology and the World Wide Web during the time span of the study also influenced the number of objectives. (2) Authors in Higher Education wrote more articles about the objectives for the teaching of secondary school biology than those in Secondary Education or other categories. This was probably a reflection of the "publish or perish" environment in many colleges and universities.

Biological network motif detection and evaluation

PubMed Central

2011-01-01

Background Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. Results We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. Conclusion We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks. PMID:22784624

Mathematics in modern immunology

DOE PAGES

Castro, Mario; Lythe, Grant; Molina-París, Carmen; ...

2016-02-19

Mathematical and statistical methods enable multidisciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. Here, we collect a representative sample of studies in T-cell biology that illustrate the benefits of modelling–experimental collaborations and that have proven valuable or even groundbreaking. Furthermore, we conclude that it is possible to find excellent examples of synergy between mathematical modelling and experiment in immunology, which have brought significant insight that would not be available without these collaborations, but that much remains to be discovered.

Mathematics in modern immunology

PubMed Central

Castro, Mario; Lythe, Grant; Molina-París, Carmen; Ribeiro, Ruy M.

2016-01-01

Mathematical and statistical methods enable multidisciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. We collect a representative sample of studies in T-cell biology that illustrate the benefits of modelling–experimental collaborations and that have proven valuable or even groundbreaking. We conclude that it is possible to find excellent examples of synergy between mathematical modelling and experiment in immunology, which have brought significant insight that would not be available without these collaborations, but that much remains to be discovered. PMID:27051512

Peer-Led Team Learning Helps Minority Students Succeed

PubMed Central

Snyder, Julia J.; Sloane, Jeremy D.; Dunk, Ryan D. P.; Wiles, Jason R.

2016-01-01

Active learning methods have been shown to be superior to traditional lecture in terms of student achievement, and our findings on the use of Peer-Led Team Learning (PLTL) concur. Students in our introductory biology course performed significantly better if they engaged in PLTL. There was also a drastic reduction in the failure rate for underrepresented minority (URM) students with PLTL, which further resulted in closing the achievement gap between URM and non-URM students. With such compelling findings, we strongly encourage the adoption of Peer-Led Team Learning in undergraduate Science, Technology, Engineering, and Mathematics (STEM) courses. PMID:26959826

Better Decomposition Heuristics for the Maximum-Weight Connected Graph Problem Using Betweenness Centrality

NASA Astrophysics Data System (ADS)

Yamamoto, Takanori; Bannai, Hideo; Nagasaki, Masao; Miyano, Satoru

We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.

Peer-Led Team Learning Helps Minority Students Succeed.

PubMed

Snyder, Julia J; Sloane, Jeremy D; Dunk, Ryan D P; Wiles, Jason R

2016-03-01

Active learning methods have been shown to be superior to traditional lecture in terms of student achievement, and our findings on the use of Peer-Led Team Learning (PLTL) concur. Students in our introductory biology course performed significantly better if they engaged in PLTL. There was also a drastic reduction in the failure rate for underrepresented minority (URM) students with PLTL, which further resulted in closing the achievement gap between URM and non-URM students. With such compelling findings, we strongly encourage the adoption of Peer-Led Team Learning in undergraduate Science, Technology, Engineering, and Mathematics (STEM) courses.

Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways.

PubMed

Karbalaei, Reza; Allahyari, Marzieh; Rezaei-Tavirani, Mostafa; Asadzadeh-Aghdaei, Hamid; Zali, Mohammad Reza

2018-01-01

Analysis reconstruction networks from two diseases, NAFLD and Alzheimer`s diseases and their relationship based on systems biology methods. NAFLD and Alzheimer`s diseases are two complex diseases, with progressive prevalence and high cost for countries. There are some reports on relation and same spreading pathways of these two diseases. In addition, they have some similar risk factors, exclusively lifestyle such as feeding, exercises and so on. Therefore, systems biology approach can help to discover their relationship. DisGeNET and STRING databases were sources of disease genes and constructing networks. Three plugins of Cytoscape software, including ClusterONE, ClueGO and CluePedia, were used to analyze and cluster networks and enrichment of pathways. An R package used to define best centrality method. Finally, based on degree and Betweenness, hubs and bottleneck nodes were defined. Common genes between NAFLD and Alzheimer`s disease were 190 genes that used construct a network with STRING database. The resulting network contained 182 nodes and 2591 edges and comprises from four clusters. Enrichment of these clusters separately lead to carbohydrate metabolism, long chain fatty acid and regulation of JAK-STAT and IL-17 signaling pathways, respectively. Also seven genes selected as hub-bottleneck include: IL6, AKT1, TP53, TNF, JUN, VEGFA and PPARG. Enrichment of these proteins and their first neighbors in network by OMIM database lead to diabetes and obesity as ancestors of NAFLD and AD. Systems biology methods, specifically PPI networks, can be useful for analyzing complicated related diseases. Finding Hub and bottleneck proteins should be the goal of drug designing and introducing disease markers.

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

PubMed

Marques, Yuri Bento; de Paiva Oliveira, Alcione; Ribeiro Vasconcelos, Ana Tereza; Cerqueira, Fabio Ribeiro

2016-12-15

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

Characterizing viscoelastic mechanical properties of highly compliant polymers and biological tissues using impact indentation.

PubMed

Mijailovic, Aleksandar S; Qing, Bo; Fortunato, Daniel; Van Vliet, Krystyn J

2018-04-15

Precise and accurate measurement of viscoelastic mechanical properties becomes increasingly challenging as sample stiffness decreases to elastic moduli <1 kPa, largely due to difficulties detecting initial contact with the compliant sample surface. This limitation is particularly relevant to characterization of biological soft tissues and compliant gels. Here, we employ impact indentation which, in contrast to shear rheology and conventional indentation, does not require contact detection a priori, and present a novel method to extract viscoelastic moduli and relaxation time constants directly from the impact response. We first validate our approach by using both impact indentation and shear rheology to characterize polydimethylsiloxane (PDMS) elastomers of stiffness ranging from 100 s of Pa to nearly 10 kPa. Assuming a linear viscoelastic constitutive model for the material, we find that the moduli and relaxation times obtained from fitting the impact response agree well with those obtained from fitting the rheological response. Next, we demonstrate our validated method on hydrated, biological soft tissues obtained from porcine brain, murine liver, and murine heart, and report the equilibrium shear moduli, instantaneous shear moduli, and relaxation time constants for each tissue. Together, our findings provide a new and straightforward approach capable of probing local mechanical properties of highly compliant viscoelastic materials with millimeter scale spatial resolution, mitigating complications involving contact detection or sample geometric constraints. Characterization and optimization of mechanical properties can be essential for the proper function of biomaterials in diverse applications. However, precise and accurate measurement of viscoelastic mechanical properties becomes increasingly difficult with increased compliance (particularly for elastic moduli <1 kPa), largely due to challenges detecting initial contact with the compliant sample surface and measuring response at short timescale or high frequency. By contrast, impact indentation has highly accurate contact detection and can be used to measure short timescale (glassy) response. Here, we demonstrate an experimental and analytical method that confers significant advantages over existing approaches to extract spatially resolved viscoelastic moduli and characteristic time constants of biological tissues (e.g., brain and heart) and engineered biomaterials. Copyright © 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Quantitative comparison of alternative methods for coarse-graining biological networks

PubMed Central

Bowman, Gregory R.; Meng, Luming; Huang, Xuhui

2013-01-01

Markov models and master equations are a powerful means of modeling dynamic processes like protein conformational changes. However, these models are often difficult to understand because of the enormous number of components and connections between them. Therefore, a variety of methods have been developed to facilitate understanding by coarse-graining these complex models. Here, we employ Bayesian model comparison to determine which of these coarse-graining methods provides the models that are most faithful to the original set of states. We find that the Bayesian agglomerative clustering engine and the hierarchical Nyström expansion graph (HNEG) typically provide the best performance. Surprisingly, the original Perron cluster cluster analysis (PCCA) method often provides the next best results, outperforming the newer PCCA+ method and the most probable paths algorithm. We also show that the differences between the models are qualitatively significant, rather than being minor shifts in the boundaries between states. The performance of the methods correlates well with the entropy of the resulting coarse-grainings, suggesting that finding states with more similar populations (i.e., avoiding low population states that may just be noise) gives better results. PMID:24089717

G-CSF/anti-G-CSF antibody complexes drive the potent recovery and expansion of CD11b+Gr-1+ myeloid cells without compromising CD8+ T cell immune responses

PubMed Central

2013-01-01

Background Administration of recombinant G-CSF following cytoreductive therapy enhances the recovery of myeloid cells, minimizing the risk of opportunistic infection. Free G-CSF, however, is expensive, exhibits a short half-life, and has poor biological activity in vivo. Methods We evaluated whether the biological activity of G-CSF could be improved by pre-association with anti-G-CSF mAb prior to injection into mice. Results We find that the efficacy of G-CSF therapy can be enhanced more than 100-fold by pre-association of G-CSF with an anti-G-CSF monoclonal antibody (mAb). Compared with G-CSF alone, administration of G-CSF/anti-G-CSF mAb complexes induced the potent expansion of CD11b+Gr-1+ myeloid cells in mice with or without concomitant cytoreductive treatment including radiation or chemotherapy. Despite driving the dramatic expansion of myeloid cells, in vivo antigen-specific CD8+ T cell immune responses were not compromised. Furthermore, injection of G-CSF/anti-G-CSF mAb complexes heightened protective immunity to bacterial infection. As a measure of clinical value, we also found that antibody complexes improved G-CSF biological activity much more significantly than pegylation. Conclusions Our findings provide the first evidence that antibody cytokine complexes can effectively expand myeloid cells, and furthermore, that G-CSF/anti-G-CSF mAb complexes may provide an improved method for the administration of recombinant G-CSF. PMID:24279871

Image portion identification methods, image parsing methods, image parsing systems, and articles of manufacture

DOEpatents

Lassahn, Gordon D.; Lancaster, Gregory D.; Apel, William A.; Thompson, Vicki S.

2013-01-08

Image portion identification methods, image parsing methods, image parsing systems, and articles of manufacture are described. According to one embodiment, an image portion identification method includes accessing data regarding an image depicting a plurality of biological substrates corresponding to at least one biological sample and indicating presence of at least one biological indicator within the biological sample and, using processing circuitry, automatically identifying a portion of the image depicting one of the biological substrates but not others of the biological substrates.

Circulating elastin peptides, role in vascular pathology.

PubMed

Robert, L; Labat-Robert, J

2014-12-01

The atherosclerotic process starts with the degradation of elastic fibers. Their presence was demonstrated in the circulation as well as several of their biological properties elucidated. We described years ago a procedure to obtain large elastin peptides by organo-alkaline hydrolysis, κ-elastin. This method enabled also the preparation of specific antibodies used to determine elastin peptides, as well as anti-elastin antibodies in body fluids and tissue extracts. Elastin peptides were determined in a large number of human blood samples. Studies were carried out to explore their pharmacological properties. Similar recent studies by other laboratories confirmed our findings and arose new interest in circulating elastin peptides for their biological activities. This recent trend justified the publication of a review of the biological and pathological activities of elastin peptides demonstrated during our previous studies, subject of this article. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Biological aspects of tissue-engineered cartilage.

PubMed

Hoshi, Kazuto; Fujihara, Yuko; Yamawaki, Takanori; Harai, Motohiro; Asawa, Yukiyo; Hikita, Atsuhiko

2018-04-01

Cartilage regenerative medicine has been progressed well, and it reaches the stage of clinical application. Among various techniques, tissue engineering, which incorporates elements of materials science, is investigated earnestly, driven by high clinical needs. The cartilage tissue engineering using a poly lactide scaffold has been exploratorily used in the treatment of cleft lip-nose patients, disclosing good clinical results during 3-year observation. However, to increase the reliability of this treatment, not only accumulation of clinical evidence on safety and usefulness of the tissue-engineered products, but also establishment of scientific background on biological mechanisms, are regarded essential. In this paper, we reviewed recent trends of cartilage tissue engineering in clinical practice, summarized experimental findings on cellular and matrix changes during the cartilage regeneration, and discussed the importance of further studies on biological aspects of tissue-engineered cartilage, especially by the histological and the morphological methods.

Implications of genome-wide association studies in cancer therapeutics.

PubMed

Patel, Jai N; McLeod, Howard L; Innocenti, Federico

2013-09-01

Genome wide association studies (GWAS) provide an agnostic approach to identifying potential genetic variants associated with disease susceptibility, prognosis of survival and/or predictive of drug response. Although these techniques are costly and interpretation of study results is challenging, they do allow for a more unbiased interrogation of the entire genome, resulting in the discovery of novel genes and understanding of novel biological associations. This review will focus on the implications of GWAS in cancer therapy, in particular germ-line mutations, including findings from major GWAS which have identified predictive genetic loci for clinical outcome and/or toxicity. Lessons and challenges in cancer GWAS are also discussed, including the need for functional analysis and replication, as well as future perspectives for biological and clinical utility. Given the large heterogeneity in response to cancer therapeutics, novel methods of identifying mechanisms and biology of variable drug response and ultimately treatment individualization will be indispensable. © 2013 The British Pharmacological Society.

Deep learning for computational biology.

PubMed

Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold; Stegle, Oliver

2016-07-29

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.

PubMed

Funk, Christopher S; Kahanda, Indika; Ben-Hur, Asa; Verspoor, Karin M

2015-01-01

Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a "medium-throughput" pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.

How Albot0 finds its way home: a novel approach to cognitive mapping using robots.

PubMed

Yeap, Wai K

2011-10-01

Much of what we know about cognitive mapping comes from observing how biological agents behave in their physical environments, and several of these ideas were implemented on robots, imitating such a process. In this paper a novel approach to cognitive mapping is presented whereby robots are treated as a species of their own and their cognitive mapping is being investigated. Such robots are referred to as Albots. The design of the first Albot, Albot0 , is presented. Albot0 computes an imprecise map and employs a novel method to find its way home. Both the map and the return-home algorithm exhibited characteristics commonly found in biological agents. What we have learned from Albot0 's cognitive mapping are discussed. One major lesson is that the spatiality in a cognitive map affords us rich and useful information and this argues against recent suggestions that the notion of a cognitive map is not a useful one. Copyright © 2011 Cognitive Science Society, Inc.

Quantitative analysis of urea in human urine and serum by 1H nuclear magnetic resonance†

PubMed Central

Liu, Lingyan; Mo, Huaping; Wei, Siwei

2016-01-01

A convenient and fast method for quantifying urea in biofluids is demonstrated using NMR analysis and the solvent water signal as a concentration reference. The urea concentration can be accurately determined with errors less than 3% between 1 mM and 50 mM, and less than 2% above 50 mM in urine and serum. The method is promising for various applications with advantages of simplicity, high accuracy, and fast non-destructive detection. With an ability to measure other metabolites simultaneously, this NMR method is also likely to find applications in metabolic profiling and system biology. PMID:22179722

A review on detection methods used for foodborne pathogens

PubMed Central

Priyanka, B.; Patil, Rajashekhar K.; Dwarakanath, Sulatha

2016-01-01

Foodborne pathogens have been a cause of a large number of diseases worldwide and more so in developing countries. This has a major economic impact. It is important to contain them, and to do so, early detection is very crucial. Detection and diagnostics relied on culture-based methods to begin with and have developed in the recent past parallel to the developments towards immunological methods such as enzyme-linked immunosorbent assays (ELISA) and molecular biology-based methods such as polymerase chain reaction (PCR). The aim has always been to find a rapid, sensitive, specific and cost-effective method. Ranging from culturing of microbes to the futuristic biosensor technology, the methods have had this common goal. This review summarizes the recent trends and brings together methods that have been developed over the years. PMID:28139531

Clinical and Biological Relevance of Genomic Heterogeneity in Chronic Lymphocytic Leukemia

PubMed Central

Friedman, Daphne R.; Lucas, Joseph E.; Weinberg, J. Brice

2013-01-01

Background Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients’ leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL. Methods To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups. Results Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes. Conclusions Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways. PMID:23468975

75 FR 23221 - Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-03

...] Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological Control Agent for Water Hyacinth AGENCY: Animal and Plant Health Inspection Service, USDA. ACTION: Notice. SUMMARY... the severity of water hyacinth infestations. Based on its finding of no significant impact, the Animal...

United States Department of Agriculture-Agricultural Research Service research on biological control of arthropods.

PubMed

Hopper, Keith R

2003-01-01

During 1999-2001, ARS scientists published over 100 papers on more than 30 species of insect pest and 60 species of predator and parasitoid. These papers address issues crucial to the three strategies of biological control: conservation, augmentation and introduction. Conservation biological control includes both conserving extant populations of natural enemies by using relatively non-toxic pesticides and increasing the abundance of natural enemies in crops by providing or improving refuges for population growth and dispersal into crops. ARS scientists have been very active in determining the effects of pesticides on beneficial arthropods and in studying movement of natural enemies from refuges into crops. Augmentation involves repeated releases of natural enemies in the field, which can be inoculative or inundative. Inoculative releases are used to initiate self-propagating populations at times or in places where they would be slow to colonize. ARS scientists have studied augmentative biological control of a variety of pest insects. The targets are mostly pests in annual crops or other ephemeral habitats, where self-reproducing populations of natural enemies are not sufficiently abundant early enough to keep pest populations in check. ARS research in augmentative biological control centers on methods for rearing large numbers of healthy, effective natural enemies and for releasing them where and when they are needed at a cost less than the value of the reduction in damage to the crop. ARS scientists have researched various aspects of introductions of exotic biological control agents against a diversity of pest insects. The major issues in biological control introductions are accurate identification and adequate systematics of both natural enemies and target pests, exploration for natural enemies, predicting the success of candidates for introduction and the likelihood of non-target impacts, quarantine and rearing methods, and post-introduction evaluation of establishment, control and non-target impacts. ARS scientists have published research on several general issues in biological control. Among the most important are the mechanisms affecting mate- and host-finding and host specificity.

Teaching practices and professional development of biology professors at small, private, liberal arts colleges in the Southeast

NASA Astrophysics Data System (ADS)

Mallory, Sarah Elizabeth Bradford

Science teaching in pre-college institutions has been undergoing reform in recent years, particularly since 1996, when the National Science Education Standards were published. This reform includes inquiry-based teaching, student-centered classrooms, authentic assessment, and collaborative learning. Professional development is also recommended in the Standards document as the means for preparing teachers for reform-based teaching in pre-college classrooms. In post-secondary institutions, there is no curriculum-governing body to institute reform, and college faculty have devised their own standards and methods for teaching science, most often in the form of lecture and traditional procedure-driven laboratory exercises. This study was conducted to find examples of reform-based biology teaching in small, private, liberal arts colleges in the Southeast, where teaching innovations may be more likely to occur due to the size and independence of the schools. Professional development opportunities were also examined, since these would be important in the development of new curricula and methods of teaching. Data were collected from 151 participants, representing 78.3% of these colleges in eight southeastern states, by survey and from three volunteers by on-site interviews. Teaching was the main responsibility reported by all respondents, with both lower and upper level biology courses taught by all participants. Significant differences were found in the use of reform-based teaching in lower level biology courses versus upper level biology courses. Overall average use of inquiry-based teaching was 70.5%, while student-centered learning was reported on average by 57% of respondents, authentic assessment was reported on average by 56.6% of respondents, and collaborative learning was reported on average by 56% of respondents. Professional development opportunities most frequently used were reported to be journal, books, and videotapes. Multivariate regression analyses revealed that professional development which involves contact with colleagues at other institutions explained the variance in teaching practices in the simplest model, although much of the variance in the dependent variables of teaching practices remains unexplained. Qualitative data from the survey and also from interviews with volunteers served to further explain and corroborate the quantitative findings.

Genetic signatures of ecological diversity along an urbanization gradient.

PubMed

Kelly, Ryan P; O'Donnell, James L; Lowell, Natalie C; Shelton, Andrew O; Samhouri, Jameal F; Hennessey, Shannon M; Feist, Blake E; Williams, Gregory D

2016-01-01

Despite decades of work in environmental science and ecology, estimating human influences on ecosystems remains challenging. This is partly due to complex chains of causation among ecosystem elements, exacerbated by the difficulty of collecting biological data at sufficient spatial, temporal, and taxonomic scales. Here, we demonstrate the utility of environmental DNA (eDNA) for quantifying associations between human land use and changes in an adjacent ecosystem. We analyze metazoan eDNA sequences from water sampled in nearshore marine eelgrass communities and assess the relationship between these ecological communities and the degree of urbanization in the surrounding watershed. Counter to conventional wisdom, we find strongly increasing richness and decreasing beta diversity with greater urbanization, and similar trends in the diversity of life histories with urbanization. We also find evidence that urbanization influences nearshore communities at local (hundreds of meters) rather than regional (tens of km) scales. Given that different survey methods sample different components of an ecosystem, we then discuss the advantages of eDNA-which we use here to detect hundreds of taxa simultaneously-as a complement to traditional ecological sampling, particularly in the context of broad ecological assessments where exhaustive manual sampling is impractical. Genetic data are a powerful means of uncovering human-ecosystem interactions that might otherwise remain hidden; nevertheless, no sampling method reveals the whole of a biological community.

Primary culture of human Schwann and schwannoma cells: improved and simplified protocol.

PubMed

Dilwali, Sonam; Patel, Pratik B; Roberts, Daniel S; Basinsky, Gina M; Harris, Gordon J; Emerick, Kevin S; Stankovic, Konstantina M

2014-09-01

Primary culture of human Schwann cells (SCs) and vestibular schwannoma (VS) cells are invaluable tools to investigate SC physiology and VS pathobiology, and to devise effective pharmacotherapies against VS, which are sorely needed. However, existing culture protocols, in aiming to create robust, pure cultures, employ methods that can lead to loss of biological characteristics of the original cells, potentially resulting in misleading biological findings. We have developed a minimally manipulative method to culture primary human SC and VS cells, without the use of selective mitogens, toxins, or time-consuming and potentially transformative laboratory techniques. Schwann cell purity was quantified longitudinally using S100 staining in SC cultures derived from the great auricular nerve and VS cultures followed for 7 and 12 weeks, respectively. SC cultures retained approximately ≥85% purity for 2 weeks. VS cultures retained approximately ≥80% purity for the majority of the span of 12 weeks, with maximal purity of 87% at 2 weeks. The VS cultures showed high level of biological similarity (68% on average) to their respective parent tumors, as assessed using a protein array featuring 41 growth factors and receptors. Apoptosis rate in vitro negatively correlated with tumor volume. Our results, obtained using a faster, simplified culturing method than previously utilized, indicate that highly pure, primary human SC and VS cultures can be established with minimal manipulation, reaching maximal purity at 2 weeks of culture. The VS cultures recapitulate the parent tumors' biology to a great degree, making them relevant models to investigate VS pathobiology. Copyright © 2014 Elsevier B.V. All rights reserved.

Primary culture of human Schwann and schwannoma cells: Improved and simplified protocol

PubMed Central

Dilwali, Sonam; Patel, Pratik B.; Roberts, Daniel S.; Basinsky, Gina M.; Harris, Gordon J.; Emerick, Kevin; Stankovic, Konstantina M.

2014-01-01

Primary culture of human Schwann cells (SCs) and vestibular schwannoma (VS) cells are invaluable tools to investigate SC physiology and VS pathobiology, and to devise effective pharmacotherapies against VS, which are sorely needed. However, existing culture protocols, in aiming to create robust, pure cultures, employ methods that can lead to loss of biological characteristics of the original cells, potentially resulting in misleading biological findings. We have developed a minimally manipulative method to culture primary human SC and VS cells, without the use of selective mitogens, toxins, or time-consuming and potentially transformative laboratory techniques. Schwann cell purity was quantified longitudinally using S100 staining in SC cultures derived from the great auricular nerve and VS cultures followed for 7 and 12 weeks, respectively. SC cultures retained approximately ≥85% purity for 2 weeks. VS cultures retained approximately ≥80% purity for the majority of the span of 12 weeks, with maximal purity of 87% at 2 weeks. The VS cultures showed high level of biological similarity (68% on average) to their respective parent tumors, as assessed using a protein array featuring 41 growth factors and receptors. Apoptosis rate in vitro negatively correlated with tumor volume. Our results, obtained using a faster, simplified culturing method than previously utilized, indicate that highly pure, primary human SC and VS cultures can be established with minimal manipulation, reaching maximal purity at 2 weeks of culture. The VS cultures recapitulate the parent tumors' biology to a great degree, making them relevant models to investigate VS pathobiology. PMID:24910344

Modularization of biochemical networks based on classification of Petri net t-invariants.

PubMed

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-02-08

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

Characterizing Topology of Probabilistic Biological Networks.

PubMed

Todor, Andrei; Dobra, Alin; Kahveci, Tamer

2013-09-06

Biological interactions are often uncertain events, that may or may not take place with some probability. Existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. Here, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. We develop a method that accurately describes the degree distribution of such networks. We also extend our method to accurately compute the joint degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. It also helps us find an adequate mathematical model using maximum likelihood estimation. Our results demonstrate that power law and log-normal models best describe degree distributions for probabilistic networks. The inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected.

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

Existing Approaches to Chemical, Biological, Radiological, and Nuclear (CBRN) Education and Training for Health Professionals: Findings from an Integrative Literature Review.

PubMed

Kako, Mayumi; Hammad, Karen; Mitani, Satoko; Arbon, Paul

2018-04-01

This review was conducted to explore the literature to determine the availability, content, and evaluation of existing chemical, biological, radiological, and nuclear (CBRN) education programs for health professionals. An integrative review of the international literature describing disaster education for CBRN (2004-2016) was conducted. The following relevant databases were searched: Proquest, Pubmed, Science Direct, Scopus, Journals @ OVID, Google Scholar, Medline, and Ichuschi ver. 5 (Japanese database for health professionals). The search terms used were: "disaster," "chemical," "biological," "radiological," "nuclear," "CBRN," "health professional education," and "method." The following Medical Subject Headings (MeSH) terms, "education," "nursing," "continuing," "disasters," "disaster planning," and "bioterrorism," were used wherever possible and appropriate. The retrieved articles were narratively analyzed according to availability, content, and method. The content was thematically analyzed to provide an overview of the core content of the training. The literature search identified 619 potentially relevant articles for this study. Duplicates (n=104) were removed and 87 articles were identified for title review. In total, 67 articles were discarded, yielding 20 articles for all-text review, following 11 studies were retained for analysis, including one Japanese study. All articles published in English were from the USA, apart from the two studies located in Japan and Sweden. The most typical content in the selected literature was CBRN theory (n=11), followed by studies based on incident command (n=8), decontamination (n=7), disaster management (n=7), triage (n=7), personal protective equipment (PPE) use (n = 5), and post-training briefing (n=3). While the CBRN training course requires the participants to gain specific skills and knowledge, proposed training courses should be effectively constructed to include approaches such as scenario-based simulations, depending on the participants' needs. Kako M , Hammad K , Mitani S , Arbon P . Existing approaches to chemical, biological, radiological, and nuclear (CBRN) education and training for health professionals: findings from an integrative literature review. Prehosp Disaster Med. 2018;33(2):182-190.

Cost Sharing, Family Health Care Burden, and the Use of Specialty Drugs for Rheumatoid Arthritis

PubMed Central

Karaca-Mandic, Pinar; Joyce, Geoffrey F; Goldman, Dana P; Laouri, Marianne

2010-01-01

Objectives To examine the impact of benefit generosity and household health care financial burden on the demand for specialty drugs in the treatment of rheumatoid arthritis (RA). Data Sources/Study Setting Enrollment, claims, and benefit design information for 35 large private employers during 2000–2005. Study Design We estimated multivariate models of the effects of benefit generosity and household financial burden on initiation and continuation of biologic therapies. Data Extraction Methods We defined initiation of biologic therapy as first-time use of etanercept, adalimumab, or infliximab, and we constructed an index of plan generosity based on coverage of biologic therapies in each plan. We estimated the household's burden by summing up the annual out-of-pocket (OOP) expenses of other family members. Principal Findings Benefit generosity affected both the likelihood of initiating a biologic and continuing drug therapy, although the effects were stronger for initiation. Initiation of a biologic was lower in households where other family members incurred high OOP expenses. Conclusions The use of biologic therapy for RA is sensitive to benefit generosity and household financial burden. The increasing use of coinsurance rates for specialty drugs (as under Medicare Part D) raises concern about adverse health consequences. PMID:20831715

Early-Life Intelligence Predicts Midlife Biological Age

PubMed Central

Caspi, Avshalom; Belsky, Daniel W.; Harrington, Honalee; Houts, Renate; Israel, Salomon; Levine, Morgan E.; Sugden, Karen; Williams, Benjamin; Poulton, Richie; Moffitt, Terrie E.

2016-01-01

Objectives: Early-life intelligence has been shown to predict multiple causes of death in populations around the world. This finding suggests that intelligence might influence mortality through its effects on a general process of physiological deterioration (i.e., individual variation in “biological age”). We examined whether intelligence could predict measures of aging at midlife before the onset of most age-related disease. Methods: We tested whether intelligence assessed in early childhood, middle childhood, and midlife predicted midlife biological age in members of the Dunedin Study, a population-representative birth cohort. Results: Lower intelligence predicted more advanced biological age at midlife as captured by perceived facial age, a 10-biomarker algorithm based on data from the National Health and Nutrition Examination Survey (NHANES), and Framingham heart age (r = 0.1–0.2). Correlations between intelligence and telomere length were less consistent. The associations between intelligence and biological age were not explained by differences in childhood health or parental socioeconomic status, and intelligence remained a significant predictor of biological age even when intelligence was assessed before Study members began their formal schooling. Discussion: These results suggest that accelerated aging may serve as one of the factors linking low early-life intelligence to increased rates of morbidity and mortality. PMID:26014827

A unified partial likelihood approach for X-chromosome association on time-to-event outcomes.

PubMed

Xu, Wei; Hao, Meiling

2018-02-01

The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E), and skewed X-chromosome inactivation (XCI-S). Although these expressions are included in various predesigned genetic variation chip platforms, the X-chromosome has generally been excluded from the majority of genome-wide association studies analyses; this is most likely due to the lack of a standardized method in handling X-chromosomal genotype data. To analyze the X-linked genetic association for time-to-event outcomes with the actual process unknown, we propose a unified approach of maximizing the partial likelihood over all of the potential biological processes. The proposed method can be used to infer the true biological process and derive unbiased estimates of the genetic association parameters. A partial likelihood ratio test statistic that has been proved asymptotically chi-square distributed can be used to assess the X-chromosome genetic association. Furthermore, if the X-chromosome expression pertains to the XCI-S process, we can infer the correct skewed direction and magnitude of inactivation, which can elucidate significant findings regarding the genetic mechanism. A population-level model and a more general subject-level model have been developed to model the XCI-S process. Finite sample performance of this novel method is examined via extensive simulation studies. An application is illustrated with implementation of the method on a cancer genetic study with survival outcome. © 2017 WILEY PERIODICALS, INC.

Mining SNPs from EST sequences using filters and ensemble classifiers.

PubMed

Wang, J; Zou, Q; Guo, M Z

2010-05-04

Abundant single nucleotide polymorphisms (SNPs) provide the most complete information for genome-wide association studies. However, due to the bottleneck of manual discovery of putative SNPs and the inaccessibility of the original sequencing reads, it is essential to develop a more efficient and accurate computational method for automated SNP detection. We propose a novel computational method to rapidly find true SNPs in public-available EST (expressed sequence tag) databases; this method is implemented as SNPDigger. EST sequences are clustered and aligned. SNP candidates are then obtained according to a measure of redundant frequency. Several new informative biological features, such as the structural neighbor profiles and the physical position of the SNP, were extracted from EST sequences, and the effectiveness of these features was demonstrated. An ensemble classifier, which employs a carefully selected feature set, was included for the imbalanced training data. The sensitivity and specificity of our method both exceeded 80% for human genetic data in the cross validation. Our method enables detection of SNPs from the user's own EST dataset and can be used on species for which there is no genome data. Our tests showed that this method can effectively guide SNP discovery in ESTs and will be useful to avoid and save the cost of biological analyses.

An introduction to microbiome analysis for human biology applications.

PubMed

Amato, Katherine R

2017-01-01

Research examining the gut microbiota is currently exploding, and results are providing new perspectives on human biology. Factors such as host diet and physiology influence the composition and function of the gut microbiota, which in turn affects human nutrition, health, and behavior via interactions with metabolism, the immune system, and the brain. These findings represent an exciting new twist on familiar topics, and as a result, gut microbiome research is likely to provide insight into unresolved biological mechanisms driving human health. However, much remains to be learned about the broader ecological and evolutionary contexts within which gut microbes and humans are affecting each other. Here, I outline the procedures for generating data describing the gut microbiota with the goal of facilitating the wider integration of microbiome analyses into studies of human biology. I describe the steps involved in sample collection, DNA extraction, PCR amplification, high-throughput sequencing, and bioinformatics. While this review serves only as an introduction to these topics, it provides sufficient resources for researchers interested in launching new microbiome initiatives. As knowledge of these methods spreads, microbiome analysis should become a standard tool in the arsenal of human biology research. © 2016 Wiley Periodicals, Inc.

Using Collision Cones to Asses Biological Deconiction Methods

NASA Astrophysics Data System (ADS)

Brace, Natalie

For autonomous vehicles to navigate the world as efficiently and effectively as biological species, improvements are needed in terms of control strategies and estimation algorithms. Reactive collision avoidance is one specific area where biological systems outperform engineered algorithms. To better understand the discrepancy between engineered and biological systems, a collision avoidance algorithm was applied to frames of trajectory data from three biological species (Myotis velifer, Hirundo rustica, and Danio aequipinnatus). The algorithm uses information that can be sensed through visual cues (relative position and velocity) to define collision cones which are used to determine if agents are on a collision course and if so, to find a safe velocity that requires minimal deviation from the original velocity for each individual agent. Two- and three-dimensional versions of the algorithm with constant speed and maximum speed velocity requirements were considered. The obstacles provided to the algorithm were determined by the sensing range in terms of either metric or topological distance. The calculated velocities showed good correlation with observed velocities over the range of sensing parameters, indicating that the algorithm is a good basis for comparison and could potentially be improved with further study.

Developing a Test of Scientific Literacy Skills (TOSLS): measuring undergraduates' evaluation of scientific information and arguments.

PubMed

Gormally, Cara; Brickman, Peggy; Lutz, Mary

2012-01-01

Life sciences faculty agree that developing scientific literacy is an integral part of undergraduate education and report that they teach these skills. However, few measures of scientific literacy are available to assess students' proficiency in using scientific literacy skills to solve scenarios in and beyond the undergraduate biology classroom. In this paper, we describe the development, validation, and testing of the Test of Scientific Literacy Skills (TOSLS) in five general education biology classes at three undergraduate institutions. The test measures skills related to major aspects of scientific literacy: recognizing and analyzing the use of methods of inquiry that lead to scientific knowledge and the ability to organize, analyze, and interpret quantitative data and scientific information. Measures of validity included correspondence between items and scientific literacy goals of the National Research Council and Project 2061, findings from a survey of biology faculty, expert biology educator reviews, student interviews, and statistical analyses. Classroom testing contexts varied both in terms of student demographics and pedagogical approaches. We propose that biology instructors can use the TOSLS to evaluate their students' proficiencies in using scientific literacy skills and to document the impacts of curricular reform on students' scientific literacy.

Developing a Test of Scientific Literacy Skills (TOSLS): Measuring Undergraduates’ Evaluation of Scientific Information and Arguments

PubMed Central

Gormally, Cara; Brickman, Peggy; Lutz, Mary

2012-01-01

Life sciences faculty agree that developing scientific literacy is an integral part of undergraduate education and report that they teach these skills. However, few measures of scientific literacy are available to assess students’ proficiency in using scientific literacy skills to solve scenarios in and beyond the undergraduate biology classroom. In this paper, we describe the development, validation, and testing of the Test of Scientific Literacy Skills (TOSLS) in five general education biology classes at three undergraduate institutions. The test measures skills related to major aspects of scientific literacy: recognizing and analyzing the use of methods of inquiry that lead to scientific knowledge and the ability to organize, analyze, and interpret quantitative data and scientific information. Measures of validity included correspondence between items and scientific literacy goals of the National Research Council and Project 2061, findings from a survey of biology faculty, expert biology educator reviews, student interviews, and statistical analyses. Classroom testing contexts varied both in terms of student demographics and pedagogical approaches. We propose that biology instructors can use the TOSLS to evaluate their students’ proficiencies in using scientific literacy skills and to document the impacts of curricular reform on students’ scientific literacy. PMID:23222832

Mining TCGA Data Using Boolean Implications

PubMed Central

Sinha, Subarna; Tsang, Emily K.; Zeng, Haoyang; Meister, Michela; Dill, David L.

2014-01-01

Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/. PMID:25054200

Multivariate ordination identifies vegetation types associated with spider conservation in brassica crops

PubMed Central

Saqib, Hafiz Sohaib Ahmed; You, Minsheng

2017-01-01

Conservation biological control emphasizes natural and other non-crop vegetation as a source of natural enemies to focal crops. There is an unmet need for better methods to identify the types of vegetation that are optimal to support specific natural enemies that may colonize the crops. Here we explore the commonality of the spider assemblage—considering abundance and diversity (H)—in brassica crops with that of adjacent non-crop and non-brassica crop vegetation. We employ spatial-based multivariate ordination approaches, hierarchical clustering and spatial eigenvector analysis. The small-scale mixed cropping and high disturbance frequency of southern Chinese vegetation farming offered a setting to test the role of alternate vegetation for spider conservation. Our findings indicate that spider families differ markedly in occurrence with respect to vegetation type. Grassy field margins, non-crop vegetation, taro and sweetpotato harbour spider morphospecies and functional groups that are also present in brassica crops. In contrast, pumpkin and litchi contain spiders not found in brassicas, and so may have little benefit for conservation biological control services for brassicas. Our findings also illustrate the utility of advanced statistical approaches for identifying spatial relationships between natural enemies and the land uses most likely to offer alternative habitats for conservation biological control efforts that generates testable hypotheses for future studies. PMID:29085741

Networks’ Characteristics Matter for Systems Biology

PubMed Central

Rider, Andrew K.; Milenković, Tijana; Siwo, Geoffrey H.; Pinapati, Richard S.; Emrich, Scott J.; Ferdig, Michael T.; Chawla, Nitesh V.

2015-01-01

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations. Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology. PMID:26500772

Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

NASA Astrophysics Data System (ADS)

Leiserson, Mark D. M.; Tatar, Diana; Cowen, Lenore J.; Hescott, Benjamin J.

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Inferring mechanisms of compensation from E-MAP and SGA data using local search algorithms for max cut.

PubMed

Leiserson, Mark D M; Tatar, Diana; Cowen, Lenore J; Hescott, Benjamin J

2011-11-01

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions.

PubMed

Hoban, Sean; Kelley, Joanna L; Lotterhos, Katie E; Antolin, Michael F; Bradburd, Gideon; Lowry, David B; Poss, Mary L; Reed, Laura K; Storfer, Andrew; Whitlock, Michael C

2016-10-01

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species' demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

Cytotoxic and genotoxic potential of drinking water: a comparison between two different concentration methods.

PubMed

Buschini, Annamaria; Giordani, Federica; Pellacani, Claudia; Rossi, Carlo; Poli, Paola

2008-04-01

The level of exposure to hazardous compounds through drinking water is low but it is maintained throughout life, therefore representing a risk factor for human health. The use of techniques averaging the consumer's exposure over time could be more useful than relying on intermittent grab samples that may misrepresent average tap water concentrations due to short-term temporal variability. In this study, we compared the induction of in vitro cytotoxic and genotoxic effects (DNA damage by the comet assay) in relation to different sampling methods, i.e. exposure over time (semipermeable membrane devices, SPMDs, exposed for 30 days) or intermittent grab samples (5 weekly water sampling, C18 concentration). Waters with different chemical characteristics were sampled to test the sensitivity of the two methods. We did not found any positive correlation between the biological findings and water chemical parameters. SPMD extracts induced a significantly greater DNA damage than C18. The different behaviour was specially found for the water samples with a low level of organic compounds and when C18 extracts were highly cytotoxic. Our findings suggest that SPMD could be of a great interest in assessing genotoxic contaminants in both raw and drinking water, with great suitability for continuous monitoring. Furthermore, the results of this study have confirmed the great importance of the biological assays in evaluating the effects of a complex mixture such as water in addition to the conventional chemical examination of water quality.

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track

PubMed Central

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users—learning BEL, working with a completely new interface, and performing complex curation—a score so close to the overall SUS average highlights the usability of BELIEF. Database URL: BELIEF is available at http://www.scaiview.com/belief/ PMID:27694210

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.

PubMed

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/. © The Author(s) 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Ethics and methods for biological rhythm research on animals and human beings.

PubMed

Portaluppi, Francesco; Smolensky, Michael H; Touitou, Yvan

2010-10-01

This article updates the ethical standards and methods for the conduct of high-quality animal and human biological rhythm research, which should be especially useful for new investigators of the rhythms of life. The editors of Chronobiology International adhere to and endorse the Code of Conduct and Best Practice Guidelines of the Committee On Publication Ethics (COPE), which encourages communication of such updates at regular intervals in the journal. The journal accepts papers representing original work, no part of which was previously submitted for publication elsewhere, except as brief abstracts, as well as in-depth reviews. The majority of research papers published in Chronobiology International entails animal and human investigations. The editors and readers of the journal expect authors of submitted manuscripts to have made an important contribution to the research of biological rhythms and related phenomena using ethical methods/procedures and unbiased, accurate, and honest reporting of findings. Authors of scientific papers are required to declare all potential conflicts of interest. The journal and its editors endorse compliance of investigators to the Guide for the Care and Use of Laboratory Animals of the Institute for Laboratory Animal Research of the National Research Council, relating to the conduct of ethical research on laboratory and other animals, and the principles of the Declaration of Helsinki of the World Medical Association, relating to the conduct of ethical research on human beings. The peer review of manuscripts by Chronobiology International thus includes judgment as to whether or not the protocols and methods conform to ethical standards. Authors are expected to show mastery of the basic methods and procedures of biological rhythm research and proper statistical assessment of data, including the appropriate application of time series data analyses, as briefly reviewed in this article. The journal editors strive to consistently achieve high standards for the research of original and review papers reported in Chronobiology International, and current examples of expectations are presented herein.

Self consistency grouping: a stringent clustering method

PubMed Central

2012-01-01

Background Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency. Methods Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently. Results Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision. Conclusions SCG has potential for finding biological relationships under stringent conditions. PMID:23320864

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

PubMed Central

2012-01-01

Background Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. PMID:22830977

How is the Inquiry Skills of Biology Preservice Teachers in Biotechnology Lecture?

NASA Astrophysics Data System (ADS)

Hayat, M. S.; Rustaman, N. Y.

2017-09-01

This study was to investigate the inquiry skills of biology pre-service teachers in one teachers college in Central Java in biotechnology lecture. The method used is a case study of 29 biology preservice teacher. Data were collected using observation sheets, questionnaires, and interview guidelines. Research findings collected through questionnaires show that most students are accustomed to asking questions and formulating biotechnology issues; Skilled in conducting experiments; Skilled in obtaining relevant information from various sources; As well as skilled at processing, analyzing and interpreting data. Based on observation: lectures are not dominated by lecturers, students are able to solve problems encountered and conduct investigations. Based on the interview towards lecturers: students are always actively involved in questioning, investigation, inquiry, problem solving and experimenting in lectures. Why do most students show good inquiry skills? Because students are accustomed to invited inquiry in biology lectures. The impact, the students become more ready to be invited to do more advanced inquiry, such as real-world application inquiry, because the skill of inquiry is essentially trained.

A group LASSO-based method for robustly inferring gene regulatory networks from multiple time-course datasets.

PubMed

Liu, Li-Zhi; Wu, Fang-Xiang; Zhang, Wen-Jun

2014-01-01

As an abstract mapping of the gene regulations in the cell, gene regulatory network is important to both biological research study and practical applications. The reverse engineering of gene regulatory networks from microarray gene expression data is a challenging research problem in systems biology. With the development of biological technologies, multiple time-course gene expression datasets might be collected for a specific gene network under different circumstances. The inference of a gene regulatory network can be improved by integrating these multiple datasets. It is also known that gene expression data may be contaminated with large errors or outliers, which may affect the inference results. A novel method, Huber group LASSO, is proposed to infer the same underlying network topology from multiple time-course gene expression datasets as well as to take the robustness to large error or outliers into account. To solve the optimization problem involved in the proposed method, an efficient algorithm which combines the ideas of auxiliary function minimization and block descent is developed. A stability selection method is adapted to our method to find a network topology consisting of edges with scores. The proposed method is applied to both simulation datasets and real experimental datasets. It shows that Huber group LASSO outperforms the group LASSO in terms of both areas under receiver operating characteristic curves and areas under the precision-recall curves. The convergence analysis of the algorithm theoretically shows that the sequence generated from the algorithm converges to the optimal solution of the problem. The simulation and real data examples demonstrate the effectiveness of the Huber group LASSO in integrating multiple time-course gene expression datasets and improving the resistance to large errors or outliers.

An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI).

PubMed

Cho, Il Haeng; Park, Kyung S; Lim, Chang Joo

2010-02-01

In this study, we described the characteristics of five different biological age (BA) estimation algorithms, including (i) multiple linear regression, (ii) principal component analysis, and somewhat unique methods developed by (iii) Hochschild, (iv) Klemera and Doubal, and (v) a variant of Klemera and Doubal's method. The objective of this study is to find the most appropriate method of BA estimation by examining the association between Work Ability Index (WAI) and the differences of each algorithm's estimates from chronological age (CA). The WAI was found to be a measure that reflects an individual's current health status rather than the deterioration caused by a serious dependency with the age. Experiments were conducted on 200 Korean male participants using a BA estimation system developed principally under the concept of non-invasive, simple to operate and human function-based. Using the empirical data, BA estimation as well as various analyses including correlation analysis and discriminant function analysis was performed. As a result, it had been confirmed by the empirical data that Klemera and Doubal's method with uncorrelated variables from principal component analysis produces relatively reliable and acceptable BA estimates. 2009 Elsevier Ireland Ltd. All rights reserved.

Quantitative phase imaging of biological cells and tissues using singleshot white light interference microscopy and phase subtraction method for extended range of measurement

NASA Astrophysics Data System (ADS)

Mehta, Dalip Singh; Sharma, Anuradha; Dubey, Vishesh; Singh, Veena; Ahmad, Azeem

2016-03-01

We present a single-shot white light interference microscopy for the quantitative phase imaging (QPI) of biological cells and tissues. A common path white light interference microscope is developed and colorful white light interferogram is recorded by three-chip color CCD camera. The recorded white light interferogram is decomposed into the red, green and blue color wavelength component interferograms and processed it to find out the RI for different color wavelengths. The decomposed interferograms are analyzed using local model fitting (LMF)" algorithm developed for reconstructing the phase map from single interferogram. LMF is slightly off-axis interferometric QPI method which is a single-shot method that employs only a single image, so it is fast and accurate. The present method is very useful for dynamic process where path-length changes at millisecond level. From the single interferogram a wavelength-dependent quantitative phase imaging of human red blood cells (RBCs) are reconstructed and refractive index is determined. The LMF algorithm is simple to implement and is efficient in computation. The results are compared with the conventional phase shifting interferometry and Hilbert transform techniques.

Multiple network alignment via multiMAGNA+.

PubMed

Vijayan, Vipin; Milenkovic, Tijana

2017-08-21

Network alignment (NA) aims to find a node mapping that identifies topologically or functionally similar network regions between molecular networks of different species. Analogous to genomic sequence alignment, NA can be used to transfer biological knowledge from well- to poorly-studied species between aligned network regions. Pairwise NA (PNA) finds similar regions between two networks while multiple NA (MNA) can align more than two networks. We focus on MNA. Existing MNA methods aim to maximize total similarity over all aligned nodes (node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Directly optimizing edge conservation during alignment construction in addition to node conservation may result in superior alignments. Thus, we present a novel MNA method called multiMAGNA++ that can achieve this. Indeed, multiMAGNA++ outperforms or is on par with existing MNA methods, while often completing faster than existing methods. That is, multiMAGNA++ scales well to larger network data and can be parallelized effectively. During method evaluation, we also introduce new MNA quality measures to allow for more fair MNA method comparison compared to the existing alignment quality measures. MultiMAGNA++ code is available on the method's web page at http://nd.edu/~cone/multiMAGNA++/.

Optimal water networks in protein cavities with GAsol and 3D-RISM.

PubMed

Fusani, Lucia; Wall, Ian; Palmer, David; Cortes, Alvaro

2018-06-01

Water molecules in protein binding sites play essential roles in biological processes. The popular 3D-RISM prediction method can calculate the solvent density distribution within minutes, but is difficult to convert it into explicit water molecules. We present GAsol, a tool that is capable of finding the network of water molecules that best fits a particular 3D-RISM density distribution in a fast and accurate manner and that outperforms other available tools by finding the globally optimal solution thanks to its genetic algorithm. https://github.com/accsc/GAsol. BSD 3-clauses license. alvaro.x.cortes@gsk.com. Supplementary data are available at Bioinformatics online.

Spatial capture-recapture

USGS Publications Warehouse

Royle, J. Andrew; Chandler, Richard B.; Sollmann, Rahel; Gardner, Beth

2013-01-01

Spatial Capture-Recapture provides a revolutionary extension of traditional capture-recapture methods for studying animal populations using data from live trapping, camera trapping, DNA sampling, acoustic sampling, and related field methods. This book is a conceptual and methodological synthesis of spatial capture-recapture modeling. As a comprehensive how-to manual, this reference contains detailed examples of a wide range of relevant spatial capture-recapture models for inference about population size and spatial and temporal variation in demographic parameters. Practicing field biologists studying animal populations will find this book to be a useful resource, as will graduate students and professionals in ecology, conservation biology, and fisheries and wildlife management.

Hydatid detection using the near-infrared transmission angular spectra of porous silicon microcavity biosensors

NASA Astrophysics Data System (ADS)

Li, Peng; Jia, Zhenhong; Lü, Guodong

2017-03-01

Hydatid, which is a parasitic disease, occurs today in many regions worldwide. Because it can present a serious threat to people’s health, finding a fast, convenient, and economical means of detection is important. This paper proposes a label- and spectrophotometer-free apparatus that uses optical biological detection based on porous silicon microcavities. In this approach, the refractive index change induced by the biological reactions of a sample in a porous silicon microcavity is detected by measuring the change in the incidence angle corresponding to the maximum transmitted intensity of a near-infrared probe laser. This paper reports that the proposed method can achieve the label-free detection of 43 kDa molecular weight hydatid disease antigens with high sensitivity.

Circulating tumor cells in breast cancer.

PubMed

Bidard, Francois-Clement; Proudhon, Charlotte; Pierga, Jean-Yves

2016-03-01

Over the past decade, technically reliable circulating tumor cell (CTC) detection methods allowed the collection of large datasets of CTC counts in cancer patients. These data can be used either as a dynamic prognostic biomarker or as tumor material for "liquid biopsy". Breast cancer appears to be the cancer type in which CTC have been the most extensively studied so far, with level-of-evidence-1 studies supporting the clinical validity of CTC count in both early and metastatic stage. This review summarizes and discusses the clinical results obtained in breast cancer patients, the issues faced by the molecular characterization of CTC and the biological findings about cancer biology and metastasis that were obtained from CTC. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

The quest for a new modelling framework in mathematical biology. Comment on "On the interplay between mathematics and biology: Hallmarks towards a new systems biology" by N. Bellomo et al.

NASA Astrophysics Data System (ADS)

Eftimie, Raluca

2015-03-01

One of the main unsolved problems of modern physics is finding a "theory of everything" - a theory that can explain, with the help of mathematics, all physical aspects of the universe. While the laws of physics could explain some aspects of the biology of living systems (e.g., the phenomenological interpretation of movement of cells and animals), there are other aspects specific to biology that cannot be captured by physics models. For example, it is generally accepted that the evolution of a cell-based system is influenced by the activation state of cells (e.g., only activated and functional immune cells can fight diseases); on the other hand, the evolution of an animal-based system can be influenced by the psychological state (e.g., distress) of animals. Therefore, the last 10-20 years have seen also a quest for a "theory of everything"-approach extended to biology, with researchers trying to propose mathematical modelling frameworks that can explain various biological phenomena ranging from ecology to developmental biology and medicine [1,2,6]. The basic idea behind this approach can be found in a few reviews on ecology and cell biology [6,7,9-11], where researchers suggested that due to the parallel between the micro-scale dynamics and the emerging macro-scale phenomena in both cell biology and in ecology, many mathematical methods used for ecological processes could be adapted to cancer modelling [7,9] or to modelling in immunology [11]. However, this approach generally involved the use of different models to describe different biological aspects (e.g., models for cell and animal movement, models for competition between cells or animals, etc.).

Methods for biological data integration: perspectives and challenges

PubMed Central

Gligorijević, Vladimir; Pržulj, Nataša

2015-01-01

Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different state-of-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development. PMID:26490630

Enabling students to learn: Design, implementation and assessment of a supplemental study strategies course for an introductory undergraduate biology course

NASA Astrophysics Data System (ADS)

Sriram, Jayanthi Sanjeevi

Attrition in the STEM disciplines is a national problem and one of the important reasons for this is student experiences in introductory courses. A myriad of factors influence students' experiences in those courses; inadequate student preparation is one of the most cited reasons. Incoming freshmen often lack the learning strategies required to meaningfully learn and succeed in college courses. Unfortunately, the instructors have limited time and/or have little experience in teaching learning strategies. In this paper, the design, implementation, and evaluation of a Supplemental Course (SC) model that emphasizes learning strategies is presented. SC was offered concurrently with the introductory biology courses for four consecutive semesters (fall 2011 to spring 2013); for 10 weeks in fall 2012 and 7 weeks in the other semesters at Miami University. 10 weeks SC began earlier in the semester than the shorter SC. This study evaluated the effects of the SC on students' (1) performance in the introductory biology course, (2) perceived changes in self-regulation and social support, and (3) experiences in the introductory biology course before, during, and after participation in the SC. A mixed methods approach was used to address these goals. A pre-post survey was administered to obtain students' use of self-regulation strategies and social-support data. Quantitative methods were utilized to analyze content exam grades and changes in self-regulation strategies and social-support. To explore the experiences of the students, semi-structured interviews were conducted, followed by analysis using grounded theory. The findings reveal that participants of the longer duration SC (with an earlier start date) significantly improved in content exam performance, perceived use of self-regulation strategies, and social support compared to the non-participants. Participants of the shorter duration SC (with a later start date) did not significantly improve in content exam performance compared to the non-participants, however, demonstrated lower failure and withdrawal rates in content course than the non-participants. Qualitative findings provided further support for changes in students' study habits after participation in the SC. Literature suggests the need for early intervention, which is a critical determinant of student success. Findings presented here support that need and suggest a model that can be implemented in a discipline specific manner, perhaps with modifications.

Application of laser chaos control methods to controlling thyroid-catatonic oscillations and burst firing of dopamine neurons

NASA Astrophysics Data System (ADS)

Duong-van, Minh

1993-11-01

A method of controlling chaotic to laminar flows in the Lorenz equations using fixed points dictated by minimizing the Lyapunov functional was proposed by Singer, Wang and Bau. Using different fixed points, we find that the solutions in a chaotic regime can also be periodic. Since the lasers equations are isomorphic to the Lorenz equations, we use this new method to control chaos when the laser is operated over the pump threshold. Furthermore, by solving the laser equations with an occasional proportional feedback mechanism, we recover the essential lasers controlling features experimentally discovered by Roy, Murphy, Jr., Maier, Gills and Hunt. This method of control chaos is now extended to various medical and biological systems.

Rectification of nanopores at surfaces

PubMed Central

Sa, Niya

2011-01-01

At the nanoscale, methods to measure surface charge can prove challenging. Herein we describe a general method to report surface charge through the measurement of ion current rectification of a nanopipette brought in close proximity to a charged substrate. This method is able to discriminate between charged cationic and anionic substrates when the nanopipette is brought within distances from ten to hundreds of nanometers from the surface. Further studies of the pH dependence on the observed rectification support a surface-induced mechanism and demonstrate the ability to further discriminate between cationic and nominally uncharged surfaces. This method could find application in measurement and mapping of heterogeneous surface charges and is particularly attractive for future biological measurements, where noninvasive, noncontact probing of surface charge will prove valuable. PMID:21675734

Differential impact of antiepileptic drugs on the effects of contraceptive methods on seizures: Interim findings of the epilepsy birth control registry.

PubMed

Herzog, Andrew G

2015-05-01

To present the interim findings of the Epilepsy Birth Control Registry (EBCR) regarding the impact of various contraceptive methods on seizures, stratified by antiepileptic drug (AED) type. This is an observational study that reports interim findings on the first 750 subjects. There are significantly greater relative risks (RR) for both seizure increase and decrease with hormonal contraception (HC) than with non-hormonal contraception (NHC). The rates of HC experiences associated with seizure increase (21.0%) are greater than with NHC (3.9%) (RR=5.39 [95% CI=3.77-7.73, p<0.0001]). The rates of HC experiences associated with seizure decrease (10.3%) are greater than with NHC (5.6%) (RR=1.85 [95% CI=1.30-2.62, p=0.0006]). While differences can reflect biological effects or reporting bias, the finding of a greater RR for seizure increase with hormonal patch than with combined oral contraceptive, perhaps related to the delivery of substantially higher concentrations of hormones, and a greater RR for seizure decrease with depomedroxyprogesterone, known to reduce seizure frequency when used in dosages which produce amenorrhea, support biological effects. All AED categories showed significantly higher frequencies of reports of seizure increase when combined with HC than with NHC. RR for seizure increase with HC was higher with valproate than with any other AED category. There were no significant differences among AEDs for seizure decrease with HC at this juncture of the study. Overall, NEIAEDs had the most favorable profile with regard to reports of seizure increase and decrease when used with HC. Interim EBCR findings suggest that contraception category and interactions between contraception category and AED category are predictive factors for changes in seizure frequency in WWE. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Finding electromagnetic and chemical enhancement factors of surface-enhanced Raman scattering.

PubMed

Dvoynenko, Mykhaylo M; Wang, Juen-Kai

2007-12-15

The authors report two methods to determine electromagnetic and chemical enhancement factors in surface-enhanced Raman scattering (SERS), which are based on saturation property and decay dynamics of photoluminescence and concurrent measurements of photoluminescence and resonance Raman scattering intensities. Considerations for experimental implementation are discussed. This study is expected to facilitate the understanding of SERS mechanisms and the advancement of the usage of SERS in chemical and biological sensor applications.

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

PubMed Central

Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang

2011-01-01

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases. PMID:21909426

Telling science’s stories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiley, H. S.

Every biologist has been frustrated by an inability to find a specific piece of information in the literature. You are planning an experiment and you want to know whether factor X modifies the cellular response to factor Y. How do you find this information? Reference books and review articles are little help because most are supremely superficial, and any specific information they might contain is hopelessly out of date (not to mention the problem with constantly changing biological nomenclature). Online searching is only useful if the data you are looking for happens to be in the title or abstract. Unlessmore » what you’re looking for is the main subject of the paper, perusing the literature is almost hopeless. So what’s the best way to find biological information? The universal struggle that biologists undergo to find information in published papers indicates that the literature is not the actual repository of most biological knowledge. Most useful information, it seems, is not actually written down, but is passed orally between investigators. In other words, the best way to find biological information is to talk to other scientists.« less

American Society for Radiation Oncology (ASTRO) Survey of Radiation Biology Educators in U.S. and Canadian Radiation Oncology Residency Programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosenstein, Barry S., E-mail: barry.rosenstein@mssm.ed; Department of Radiation Oncology, New York University School of Medicine, New York, NY; Held, Kathryn D.

2009-11-01

Purpose: To obtain, in a survey-based study, detailed information on the faculty currently responsible for teaching radiation biology courses to radiation oncology residents in the United States and Canada. Methods and Materials: In March-December 2007 a survey questionnaire was sent to faculty having primary responsibility for teaching radiation biology to residents in 93 radiation oncology residency programs in the United States and Canada. Results: The responses to this survey document the aging of the faculty who have primary responsibility for teaching radiation biology to radiation oncology residents. The survey found a dramatic decline with time in the percentage of educatorsmore » whose graduate training was in radiation biology. A significant number of the educators responsible for teaching radiation biology were not fully acquainted with the radiation sciences, either through training or practical application. In addition, many were unfamiliar with some of the organizations setting policies and requirements for resident education. Freely available tools, such as the American Society for Radiation Oncology (ASTRO) Radiation and Cancer Biology Practice Examination and Study Guides, were widely used by residents and educators. Consolidation of resident courses or use of a national radiation biology review course was viewed as unlikely by most programs. Conclusions: A high priority should be given to the development of comprehensive teaching tools to assist those individuals who have responsibility for teaching radiation biology courses but who do not have an extensive background in critical areas of radiobiology related to radiation oncology. These findings also suggest a need for new graduate programs in radiobiology.« less

DNA methylation age is not accelerated in brain or blood of subjects with schizophrenia.

PubMed

McKinney, Brandon C; Lin, Huang; Ding, Ying; Lewis, David A; Sweet, Robert A

2017-10-05

Individuals with schizophrenia (SZ) exhibit multiple premature age-related phenotypes and die ~20years prematurely. The accelerated aging hypothesis of SZ has been advanced to explain these observations, it posits that SZ-associated factors accelerate the progressive biological changes associated with normal aging. Testing the hypothesis has been limited by the absence of robust, meaningful, and multi-tissue measures of biological age. Recently, a method was described in which DNA methylation (DNAm) levels at 353 genomic sites are used to produce "DNAm age", an estimate of biological age with advantages over existing measures. We used this method and 3 publicly-available DNAm datasets, 1 from brain and 2 from blood, to test the hypothesis. The brain dataset was composed of data from the dorsolateral prefrontal cortex of 232 non-psychiatric control (NPC) and 195 SZ subjects. Blood dataset #1 was composed of data from whole blood of 304 NPC and 332 SZ subjects, and blood dataset #2 was composed of data from whole blood of 405 NPC and 260 SZ subjects. DNAm age and chronological age correlated strongly (r=0.92-0.95, p<0.0001) in both NPC and SZ subjects in all 3 datasets. DNAm age acceleration did not differ between NPC and SZ subjects in the brain dataset (t=0.52, p=0.60), blood dataset #1 (t=1.51, p=0.13), or blood dataset #2 (t=0.93, p=0.35). Consistent with our previous findings from a smaller study of postmortem brains, our findings suggest there is no acceleration of brain or blood aging in SZ and, thus, do not support the accelerated aging hypothesis of SZ. Copyright © 2017 Elsevier B.V. All rights reserved.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Study on Transformation of Ginsenosides in Different Methods

PubMed Central

Zheng, Meng-meng; Xu, Fang-xue; Li, Yu-juan; Xi, Xiao-zhi; Cui, Xiao-wei

2017-01-01

Ginseng is a traditional Chinese medicine and has the extensive pharmacological activity. Ginsenosides are the major constituent in ginseng and have the unique biological activity and medicinal value. Ginsenosides have the good effects on antitumor, anti-inflammatory, antioxidative and inhibition of the cell apoptosis. Studies have showed that the major ginsenosides could be converted into rare ginsenosides, which played a significant role in exerting pharmacological activity. However, the contents of some rare ginsenosides are very little. So it is very important to find the effective way to translate the main ginsenosides to rare ginsenosides. In order to provide the theoretical foundation for the transformation of ginsenoside in vitro, in this paper, many methods of the transformation of ginsenoside were summarized, mainly including physical methods, chemical methods, and biotransformation methods. PMID:29387726

Extracellular biosynthesis of platinum nanoparticles using the fungus Fusarium oxysporum.

PubMed

Syed, Asad; Ahmad, Absar

2012-09-01

Nanoscience is a blooming field and promises a better future. In order to fabricate nanoparticles in an eco-friendly and inexpensive manner, significant efforts are being made to replace the chemical and physical methods currently being used with the biological methods. Chemical methods are toxic while the physical ones are very expensive. Biological methods, apart from being cost-effective, also provide protein capped nanoparticles which are thus very stable, have good dispersity and do not flocculate, and may find use in various applications. The present work emphasizes on platinum nanoparticles synthesis protocol which occurs at ambient conditions. The fungus Fusarium oxysporum when incubated with hexachloroplatinic acid (H(2)PtCl(6)) in ambient conditions reduces the precursor and leads to the formation of stable extracellular platinum nanoparticles. The biosynthesis of platinum nanoparticles was monitored by UV-visible spectroscopy and these nanoparticles were completely characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The nanoparticles are in the size range of 5-30 nm and are stabilized by proteins present in the solution. The reduction process is believed to occur enzymatically, thus creating the possibility of a rational, fungal-based method for the synthesis of nanoparticles over a wide range of chemical compositions. Copyright © 2012 Elsevier B.V. All rights reserved.

Adjudin - A Male Contraceptive with Other Biological Activities

PubMed Central

Cheng, Yan-Ho; Xia, Weiliang; Wong, Elissa W.P.; Xie, Qian R.; Shao, Jiaxiang; Liu, Tengyuan; Quan, Yizhou; Zhang, Tingting; Yang, Xiao; Geng, Keyi; Silvestrini, Bruno; Cheng, Chuen-Yan

2018-01-01

Background Adjudin has been explored as a male contraceptive for the last 15 years since its initial synthesis in the late 1990s. More than 50 papers have been published and listed in PubMed in which its mechanism that induces exfoliation of germ cells from the seminiferous epithelium, such as its effects on actin microfilaments at the apical ES (ectoplasmic specialization, a testis-specific actin-rich anchoring junction) has been delineated. Objective Recent studies have demonstrated that, besides its activity to induce germ cell exfoliation from the seminiferous epithelium to cause reversible infertility in male rodents, adjudin possesses other biological activities, which include anti-cancer, anti-inflammation in the brain, and anti-ototoxicity induced by gentamicin in rodents. Results of these findings likely spark the interest of investigators to explore other medical use of this and other indazole-based compounds, possibly mediated by the signaling pathway(s) in the mitochondria of mammalian cells following treatment with adjudin. In this review, we carefully evaluate these recent findings. Methods Papers published and listed at www.pubmed.org and patents pertinent to adjudin and its related compounds were searched. Findings were reviewed and critically evaluated, and summarized herein. Results Adjudin is a novel compound that possesses anti-spermatogenetic activity. Furthermore, it possesses anti-cancer, anti-inflammation, anti-neurodegeneration, and anti-ototoxicity activities based on studies using different in vitro and in vivo models. Conclusion: Studies on adjudin should be expanded to better understand its biological activities so that it can become a useful drug for treatment of other ailments besides serving as a male contraceptive. PMID:26510796

Associations of Stressful Life Events and Social Strain With Incident Cardiovascular Disease in the Women's Health Initiative

PubMed Central

Kershaw, Kiarri N.; Brenes, Gretchen A.; Charles, Luenda E.; Coday, Mace; Daviglus, Martha L.; Denburg, Natalie L.; Kroenke, Candyce H.; Safford, Monika M.; Savla, Tina; Tindle, Hilary A.; Tinker, Lesley F.; Van Horn, Linda

2014-01-01

Background Epidemiologic studies have yielded mixed findings on the association of psychosocial stressors with cardiovascular disease (CVD) risk. In this study, we examined associations of stressful life events (SLE) and social strain with incident coronary heart disease (CHD) and stroke (overall, and for hemorrhagic and ischemic strokes) independent of sociodemographic characteristics, and we evaluated whether these relationships were explained by traditional behavioral and biological risk factors. Methods and Results Data from approximately 82 000 Women's Health Initiative Observational Study participants were used for the SLE and social strain analyses, respectively. Participants were followed for events for up to 18.0 years (median, 14.0). Separate Cox proportional hazards models were generated to estimate associations of each stress measure with incident CVD. After adjusting for sociodemographic characteristics and depressive symptoms, higher SLE and social strain were associated with higher incident CHD and stroke (each P trend <0.05). Hazard ratios and 95% confidence intervals were 1.12 (1.01, 1.25) for incident CHD and 1.14 (1.01, 1.28) for incident stroke among participants reporting high versus low SLE. Findings were similar for social strain. Associations were attenuated with further adjustment for mediating behavioral and biological risk factors. Findings were similar for associations of SLE with ischemic stroke and hemorrhagic stroke, but social strain was only associated with ischemic stroke. Conclusions Higher SLE and social strain were associated with higher incident CVD independent of sociodemographic factors and depressive symptoms, but not behavioral and biological risk factors. PMID:24973226

Prediction of missing links and reconstruction of complex networks

NASA Astrophysics Data System (ADS)

Zhang, Cheng-Jun; Zeng, An

2016-04-01

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

A calibration method for the higher modes of a micro-mechanical cantilever

NASA Astrophysics Data System (ADS)

Shatil, N. R.; Homer, M. E.; Picco, L.; Martin, P. G.; Payton, O. D.

2017-05-01

Micro-mechanical cantilevers are increasingly being used as a characterisation tool in both material and biological sciences. New non-destructive applications are being developed that rely on the information encoded within the cantilever's higher oscillatory modes, such as atomic force microscopy techniques that measure the non-topographic properties of a sample. However, these methods require the spring constants of the cantilever at higher modes to be known in order to quantify their results. Here, we show how to calibrate the micro-mechanical cantilever and find the effective spring constant of any mode. The method is uncomplicated to implement, using only the properties of the cantilever and the fundamental mode that are straightforward to measure.

Schematic and realistic biological motion identification in children with high-functioning autism spectrum disorder

PubMed Central

Wright, Kristyn; Kelley, Elizabeth; Poulin-Dubois, Diane

2014-01-01

Research investigating biological motion perception in children with ASD has revealed conflicting findings concerning whether impairments in biological motion perception exist. The current study investigated how children with high-functioning ASD (HF-ASD) performed on two tasks of biological motion identification: a novel schematic motion identification task and a point-light biological motion identification task. Twenty-two HFASD children were matched with 21 TD children on gender, non-verbal mental, and chronological, age (M years = 6.72). On both tasks, HF-ASD children performed with similar accuracy as TD children. Across groups, children performed better on animate than on inanimate trials of both tasks. These findings suggest that HF-ASD children's identification of both realistic and schematic biological motion identification is unimpaired. PMID:25395988

ESL students learning biology: The role of language and social interactions

NASA Astrophysics Data System (ADS)

Jaipal, Kamini

This study explored three aspects related to ESL students in a mainstream grade 11 biology classroom: (1) the nature of students' participation in classroom activities, (2) the factors that enhanced or constrained ESL students' engagement in social interactions, and (3) the role of language in the learning of science. Ten ESL students were observed over an eight-month period in this biology classroom. Data were collected using qualitative research methods such as participant observation, audio-recordings of lessons, field notes, semi-structured interviews, short lesson recall interviews and students' written work. The study was framed within sociocultural perspectives, particularly the social constructivist perspectives of Vygotsky (1962, 1978) and Wertsch (1991). Data were analysed with respect to the three research aspects. Firstly, the findings showed that ESL students' preferred and exhibited a variety of participation practices that ranged from personal-individual to socio-interactive in nature. Both personal-individual and socio-interactive practices appeared to support science and language learning. Secondly, the findings indicated that ESL students' engagement in classroom social interactions was most likely influenced by the complex interactions between a number of competing factors at the individual, interpersonal and community/cultural levels (Rogoff, Radziszewska, & Masiello, 1995). In this study, six factors that appeared to enhance or constrain ESL students' engagement in classroom social interactions were identified. These factors were socio-cultural factors, prior classroom practice, teaching practices, affective factors, English language proficiency, and participation in the research project. Thirdly, the findings indicated that language played a significant mediational role in ESL students' learning of science. The data revealed that the learning of science terms and concepts can be explained by a functional model of language that includes: (1) the use of discourse to construct meanings, (2) multiple semiotic representations of the thing/process, and (3) constructing taxonomies and ways of reasoning. Other important findings were: talking about language is integral to biology teaching and learning, ESL students' prior knowledge of everyday words does not necessarily help them interpret written questions on worksheets, and ESL students' prior knowledge of concepts in their first language does not necessarily support concept learning in the second language.

A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

PubMed

Guo, Wei-Feng; Zhang, Shao-Wu; Shi, Qian-Qian; Zhang, Cheng-Ming; Zeng, Tao; Chen, Luonan

2018-01-19

The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.

Network design and analysis for multi-enzyme biocatalysis.

PubMed

Blaß, Lisa Katharina; Weyler, Christian; Heinzle, Elmar

2017-08-10

As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.

[Biological behavior of hypopharyngeal carcinoma].

PubMed

Zhou, L X

1997-01-01

Hypopharyngeal squamous cell carcinomas (HPC) has an extremely poor prognosis. Characteristics of cell lines of head and neck squamous cell carcinomas including HPC were studied by various methods, e.g., chemosensitivity test and the immunohistochemistry staining method, to determine whether this poor prognosis is due to the biological behavior of this cancer. An HPC cell line was found to be resistant to anti tumor drugs, i.e., PEP, MTX and CPM and moderately sensitive to CDDP, 5-FU and ADM. Thermoresistance to hyperthermatic treatment and weak expression of ICAM-1 on the HPC cell line were observed. DNA synthesis by the HPC cell line was induced by stimulation with a low concentration of EGF and the amount of EGFR on these HPC cells was very high. In addition, cyclinD1 overexpression was found in the HPC cell line. Based on the above findings, further analysis of hypopharyngeal carcinoma cells and the development of a new treatment modality to control tumor growth and metastatic factors influencing the poor outcome are necessary to improve the prognosis of this cancer.

Cancer genetics meets biomolecular mechanism-bridging an age-old gulf.

PubMed

González-Sánchez, Juan Carlos; Raimondi, Francesco; Russell, Robert B

2018-02-01

Increasingly available genomic sequencing data are exploited to identify genes and variants contributing to diseases, particularly cancer. Traditionally, methods to find such variants have relied heavily on allele frequency and/or familial history, often neglecting to consider any mechanistic understanding of their functional consequences. Thus, while the set of known cancer-related genes has increased, for many, their mechanistic role in the disease is not completely understood. This issue highlights a wide gap between the disciplines of genetics, which largely aims to correlate genetic events with phenotype, and molecular biology, which ultimately aims at a mechanistic understanding of biological processes. Fortunately, new methods and several systematic studies have proved illuminating for many disease genes and variants by integrating sequencing with mechanistic data, including biomolecular structures and interactions. These have provided new interpretations for known mutations and suggested new disease-relevant variants and genes. Here, we review these approaches and discuss particular examples where these have had a profound impact on the understanding of human cancers. © 2018 Federation of European Biochemical Societies.

Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks.

PubMed

Blatti, Charles; Sinha, Saurabh

2016-07-15

Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR. blatti@illinois.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

The risk of serious infection with biologics in treating patients with rheumatoid arthritis: A Systematic Review and Meta-analysis

PubMed Central

Singh, Jasvinder A.; Cameron, Chris; Noorbaloochi, Shahrzad; Cullis, Tyler; Tucker, Matthew; Christensen, Robin; Ghogomu, Elizabeth Tanjong; Coyle, Doug; Clifford, Tammy; Tugwell, Peter; Wells, George A.

2015-01-01

Background Serious infections are a major concern for patients considering treatmentsfor rheumatoid arthritis (RA). Evidence is inconsistent on whether biologicsare associated with an increased risk of serious infection compared to traditional disease-modifying anti-rheumatic drugs (DMARDs). Methods A systematic literature search was undertaken using MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and www.clinicaltrials.gov from inception through February 11, 2014. Search terms included biologics, rheumatoid arthritis and their synonyms. Trials were eligible for inclusion if they included any of the biologics and reported serious infections. The risk of bias was assessed using the Cochrane Risk of Bias Tool. We conducted a Bayesian network meta-analysis,using a binomial likelihood model, of published trials to assess the risk of serious infections of biologics in RA patients, compared to traditional DMARDs. Findings The systematic review identified 106 trials that included RA patients on biologic and reported on serious infections. Compared to traditional DMARDs, standard-dose biologic (odds ratio [OR],1.31; 95% credible interval [CrI], 1.09 to 1.58) andhigh-dose biologic (OR, 1.90; 95% Crl, 1.50 to 2.39) were associated with an increased risk of serious infections, while low-dose biologics (OR, 0.93; 95% CrI, 0.65 to 1.33) were not. The risk was lower in patients who are methotrexate naïve compared withtraditional DMARD- or anti-TNF-biologic-experienced. The absolute increase in the number of serious infectionsper 1000 patients treated each year compared to traditional DMARDs ranged from 6 for standard-dose biologic to 55 for combination biologic therapy. Interpretation Standard-dose and high-dose biologics (with/without traditional DMARDs) are associated with an increase in serious infections compared to traditional DMARDs in RA, while low-dose biologics are not.Clinicians should discuss the balance between benefit and harm with the individual RA patient before initiating biologic therapy. Funding Rheumatology division at the University of Alabama at Birmingham. PMID:25975452

Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

PubMed Central

Leiserson, Mark D.M.; Tatar, Diana; Cowen, Lenore J.

2011-01-01

Abstract A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome. PMID:21882903

Spatially extended hybrid methods: a review

PubMed Central

2018-01-01

Many biological and physical systems exhibit behaviour at multiple spatial, temporal or population scales. Multiscale processes provide challenges when they are to be simulated using numerical techniques. While coarser methods such as partial differential equations are typically fast to simulate, they lack the individual-level detail that may be required in regions of low concentration or small spatial scale. However, to simulate at such an individual level throughout a domain and in regions where concentrations are high can be computationally expensive. Spatially coupled hybrid methods provide a bridge, allowing for multiple representations of the same species in one spatial domain by partitioning space into distinct modelling subdomains. Over the past 20 years, such hybrid methods have risen to prominence, leading to what is now a very active research area across multiple disciplines including chemistry, physics and mathematics. There are three main motivations for undertaking this review. Firstly, we have collated a large number of spatially extended hybrid methods and presented them in a single coherent document, while comparing and contrasting them, so that anyone who requires a multiscale hybrid method will be able to find the most appropriate one for their need. Secondly, we have provided canonical examples with algorithms and accompanying code, serving to demonstrate how these types of methods work in practice. Finally, we have presented papers that employ these methods on real biological and physical problems, demonstrating their utility. We also consider some open research questions in the area of hybrid method development and the future directions for the field. PMID:29491179

Coloc-stats: a unified web interface to perform colocalization analysis of genomic features.

PubMed

Simovski, Boris; Kanduri, Chakravarthi; Gundersen, Sveinung; Titov, Dmytro; Domanska, Diana; Bock, Christoph; Bossini-Castillo, Lara; Chikina, Maria; Favorov, Alexander; Layer, Ryan M; Mironov, Andrey A; Quinlan, Aaron R; Sheffield, Nathan C; Trynka, Gosia; Sandve, Geir K

2018-06-05

Functional genomics assays produce sets of genomic regions as one of their main outputs. To biologically interpret such region-sets, researchers often use colocalization analysis, where the statistical significance of colocalization (overlap, spatial proximity) between two or more region-sets is tested. Existing colocalization analysis tools vary in the statistical methodology and analysis approaches, thus potentially providing different conclusions for the same research question. As the findings of colocalization analysis are often the basis for follow-up experiments, it is helpful to use several tools in parallel and to compare the results. We developed the Coloc-stats web service to facilitate such analyses. Coloc-stats provides a unified interface to perform colocalization analysis across various analytical methods and method-specific options (e.g. colocalization measures, resolution, null models). Coloc-stats helps the user to find a method that supports their experimental requirements and allows for a straightforward comparison across methods. Coloc-stats is implemented as a web server with a graphical user interface that assists users with configuring their colocalization analyses. Coloc-stats is freely available at https://hyperbrowser.uio.no/coloc-stats/.

Finding and estimating chemical property data for environmental assessment.

PubMed

Boethling, Robert S; Howard, Philip H; Meylan, William M

2004-10-01

The ability to predict the behavior of a chemical substance in a biological or environmental system largely depends on knowledge of the physicochemical properties and reactivity of that substance. We focus here on properties, with the objective of providing practical guidance for finding measured values and using estimation methods when necessary. Because currently available computer software often makes it more convenient to estimate than to retrieve measured values, we try to discourage irrational exuberance for these tools by including comprehensive lists of Internet and hard-copy data resources. Guidance for assessors is presented in the form of a process to obtain data that includes establishment of chemical identity, identification of data sources, assessment of accuracy and reliability, substructure searching for analogs when experimental data are unavailable, and estimation from chemical structure. Regarding property estimation, we cover estimation from close structural analogs in addition to broadly applicable methods requiring only the chemical structure. For the latter, we list and briefly discuss the most widely used methods. Concluding thoughts are offered concerning appropriate directions for future work on estimation methods, again with an emphasis on practical applications.

Eyes Wide Shut: the impact of dim-light vision on neural investment in marine teleosts.

PubMed

Iglesias, Teresa L; Dornburg, Alex; Warren, Dan L; Wainwright, Peter C; Schmitz, Lars; Economo, Evan P

2018-05-28

Understanding how organismal design evolves in response to environmental challenges is a central goal of evolutionary biology. In particular, assessing the extent to which environmental requirements drive general design features among distantly related groups is a major research question. The visual system is a critical sensory apparatus that evolves in response to changing light regimes. In vertebrates, the optic tectum is the primary visual processing centre of the brain and yet it is unclear how or whether this structure evolves while lineages adapt to changes in photic environment. On one hand, dim-light adaptation is associated with larger eyes and enhanced light-gathering power that could require larger information processing capacity. On the other hand, dim-light vision may evolve to maximize light sensitivity at the cost of acuity and colour sensitivity, which could require less processing power. Here, we use X-ray microtomography and phylogenetic comparative methods to examine the relationships between diel activity pattern, optic morphology, trophic guild and investment in the optic tectum across the largest radiation of vertebrates-teleost fishes. We find that despite driving the evolution of larger eyes, enhancement of the capacity for dim-light vision generally is accompanied by a decrease in investment in the optic tectum. These findings underscore the importance of considering diel activity patterns in comparative studies and demonstrate how vision plays a role in brain evolution, illuminating common design principles of the vertebrate visual system. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Two mothers and a donor: exploration of children’s family concepts in lesbian households

PubMed Central

Raes, I.; Van Parys, H.; Provoost, V.; Buysse, A.; De Sutter, P.; Pennings, G.

2015-01-01

Background: Although children from lesbian families appear to make a distinction between a residential father and a donor, defining these two concepts seems to be a challenge. They need to appeal to more familiar concepts such as the hetero-normative concept of ‘mother’ to give a definition of the unfamiliar concepts they are confronted with. Methods: The study is based on qualitative in-depth interviews with 6 children (9-10 years old) from lesbian families, all of which have been conceived using anonymous sperm donation. Semi-structured interviews were conducted. Results: Two findings stand out. First, in defining the concepts of biological and non-biological mother, both mothers were described as equal parents. No difference was attached by the children to the mothers’ position as a parent. Second, the concepts ‘non-biological mother’ and ‘donor’ were defined by looking at the hetero-normative concepts of ‘mummy’ and ‘daddy’. To define the non-biological mother, both a ‘mummy’ and a ‘daddy’ were used as a reference. To define the donor concept, often references were made to a daddy. This comparison with a ‘daddy’ turned out to be complex due to the conflict between the role as a progenitor and the lack of a social relationship. The lack of language surrounding this concept turned out to be difficult. Wider implications of the findings: This study illustrates the complexity and ambiguity of children‘s experiences and perceptions when dealing with issues related to genetic and social parenthood. PMID:26175886

How Four Scientists Integrate Thermodynamic and Kinetic Theory, Context, Analogies, and Methods in Protein-Folding and Dynamics Research: Implications for Biochemistry Instruction.

PubMed

Jeffery, Kathleen A; Pelaez, Nancy; Anderson, Trevor R

2018-01-01

To keep biochemistry instruction current and relevant, it is crucial to expose students to cutting-edge scientific research and how experts reason about processes governed by thermodynamics and kinetics such as protein folding and dynamics. This study focuses on how experts explain their research into this topic with the intention of informing instruction. Previous research has modeled how expert biologists incorporate research methods, social or biological context, and analogies when they talk about their research on mechanisms. We used this model as a guiding framework to collect and analyze interview data from four experts. The similarities and differences that emerged from analysis indicate that all experts integrated theoretical knowledge with their research context, methods, and analogies when they explained how phenomena operate, in particular by mapping phenomena to mathematical models; they explored different processes depending on their explanatory aims, but readily transitioned between different perspectives and explanatory models; and they explained thermodynamic and kinetic concepts of relevance to protein folding in different ways that aligned with their particular research methods. We discuss how these findings have important implications for teaching and future educational research. © 2018 K. A. Jeffery et al. CBE—Life Sciences Education © 2018 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Detection of Patient Subgroups with Differential Expression in Omics Data: A Comprehensive Comparison of Univariate Measures

PubMed Central

Ahrens, Maike; Turewicz, Michael; Casjens, Swaantje; May, Caroline; Pesch, Beate; Stephan, Christian; Woitalla, Dirk; Gold, Ralf; Brüning, Thomas; Meyer, Helmut E.

2013-01-01

Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can be characterized by differential expression for only a subgroup-specific set of genes or proteins. Finding such genes and corresponding patient subgroups can help in understanding pathological pathways, diagnosis and defining drug targets. The size of the subgroup and the type of differential expression determine the optimal strategy for subgroup identification. To date, commonly used software packages hardly provide statistical tests and methods for the detection of such subgroups. Different univariate methods for subgroup detection are characterized and compared, both on simulated and on real data. We present an advanced design for simulation studies: Data is simulated under different distributional assumptions for the expression of the subgroup, and performance results are compared against theoretical upper bounds. For each distribution, different degrees of deviation from the majority of observations are considered for the subgroup. We evaluate classical approaches as well as various new suggestions in the context of omics data, including outlier sum, PADGE, and kurtosis. We also propose the new FisherSum score. ROC curve analysis and AUC values are used to quantify the ability of the methods to distinguish between genes or proteins with and without certain subgroup patterns. In general, FisherSum for small subgroups and -test for large subgroups achieve best results. We apply each method to a case-control study on Parkinson's disease and underline the biological benefit of the new method. PMID:24278130

Alignment of dynamic networks.

PubMed

Vijayan, V; Critchlow, D; Milenkovic, T

2017-07-15

Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. http://nd.edu/∼cone/DynaMAGNA++/ . tmilenko@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Alignment of dynamic networks

PubMed Central

Vijayan, V.; Critchlow, D.; Milenković, T.

2017-01-01

Abstract Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results: For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation: http://nd.edu/∼cone/DynaMAGNA++/. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881980

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

PubMed

Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

Mass-spectrometric identification of primary biological particle markers and application to pristine submicron aerosol measurements in Amazonia

NASA Astrophysics Data System (ADS)

Schneider, J.; Freutel, F.; Zorn, S. R.; Chen, Q.; Farmer, D. K.; Jimenez, J. L.; Martin, S. T.; Artaxo, P.; Wiedensohler, A.; Borrmann, S.

2011-11-01

The detection of primary biological material in submicron aerosol by means of thermal desorption/electron impact ionization aerosol mass spectrometry was investigated. Mass spectra of amino acids, carbohydrates, small peptides, and proteins, all of which are key building blocks of biological particles, were recorded in laboratory experiments. Several characteristic marker fragments were identified. The intensity of the marker signals relative to the total organic mass spectrum allows for an estimation of the content of primary biological material in ambient organic aerosol. The developed method was applied to mass spectra recorded during AMAZE-08, a field campaign conducted in the pristine rainforest of the central Amazon Basin, Brazil, during the wet season of February and March 2008. The low abundance of identified marker fragments places upper limits of 7.5% for amino acids and 5.6% for carbohydrates on the contribution of primary biological aerosol particles (PBAP) to the submicron organic aerosol mass concentration during this time period. Upper limits for the absolute submicron concentrations for both compound classes range from 0.01 to 0.1 μg m-3. Carbohydrates and proteins (composed of amino acids) make up for about two thirds of the dry mass of a biological cell. Thus, our findings suggest an upper limit for the PBAP mass fraction of about 20% to the submicron organic aerosol measured in Amazonia during AMAZE-08.

76 FR 13597 - Availability of an Environmental Assessment and Finding of No Significant Impact for a Biological...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-14

... Significant Impact for a Biological Control Agent for Hawkweeds AGENCY: Animal and Plant Health Inspection... Inspection Service (APHIS) has prepared an environmental assessment and finding of no significant impact... / Monday, March 14, 2011 / Notices#0;#0; [[Page 13597

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

Male Inmate Profiles and Their Biological Correlates

PubMed Central

Horn, Mathilde; Potvin, Stephane; Allaire, Jean-François; Côté, Gilles; Gobbi, Gabriella; Benkirane, Karim; Vachon, Jeanne; Dumais, Alexandre

2014-01-01

Objective: Borderline and antisocial personality disorders (PDs) share common clinical features (impulsivity, aggressiveness, substance use disorders [SUDs], and suicidal behaviours) that are greatly overrepresented in prison populations. These disorders have been associated biologically with testosterone and cortisol levels. However, the associations are ambiguous and the subject of controversy, perhaps because these heterogeneous disorders have been addressed as unitary constructs. A consideration of profiles of people, rather than of exclusive diagnoses, might yield clearer relationships. Methods: In our study, multiple correspondence analysis and cluster analysis were employed to identify subgroups among 545 newly convicted inmates. The groups were then compared in terms of clinical features and biological markers, including levels of cortisol, testosterone, estradiol, progesterone, and sulfoconjugated dehydroepiandrosterone (DHEA-S). Results: Four clusters with differing psychiatric, criminal, and biological profiles emerged. Clinically, one group had intermediate scores for each of the tested clinical features. Another group comprised people with little comorbidity. Two others displayed severe impulsivity, PD, and SUD. Biologically, cortisol levels were lowest in the last 2 groups and highest in the group with less comorbidity. In keeping with previous findings reported in the literature, testosterone was higher in a younger population with severe psychiatric symptoms. However, some apparently comparable behavioural outcomes were found to be related to distinct biological profiles. No differences were observed for estradiol, progesterone, or DHEA-S levels. Conclusions: The results not only confirm the importance of biological markers in the study of personality features but also demonstrate the need to consider the role of comorbidities and steroid coregulation. PMID:25161069

Students' Studying and Approaches to Learning in Introductory Biology

PubMed Central

2004-01-01

This exploratory study was conducted in an introductory biology course to determine 1) how students used the large lecture environment to create their own learning tasks during studying and 2) whether meaningful learning resulted from the students' efforts. Academic task research from the K–12 education literature and student approaches to learning research from the postsecondary education literature provided the theoretical framework for the mixed methods study. The subject topic was cell division. Findings showed that students 1) valued lectures to develop what they believed to be their own understanding of the topic; 2) deliberately created and engaged in learning tasks for themselves only in preparation for the unit exam; 3) used course resources, cognitive operations, and study strategies that were compatible with surface and strategic, rather than deep, approaches to learning; 4) successfully demonstrated competence in answering familiar test questions aligned with their surface and strategic approaches to studying and learning; and 5) demonstrated limited meaningful understanding of the significance of cell division processes. Implications for introductory biology education are discussed. PMID:15592598

Middle/high school students in the research laboratory: A summer internship program emphasizing the interdisciplinary nature of biology.

PubMed

McMiller, Tracee; Lee, Tameshia; Saroop, Ria; Green, Tyra; Johnson, Casonya M

2006-03-01

We describe an eight-week summer Young Scientist in Training (YSIT) internship program involving middle and high school students. This program exposed students to current basic research in molecular genetics, while introducing or reinforcing principles of the scientific method and demonstrating the uses of mathematics and chemistry in biology. For the laboratory-based program, selected students from Baltimore City Schools working in groups of three were teamed with undergraduate research assistants at Morgan State University. Teams were assigned a project that was indirectly related to our laboratory research on the characterization of gene expression in Caenorhabditis elegans. At the end of the program, teams prepared posters detailing their accomplishments, and presented their findings to parents and faculty members during a mini-symposium. The posters were also submitted to the respective schools and the interns were offered a presentation of their research at local high school science fairs. Copyright © 2006 International Union of Biochemistry and Molecular Biology, Inc.

SU-F-R-01: Preclinical Radioimmunogenomics Study to Design Personalized Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdollahi, H

2016-06-15

Purpose: Radiogenomics is an active area of research to find clinical correlation between genomics and radiotherapy outcomes. In this era, many different biological issues should be taken into account. In this study we aimed to introduce “Radioimmunogenomics” as a new approach to study immunogetics issue regard to radiotherapy induced clinical manifestations. Methods: We studied different immunological pathways and signaling molecules which underling radiation response of normal and malignant tissues. In the other hand, we found many genes and proteins are responsible to radiation effects on biological tissues. We defined a theoretical framework to correlate these genes with radiotherapy outcomes asmore » TCP and NTCP biological dose tools. Results: Our theoretical results showed, high-throughput immunogenomics biomarkers can be correlated with radiotherapy outcomes. Genes regarding to inflammation, apoptosis, repair molecules and many other immunological markers can be defined as radioimmune markers to predict radiotherapy response. Conclusion: Radioimmunogenomics can be used as a new personalized radiotherapy research area to enhance treatment outcome as well as quality of life.« less

TURNING IT UPSIDE DOWN: AREAS OF PRESERVED COGNITIVE FUNCTION IN SCHIZOPHRENIA

PubMed Central

Gold, James M.; Hahn, Britta; Strauss, Gregory P.; Waltz, James A.

2013-01-01

Patients with schizophrenia demonstrate marked impairments on most clinical neuropsychological tests. These findings suggest that patients suffer from a generalized form of cognitive impairment, with little evidence of spared performance documented in several large meta-analytic reviews of the clinical literature. In contrast, we review evidence for relative sparing of aspects of attention, procedural memory, and emotional processing observed in studies that have employed experimental approaches adapted from the cognitive and affective neuroscience literature. These islands of preserved performance suggest that the cognitive deficits in schizophrenia are not as general as they appear to be when assayed with clinical neuropsychological methods. The apparent contradiction in findings across methods may offer important clues about the nature of cognitive impairment in schizophrenia. The documentation of preserved cognitive function in schizophrenia may serve to sharpen hypotheses about the biological mechanisms that are implicated in the illness. PMID:19452280

Stem cell biology and drug discovery

PubMed Central

2011-01-01

There are many reasons to be interested in stem cells, one of the most prominent being their potential use in finding better drugs to treat human disease. This article focuses on how this may be implemented. Recent advances in the production of reprogrammed adult cells and their regulated differentiation to disease-relevant cells are presented, and diseases that have been modeled using these methods are discussed. Remaining difficulties are highlighted, as are new therapeutic insights that have emerged. PMID:21649940

Small Groups in Programmed Environments: Behavioral and Biological Interactions.

DTIC Science & Technology

1983-04-01

DISTRIBUTION STATEMENT (of the abettdre entered in Block 20. it differm Iroi Repot) IS. SUPPLEMENTARY NOTES The Pavlovian Journal of Bioloqical Science, in...microsociety. Summarized are previous research emphases and findings in relationship to (1) conditions \\ DD FO 14c13 aj m-n€ or t nov as is ONscmTa. - DO...research on individual and group effectiveness under laboratory conditions would be advantaged by a more effective method for long-ter, analyses of human

Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

PubMed

Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

2016-08-01

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

Impact of wastewater treatment configuration and seasonal conditions on thyroid hormone disruption and stress effects in Rana catesbeiana tailfin.

PubMed

Wojnarowicz, Pola; Ogunlaja, Olumuyiwa O; Xia, Chen; Parker, Wayne J; Helbing, Caren C

2013-12-03

Improved endocrine disrupting compound (EDC) removal is desirable in municipal wastewater treatment plants (MWWTPs) although increased removal does not always translate into reduced biological activity. Suitable methods for determining reduction in biological activity of effluents are needed. In order to determine which MWWTPs are the most effective at removing EDC activities, we operated three configurations of pilot sized biological reactors (conventional activated sludge, CAS; nitrifying activated sludge, NAS; and biological nutrient removal, BNR) receiving the same influent under simulated winter and summer conditions. As frogs are model organisms for the study of thyroid hormone (TH) action, we used the North American species Rana catesbeiana in a cultured tadpole tailfin (C-fin) assay to compare the effluents. TH-responsive (thyroid hormone receptors alpha (thra) and beta (thrb)) and stress-responsive (superoxide dismutase, catalase, and heat shock protein 30) mRNA transcript levels were examined. Effluents infrequently perturbed stress-responsive transcript abundance but thra/thrb levels were significantly altered. In winter conditions, CAS caused frequent TH perturbations while BNR caused none. In summer conditions, however, BNR caused substantial TH perturbations while CAS caused few. Our findings contrast other studies of seasonal variations of EDC removal and accentuate the importance of utilizing appropriate biological readouts for assessing EDC activities.

A Photo-triggered and photo-calibrated nitric oxide donor: Rational design, spectral characterizations, and biological applications.

PubMed

He, Haihong; Liu, Yuxin; Zhou, Zhongneng; Guo, Chunlei; Wang, Hong-Yin; Wang, Zhuang; Wang, Xueli; Zhang, Ziqian; Wu, Fu-Gen; Wang, Haolu; Chen, Daijie; Yang, Dahai; Liang, Xiaowen; Chen, Jinquan; Zhou, Shengmin; Liang, Xin; Qian, Xuhong; Yang, Youjun

2018-04-27

Nitric oxide (NO) donors are valuable tools to probe the profound implications of NO in health and disease. The elusive nature of NO bio-relevance has largely limited the use of spontaneous NO donors and promoted the development of next generation NO donors, whose NO release is not only stimulated by a trigger, but also readily monitored via a judiciously built-in self-calibration mechanism. Light is without a doubt the most sensitive, versatile and biocompatible method of choice for both triggering and monitoring, for applications in complex biological matrices. Herein, we designed and synthesized an N-nitroso rhodamine derivative (NOD560) as a photo-triggered and photo-calibrated NO donor to address this need. NOD560 is essentially non-fluorescent. Upon irradiation by green light (532 nm), it efficiently release NO and a rhodamine dye, the dramatic fluorescence turn-on from which could be harnessed to conveniently monitor the localization, flux, and dose of NO release. The potentials of NOD560 for in vitro biological applications were also exemplified in in vitro biological models, i.e. mesenchymal stem cell (MSC) migration suppression. NOD560 is expected to complement the existing NO donors and find widespread applications in chemical biological studies. Copyright © 2018 Elsevier Inc. All rights reserved.

Cell migration analysis: A low-cost laboratory experiment for cell and developmental biology courses using keratocytes from fish scales.

PubMed

Prieto, Daniel; Aparicio, Gonzalo; Sotelo-Silveira, Jose R

2017-11-01

Cell and developmental processes are complex, and profoundly dependent on spatial relationships that change over time. Innovative educational or teaching strategies are always needed to foster deep comprehension of these processes and their dynamic features. However, laboratory exercises in cell and developmental biology at the undergraduate level do not often take into account the time dimension. In this article, we provide a laboratory exercise focused in cell migration, aiming to stimulate thinking in time and space dimensions through a simplification of more complex processes occurring in cell or developmental biology. The use of open-source tools for the analysis, as well as the whole package of raw results (available at http://github.com/danielprieto/keratocyte) make it suitable for its implementation in courses with very diverse budgets. Aiming to facilitate the student's transition from science-students to science-practitioners we propose an exercise of scientific thinking, and an evaluation method. This in turn is communicated here to facilitate the finding of common caveats and weaknesses in the process of producing simple scientific communications describing the results achieved. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(6):475-482, 2017. © 2017 The International Union of Biochemistry and Molecular Biology.

DrugQuest - a text mining workflow for drug association discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways.

PubMed

Kong, Wei; Mou, Xiaoyang; Di, Benteng; Deng, Jin; Zhong, Ruxing; Wang, Shuaiqun

2017-11-20

Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Method and apparatus for biological sequence comparison

DOEpatents

Marr, T.G.; Chang, W.I.

1997-12-23

A method and apparatus are disclosed for comparing biological sequences from a known source of sequences, with a subject (query) sequence. The apparatus takes as input a set of target similarity levels (such as evolutionary distances in units of PAM), and finds all fragments of known sequences that are similar to the subject sequence at each target similarity level, and are long enough to be statistically significant. The invention device filters out fragments from the known sequences that are too short, or have a lower average similarity to the subject sequence than is required by each target similarity level. The subject sequence is then compared only to the remaining known sequences to find the best matches. The filtering member divides the subject sequence into overlapping blocks, each block being sufficiently large to contain a minimum-length alignment from a known sequence. For each block, the filter member compares the block with every possible short fragment in the known sequences and determines a best match for each comparison. The determined set of short fragment best matches for the block provide an upper threshold on alignment values. Regions of a certain length from the known sequences that have a mean alignment value upper threshold greater than a target unit score are concatenated to form a union. The current block is compared to the union and provides an indication of best local alignment with the subject sequence. 5 figs.

Method and apparatus for biological sequence comparison

DOEpatents

Marr, Thomas G.; Chang, William I-Wei

1997-01-01

A method and apparatus for comparing biological sequences from a known source of sequences, with a subject (query) sequence. The apparatus takes as input a set of target similarity levels (such as evolutionary distances in units of PAM), and finds all fragments of known sequences that are similar to the subject sequence at each target similarity level, and are long enough to be statistically significant. The invention device filters out fragments from the known sequences that are too short, or have a lower average similarity to the subject sequence than is required by each target similarity level. The subject sequence is then compared only to the remaining known sequences to find the best matches. The filtering member divides the subject sequence into overlapping blocks, each block being sufficiently large to contain a minimum-length alignment from a known sequence. For each block, the filter member compares the block with every possible short fragment in the known sequences and determines a best match for each comparison. The determined set of short fragment best matches for the block provide an upper threshold on alignment values. Regions of a certain length from the known sequences that have a mean alignment value upper threshold greater than a target unit score are concatenated to form a union. The current block is compared to the union and provides an indication of best local alignment with the subject sequence.

Spectral embedding finds meaningful (relevant) structure in image and microarray data

PubMed Central

Higgs, Brandon W; Weller, Jennifer; Solka, Jeffrey L

2006-01-01

Background Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. Results We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. Conclusion Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology. PMID:16483359

Dispensing Processes Impact Apparent Biological Activity as Determined by Computational and Statistical Analyses

PubMed Central

Ekins, Sean; Olechno, Joe; Williams, Antony J.

2013-01-01

Dispensing and dilution processes may profoundly influence estimates of biological activity of compounds. Published data show Ephrin type-B receptor 4 IC50 values obtained via tip-based serial dilution and dispensing versus acoustic dispensing with direct dilution differ by orders of magnitude with no correlation or ranking of datasets. We generated computational 3D pharmacophores based on data derived by both acoustic and tip-based transfer. The computed pharmacophores differ significantly depending upon dispensing and dilution methods. The acoustic dispensing-derived pharmacophore correctly identified active compounds in a subsequent test set where the tip-based method failed. Data from acoustic dispensing generates a pharmacophore containing two hydrophobic features, one hydrogen bond donor and one hydrogen bond acceptor. This is consistent with X-ray crystallography studies of ligand-protein interactions and automatically generated pharmacophores derived from this structural data. In contrast, the tip-based data suggest a pharmacophore with two hydrogen bond acceptors, one hydrogen bond donor and no hydrophobic features. This pharmacophore is inconsistent with the X-ray crystallographic studies and automatically generated pharmacophores. In short, traditional dispensing processes are another important source of error in high-throughput screening that impacts computational and statistical analyses. These findings have far-reaching implications in biological research. PMID:23658723

Early indicators of exposure to biological threat agents using host gene profiles in peripheral blood mononuclear cells

PubMed Central

Das, Rina; Hammamieh, Rasha; Neill, Roger; Ludwig, George V; Eker, Steven; Lincoln, Patrick; Ramamoorthy, Preveen; Dhokalia, Apsara; Mani, Sachin; Mendis, Chanaka; Cummings, Christiano; Kearney, Brian; Royaee, Atabak; Huang, Xiao-Zhe; Paranavitana, Chrysanthi; Smith, Leonard; Peel, Sheila; Kanesa-Thasan, Niranjan; Hoover, David; Lindler, Luther E; Yang, David; Henchal, Erik; Jett, Marti

2008-01-01

Background Effective prophylaxis and treatment for infections caused by biological threat agents (BTA) rely upon early diagnosis and rapid initiation of therapy. Most methods for identifying pathogens in body fluids and tissues require that the pathogen proliferate to detectable and dangerous levels, thereby delaying diagnosis and treatment, especially during the prelatent stages when symptoms for most BTA are indistinguishable flu-like signs. Methods To detect exposures to the various pathogens more rapidly, especially during these early stages, we evaluated a suite of host responses to biological threat agents using global gene expression profiling on complementary DNA arrays. Results We found that certain gene expression patterns were unique to each pathogen and that other gene changes occurred in response to multiple agents, perhaps relating to the eventual course of illness. Nonhuman primates were exposed to some pathogens and the in vitro and in vivo findings were compared. We found major gene expression changes at the earliest times tested post exposure to aerosolized B. anthracis spores and 30 min post exposure to a bacterial toxin. Conclusion Host gene expression patterns have the potential to serve as diagnostic markers or predict the course of impending illness and may lead to new stage-appropriate therapeutic strategies to ameliorate the devastating effects of exposure to biothreat agents. PMID:18667072

A Gravity-Responsive Time-Keeping Protein of the Plant and Animal Cell Surface

NASA Technical Reports Server (NTRS)

Morre, D. James

2003-01-01

The hypothesis under investigation was that a ubiquinol (NADH) oxidase protein of the cell surface with protein disulfide-thiol interchange activity (= NOX protein) is a plant and animal time-keeping ultradian (period of less than 24 h) driver of both cell enlargement and the biological clock that responds to gravity. Despite considerable work in a large number of laboratories spanning several decades, this is, to my knowledge, our work is the first demonstration of a time-keeping biochemical reaction that is both gravity-responsive and growth-related and that has been shown to determine circadian periodicity. As such, the NOX protein may represent both the long-sought biological gravity receptor and the core oscillator of the cellular biological clock. Completed studies have resulted in 12 publications and two issued NASA-owned patents of the clock activity. The gravity response and autoentrainment were characterized in cultured mammalian cells and in two plant systems together with entrainment by light and small molecules (melatonin). The molecular basis of the oscillatory behavior was investigated using spectroscopic methods (Fourier transform infrared and circular dichroism) and high resolution electron microscopy. We have also applied these findings to an understanding of the response to hypergravity. Statistical methods for analysis of time series phenomena were developed (Foster et al., 2003).

Detecting gene subnetworks under selection in biological pathways.

PubMed

Gouy, Alexandre; Daub, Joséphine T; Excoffier, Laurent

2017-09-19

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

An analysis of learning in an online biology course for teachers and teacher candidates: A mixed methods approach

NASA Astrophysics Data System (ADS)

Lebec, Michael Thomas

Due to discipline specific shortages, web-based learning has been proposed as a convenient way to upgrade the content knowledge of instructors interested in learning to teach science. Despite quantitative evidence that web-based instruction is equivalent to traditional methods, questions remain regarding its use. The efficiency and practicality of this approach with teachers in particular has not been extensively studied. This investigation examines learning in an online biology course designed to help teachers prepare for science certification exams. Research questions concern flow teachers learn biology in the online environment and how this setting influences the learning process. Quantitative and qualitative methodologies are employed in an attempt to provide a more complete perspective than typical studies of online learning. Concept maps, tests, and online discussion transcripts are compared as measures of assimilated knowledge, while interviews reflect participants' views on the course. Findings indicate that participants experienced gains in declarative knowledge, but little improvement with respect to conditional knowledge. Qualitative examination of concept maps demonstrates gaps in participants' understandings of key course ideas. Engagement in the use of online resources varied according to participants' attitudes towards online learning. Subjects also reported a lack of motivation to fully engage in the course due to busy teaching schedules and the absence of accountability.

Indirect nontarget effects of host-specific biological control agents: Implications for biological control

Treesearch

Dean E. Pearson; Ragan M. Callaway

2005-01-01

Classical biological control of weeds currently operates under the assumption that biological control agents are safe (i.e., low risk) if they do not directly attack nontarget species. However, recent studies indicate that even highly host-specific biological control agents can impact nontarget species through indirect effects. This finding has profound...

Do Sophisticated Epistemic Beliefs Predict Meaningful Learning? Findings from a Structural Equation Model of Undergraduate Biology Learning

ERIC Educational Resources Information Center

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-01-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely "multiple-source," "uncertainty," "development," and "justification." COLB is further…

Disentangling multidimensional spatio-temporal data into their common and aberrant responses

DOE PAGES

Chang, Young Hwan; Korkola, James; Amin, Dhara N.; ...

2015-04-22

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures andmore » how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.« less

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

PubMed Central

2010-01-01

Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks. PMID:21070623

The Transcriptome of Lutzomyia longipalpis (Diptera: Psychodidae) Male Reproductive Organs

PubMed Central

Bretãs, Jorge A. C.; Mazzoni, Camila J.; Souza, Nataly A.; Albano, Rodolpho M.; Wagner, Glauber; Davila, Alberto M. R.; Peixoto, Alexandre A.

2012-01-01

Background It has been suggested that genes involved in the reproductive biology of insect disease vectors are potential targets for future alternative methods of control. Little is known about the molecular biology of reproduction in phlebotomine sand flies and there is no information available concerning genes that are expressed in male reproductive organs of Lutzomyia longipalpis, the main vector of American visceral leishmaniasis and a species complex. Methods/Principal Findings We generated 2678 high quality ESTs (“Expressed Sequence Tags”) of L. longipalpis male reproductive organs that were grouped in 1391 non-redundant sequences (1136 singlets and 255 clusters). BLAST analysis revealed that only 57% of these sequences share similarity with a L. longipalpis female EST database. Although no more than 36% of the non-redundant sequences showed similarity to protein sequences deposited in databases, more than half of them presented the best-match hits with mosquito genes. Gene ontology analysis identified subsets of genes involved in biological processes such as protein biosynthesis and DNA replication, which are probably associated with spermatogenesis. A number of non-redundant sequences were also identified as putative male reproductive gland proteins (mRGPs), also known as male accessory gland protein genes (Acps). Conclusions The transcriptome analysis of L. longipalpis male reproductive organs is one step further in the study of the molecular basis of the reproductive biology of this important species complex. It has allowed the identification of genes potentially involved in spermatogenesis as well as putative mRGPs sequences, which have been studied in many insect species because of their effects on female post-mating behavior and physiology and their potential role in sexual selection and speciation. These data open a number of new avenues for further research in the molecular and evolutionary reproductive biology of sand flies. PMID:22496818

In situ Biological Dose Mapping Estimates the Radiation Burden Delivered to ‘Spared’ Tissue between Synchrotron X-Ray Microbeam Radiotherapy Tracks

PubMed Central

Rothkamm, Kai; Crosbie, Jeffrey C.; Daley, Frances; Bourne, Sarah; Barber, Paul R.; Vojnovic, Borivoj; Cann, Leonie; Rogers, Peter A. W.

2012-01-01

Microbeam radiation therapy (MRT) using high doses of synchrotron X-rays can destroy tumours in animal models whilst causing little damage to normal tissues. Determining the spatial distribution of radiation doses delivered during MRT at a microscopic scale is a major challenge. Film and semiconductor dosimetry as well as Monte Carlo methods struggle to provide accurate estimates of dose profiles and peak-to-valley dose ratios at the position of the targeted and traversed tissues whose biological responses determine treatment outcome. The purpose of this study was to utilise γ-H2AX immunostaining as a biodosimetric tool that enables in situ biological dose mapping within an irradiated tissue to provide direct biological evidence for the scale of the radiation burden to ‘spared’ tissue regions between MRT tracks. Γ-H2AX analysis allowed microbeams to be traced and DNA damage foci to be quantified in valleys between beams following MRT treatment of fibroblast cultures and murine skin where foci yields per unit dose were approximately five-fold lower than in fibroblast cultures. Foci levels in cells located in valleys were compared with calibration curves using known broadbeam synchrotron X-ray doses to generate spatial dose profiles and calculate peak-to-valley dose ratios of 30–40 for cell cultures and approximately 60 for murine skin, consistent with the range obtained with conventional dosimetry methods. This biological dose mapping approach could find several applications both in optimising MRT or other radiotherapeutic treatments and in estimating localised doses following accidental radiation exposure using skin punch biopsies. PMID:22238667

Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue.

PubMed

Kerkentzes, Konstantinos; Lagani, Vincenzo; Tsamardinos, Ioannis; Vyberg, Mogens; Røe, Oluf Dimitri

2014-01-01

Novel statistical methods and increasingly more accurate gene annotations can transform "old" biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches. The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways. Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93-100% (AUC = 0.93-1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue. Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.

A Facile and Efficient Synthesis of Diaryl Amines or Ethers under Microwave Irradiation at Presence of KF/Al2O3 without Solvent and Their Anti-Fungal Biological Activities against Six Phytopathogens

PubMed Central

Huang, Liang-Zhu; Han, Pan; Li, You-Qiang; Xu, Ying-Meng; Zhang, Tao; Du, Zhen-Ting

2013-01-01

A series of diaryl amines, ethers and thioethers were synthesized under microwave irradiation efficiently at presence of KF/Al2O3 in 83%–96% yields without any solvent. The salient characters of this method lie in short reaction time, high yields, general applicability to substrates and simple workup procedure. At the same time, their antifungal biological activities against six phytopathogen were evaluated. Most of the compounds (3b, 3c, 3g–o) are more potent than thiophannate-methyl against to Magnaporthe oryzae. This implies that diaryl amine or ether moiety may be helpful in finding a fungicide against Magnaporthe oryzae. PMID:24036444

Disease gene classification with metagraph representations.

PubMed

Kircali Ata, Sezin; Fang, Yuan; Wu, Min; Li, Xiao-Li; Xiao, Xiaokui

2017-12-01

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics on the PPIK network, we further propose to represent proteins with metagraphs. Different from a traditional network motif or subgraph, a metagraph can capture a particular topological arrangement involving the interactions/associations between both proteins and keywords. Based on the novel metagraph representations for proteins, we further build classifiers for disease protein classification through supervised learning. Our experiments on three different PPI databases demonstrate that the proposed method consistently improves disease protein prediction across various classifiers, by 15.3% in AUC on average. It outperforms the baselines including the diffusion-based methods (e.g., RWR) and the module-based methods by 13.8-32.9% for overall disease protein prediction. For predicting breast cancer genes, it outperforms RWR, PRINCE and the module-based baselines by 6.6-14.2%. Finally, our predictions also turn out to have better correlations with literature findings from PubMed. Copyright © 2017 Elsevier Inc. All rights reserved.

Distinguishing Error from Chaos in Ecological Time Series

NASA Astrophysics Data System (ADS)

Sugihara, George; Grenfell, Bryan; May, Robert M.

1990-11-01

Over the years, there has been much discussion about the relative importance of environmental and biological factors in regulating natural populations. Often it is thought that environmental factors are associated with stochastic fluctuations in population density, and biological ones with deterministic regulation. We revisit these ideas in the light of recent work on chaos and nonlinear systems. We show that completely deterministic regulatory factors can lead to apparently random fluctuations in population density, and we then develop a new method (that can be applied to limited data sets) to make practical distinctions between apparently noisy dynamics produced by low-dimensional chaos and population variation that in fact derives from random (high-dimensional)noise, such as environmental stochasticity or sampling error. To show its practical use, the method is first applied to models where the dynamics are known. We then apply the method to several sets of real data, including newly analysed data on the incidence of measles in the United Kingdom. Here the additional problems of secular trends and spatial effects are explored. In particular, we find that on a city-by-city scale measles exhibits low-dimensional chaos (as has previously been found for measles in New York City), whereas on a larger, country-wide scale the dynamics appear as a noisy two-year cycle. In addition to shedding light on the basic dynamics of some nonlinear biological systems, this work dramatizes how the scale on which data is collected and analysed can affect the conclusions drawn.

Establishment of an AAV Reverse Infection-Based Array

PubMed Central

Wang, Gang; Dong, Zheyue; Shen, Wei; Zheng, Gang; Wu, Xiaobing; Xue, Jinglun; Wang, Yue; Chen, Jinzhong

2010-01-01

Background The development of a convenient high-throughput gene transduction approach is critical for biological screening. Adeno-associated virus (AAV) vectors are broadly used in gene therapy studies, yet their applications in in vitro high-throughput gene transduction are limited. Principal Findings We established an AAV reverse infection (RI)-based method in which cells were transduced by quantified recombinant AAVs (rAAVs) pre-coated onto 96-well plates. The number of pre-coated rAAV particles and number of cells loaded per well, as well as the temperature stability of the rAAVs on the plates, were evaluated. As the first application of this method, six serotypes or hybrid serotypes of rAAVs (AAV1, AAV2, AAV5/5, AAV8, AAV25 m, AAV28 m) were compared for their transduction efficiencies using various cell lines, including BHK21, HEK293, BEAS-2BS, HeLaS3, Huh7, Hepa1-6, and A549. AAV2 and AAV1 displayed high transduction efficiency; thus, they were deemed to be suitable candidate vectors for the RI-based array. We next evaluated the impact of sodium butyrate (NaB) treatment on rAAV vector-mediated reporter gene expression and found it was significantly enhanced, suggesting that our system reflected the biological response of target cells to specific treatments. Conclusions/Significance Our study provides a novel method for establishing a highly efficient gene transduction array that may be developed into a platform for cell biological assays. PMID:20976058

Community structure in networks

NASA Astrophysics Data System (ADS)

Newman, Mark

2004-03-01

Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease.

PubMed

Torres, Matthew P; Dewhurst, Henry; Sundararaman, Niveda

2016-11-01

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable body of potential regulatory elements that impact hundreds of different biological processes important in eukaryotic biology and human health. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Unifying Quantum Physics with Biology

NASA Astrophysics Data System (ADS)

Goradia, Shantilal

2014-09-01

We find that the natural logarithm of the age of the universe in quantum mechanical units is close to 137. Since science is not religion, it is our moral duty to recognize the importance of this finding on the following ground. The experimentally obtained number 137 is a mystical number in science, as if written by the hand of God. It is found in cosmology; unlike other theories, it works in biology too. A formula by Boltzmann also works in both: biology and physics, as if it is in the heart of God. His formula simply leads to finding the logarithm of microstates. One of the two conflicting theories of physics (1) Einstein's theory of General Relativity and (2) Quantum Physics, the first applies only in cosmology, but the second applies in biology too. Since we have to convert the age of the universe, 13 billion years, into 1,300,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 Planck times to get close to 137, quantum physics clearly shows the characteristics of unifying with biology. The proof of its validity also lies in its ability to extend information system observed in biology.

OBIS-USA and Ocean Acidification: Chemical and Biological Observation Data, Integrated for Discovery and Applications

NASA Astrophysics Data System (ADS)

Fornwall, M.; Jewett, L.; Yates, K.; Goldstein, P.

2012-12-01

OBIS-USA (usgs.gov/obis-usa), a program of USGS Core Science, Analytics and Synthesis, is the US Regional node of the International Ocean Biogeographic Information System (iobis.org). OBIS data records observations of biological occurrences - identifiable species - at known time and coordinates. Within US research and operational communities, OBIS-USA serves an expanding range of applications by capturing details to accompany each observation: information to understand record quality and suitability for applications, details about observation circumstances such as sampling method and sampling conditions, and biological details such as sex, life stage, behavior and other characteristics. The NOAA Ocean Acidification Program and its associated data management effort (led by National Oceanographic Data Center) aim to enable users to locate, understand and use marine data from multiple sources and of multiple types to address questions related to ocean acidification and it impacts on marine ecosystems. By the nature of researching ocean acidification, data-driven applications require users to find and apply datasets that represent different disciplines as well as different researchers, organizations, agencies, funding models, data management practices and formats, and survey and observation methods. We refer to any collection(s) of data having diverse characteristics on these and other dimensions as "heterogeneous data". However, data management and Internet technologies enable the data itself and many of its diverse characteristics to be discoverable and understandable enough for users to build effective models, applications, and solutions. While it may not be simple to make heterogeneous data uniform or "seamless", current technologies enable at least the data characteristics to be sufficiently well-understood that users can consume data and accommodate its diverse characteristics in their process of generating outputs. Via this abstract and accompanying poster presentation, OBIS-USA and the NOAA Ocean Acidification Program describe proposed methods for obtaining diverse data, such as both chemical observations (those necessary to derive calcium carbonate saturation state) and biological marine observations (species occurrence, abundance), in order to use these sources of information in combined analysis for current and future research on ocean acidification and its relation to observed biology. Current OBIS-USA biological observations represent in-situ observations of marine taxa, and in the context of Ocean Acidification and this poster presentation, OBIS-USA shows a path toward including experimental biology observations as well as in-situ.

Cell biology: at the center of modern biomedicine.

PubMed

Budde, Priya Prakash; Williams, Elizabeth H; Misteli, Tom

2012-10-01

How does basic cell biology contribute to biomedicine? A new series of Features in JCB provides a cross section of compelling examples of how basic cell biology findings can lead to therapeutics. These articles highlight the fruitful, essential, and increasingly prominent bridge that exists between cell biology and the clinic.

The neurophysiology of biological motion perception in schizophrenia

PubMed Central

Jahshan, Carol; Wynn, Jonathan K; Mathis, Kristopher I; Green, Michael F

2015-01-01

Introduction The ability to recognize human biological motion is a fundamental aspect of social cognition that is impaired in people with schizophrenia. However, little is known about the neural substrates of impaired biological motion perception in schizophrenia. In the current study, we assessed event-related potentials (ERPs) to human and nonhuman movement in schizophrenia. Methods Twenty-four subjects with schizophrenia and 18 healthy controls completed a biological motion task while their electroencephalography (EEG) was simultaneously recorded. Subjects watched clips of point-light animations containing 100%, 85%, or 70% biological motion, and were asked to decide whether the clip resembled human or nonhuman movement. Three ERPs were examined: P1, N1, and the late positive potential (LPP). Results Behaviorally, schizophrenia subjects identified significantly fewer stimuli as human movement compared to healthy controls in the 100% and 85% conditions. At the neural level, P1 was reduced in the schizophrenia group but did not differ among conditions in either group. There were no group differences in N1 but both groups had the largest N1 in the 70% condition. There was a condition × group interaction for the LPP: Healthy controls had a larger LPP to 100% versus 85% and 70% biological motion; there was no difference among conditions in schizophrenia subjects. Conclusions Consistent with previous findings, schizophrenia subjects were impaired in their ability to recognize biological motion. The EEG results showed that biological motion did not influence the earliest stage of visual processing (P1). Although schizophrenia subjects showed the same pattern of N1 results relative to healthy controls, they were impaired at a later stage (LPP), reflecting a dysfunction in the identification of human form in biological versus nonbiological motion stimuli. PMID:25722951

Mars Sample Handling Protocol Workshop Series

NASA Technical Reports Server (NTRS)

Race, Margaret S. (Editor); Nealson, Kenneth H.; Rummel, John D. (Editor); Acevedo, Sara E. (Editor); Devincenzi, Donald L. (Technical Monitor)

2001-01-01

This report provides a record of the proceedings and recommendations of Workshop 3 of the Series, which was held in San Diego, California, March 19-21, 2001. Materials such as the Workshop agenda and participant lists as well as complete citations of all references and a glossary of terms and acronyms appear in the Appendices. Workshop 3 builds on the deliberations and findings of the earlier workshops in the Series, which have been reported separately. During Workshop 3, five individual sub-groups were formed to discuss the following topics: (1) Unifying Properties of Life, (2) Morphological organization and chemical properties, (3) Geochemical and geophysical properties, (4) Chemical Method and (5) Cell Biology Methods.

Method for selective immobilization of macromolecules on self assembled monolayer surfaces

DOEpatents

Laskin, Julia [Richland, WA; Wang, Peng [Billerica, MA

2011-11-29

Disclosed is a method for selective chemical binding and immobilization of macromolecules on solid supports in conjunction with self-assembled monolayer (SAM) surfaces. Immobilization involves selective binding of peptides and other macromolecules to SAM surfaces using reactive landing (RL) of mass-selected, gas phase ions. SAM surfaces provide a simple and convenient platform for tailoring chemical properties of a variety of substrates. The invention finds applications in biochemistry ranging from characterization of molecular recognition events at the amino acid level and identification of biologically active motifs in proteins, to development of novel biosensors and substrates for stimulated protein and cell adhesion.

Epidemiological designs for vaccine safety assessment: methods and pitfalls.

PubMed

Andrews, Nick

2012-09-01

Three commonly used designs for vaccine safety assessment post licensure are cohort, case-control and self-controlled case series. These methods are often used with routine health databases and immunisation registries. This paper considers the issues that may arise when designing an epidemiological study, such as understanding the vaccine safety question, case definition and finding, limitations of data sources, uncontrolled confounding, and pitfalls that apply to the individual designs. The example of MMR and autism, where all three designs have been used, is presented to help consider these issues. Copyright © 2011 The International Alliance for Biological Standardization. Published by Elsevier Ltd. All rights reserved.

Investigation of production method, geographical origin and species authentication in commercially relevant shrimps using stable isotope ratio and/or multi-element analyses combined with chemometrics: an exploratory analysis.

PubMed

Ortea, Ignacio; Gallardo, José M

2015-03-01

Three factors defining the traceability of a food product are production method (wild or farmed), geographical origin and biological species, which have to be checked and guaranteed, not only in order to avoid mislabelling and commercial fraud, but also to address food safety issues and to comply with legal regulations. The aim of this study was to determine whether these three factors could be differentiated in shrimps using stable isotope ratio analysis of carbon and nitrogen and/or multi-element composition. Different multivariate statistics methods were applied to different data subsets in order to evaluate their performance in terms of classification or predictive ability. Although the success rates varied depending on the dataset used, the combination of both techniques allowed the correct classification of 100% of the samples according to their actual origin and method of production, and 93.5% according to biological species. Even though further studies including a larger number of samples in each group are needed in order to validate these findings, we can conclude that these methodologies should be considered for studies regarding seafood product authenticity. Copyright © 2014 Elsevier Ltd. All rights reserved.

Correlative Imaging of Fluorescent Proteins in Resin-Embedded Plant Material1

PubMed Central

Bell, Karen; Mitchell, Steve; Paultre, Danae; Posch, Markus; Oparka, Karl

2013-01-01

Fluorescent proteins (FPs) were developed for live-cell imaging and have revolutionized cell biology. However, not all plant tissues are accessible to live imaging using confocal microscopy, necessitating alternative approaches for protein localization. An example is the phloem, a tissue embedded deep within plant organs and sensitive to damage. To facilitate accurate localization of FPs within recalcitrant tissues, we developed a simple method for retaining FPs after resin embedding. This method is based on low-temperature fixation and dehydration, followed by embedding in London Resin White, and avoids the need for cryosections. We show that a palette of FPs can be localized in plant tissues while retaining good structural cell preservation, and that the polymerized block face can be counterstained with cell wall probes. Using this method we have been able to image green fluorescent protein-labeled plasmodesmata to a depth of more than 40 μm beneath the resin surface. Using correlative light and electron microscopy of the phloem, we were able to locate the same FP-labeled sieve elements in semithin and ultrathin sections. Sections were amenable to antibody labeling, and allowed a combination of confocal and superresolution imaging (three-dimensional-structured illumination microscopy) on the same cells. These correlative imaging methods should find several uses in plant cell biology. PMID:23457228

Linking Biological and Cognitive Aging: Toward Improving Characterizations of Developmental Time

PubMed Central

DeCarlo, Correne A.; Dixon, Roger A.

2011-01-01

Objectives. Chronological age is the most frequently employed predictor in life-span developmental research, despite repeated assertions that it is best conceived as a proxy for true mechanistic changes that influence cognition across time. The present investigation explores the potential that selected functional biomarkers may contribute to the more effective conceptual and operational definitions of developmental time. Methods. We used data from the Victoria Longitudinal Study to explore both static and dynamic biological or physiological markers that arguably influence process-specific mechanisms underlying cognitive changes in late life. Multilevel models were fit to test the dynamic coupling between change in theoretically relevant biomarkers (e.g., grip strength, pulmonary function) and change in select cognitive measures (e.g., executive function, episodic and semantic memory). Results. Results showed that, independent of the passage of developmental time (indexed as years in study), significant time-varying covariation was observed linking corresponding declines for select cognitive outcomes and biological markers. Discussion. Our findings support the interpretation that cognitive decline is not due to chronological aging per se but rather reflects multiple causal factors from a broad range of biological and physical health domains that operate along the age continuum. PMID:21743053

Magnetoencephalography Study of Right Parietal Lobe Dysfunction of the Evoked Mirror Neuron System in Antipsychotic-Free Schizophrenia

PubMed Central

Kato, Yutaka; Muramatsu, Taro; Kato, Motoichiro; Shibukawa, Yoshiyuki; Shintani, Masuro; Mimura, Masaru

2011-01-01

Introduction Patients with schizophrenia commonly exhibit deficits of non-verbal communication in social contexts, which may be related to cognitive dysfunction that impairs recognition of biological motion. Although perception of biological motion is known to be mediated by the mirror neuron system, there have been few empirical studies of this system in patients with schizophrenia. Methods Using magnetoencephalography, we examined whether antipsychotic-free schizophrenia patients displayed mirror neuron system dysfunction during observation of biological motion (jaw movement of another individual). Results Compared with normal controls, the patients with schizophrenia had fewer components of both the waveform and equivalent current dipole, suggesting aberrant brain activity resulting from dysfunction of the right inferior parietal cortex. They also lacked the changes of alpha band and gamma band oscillation seen in normal controls, and had weaker phase-locking factors and gamma-synchronization predominantly in right parietal cortex. Conclusions Our findings demonstrate that untreated patients with schizophrenia exhibit aberrant mirror neuron system function based on the right inferior parietal cortex, which is characterized by dysfunction of gamma-synchronization in the right parietal lobe during observation of biological motion. PMID:22132217

SuperDCA for genome-wide epistasis analysis.

PubMed

Puranen, Santeri; Pesonen, Maiju; Pensar, Johan; Xu, Ying Ying; Lees, John A; Bentley, Stephen D; Croucher, Nicholas J; Corander, Jukka

2018-05-29

The potential for genome-wide modelling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has previously been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10 4 -10 5 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here, we introduce a novel inference method (SuperDCA) that employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 10 5 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA, thus, holds considerable potential in building understanding about numerous organisms at a systems biological level.

Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels

NASA Astrophysics Data System (ADS)

Moghaddasi, Hanieh; Khalifeh, Khosrow; Darooneh, Amir Hossein

2017-01-01

Functional DNA sub-sequences and genome elements are spatially clustered through the genome just as keywords in literary texts. Therefore, some of the methods for ranking words in texts can also be used to compare different DNA sub-sequences. In analogy with the literary texts, here we claim that the distribution of distances between the successive sub-sequences (words) is q-exponential which is the distribution function in non-extensive statistical mechanics. Thus the q-parameter can be used as a measure of words clustering levels. Here, we analyzed the distribution of distances between consecutive occurrences of 16 possible dinucleotides in human chromosomes to obtain their corresponding q-parameters. We found that CG as a biologically important two-letter word concerning its methylation, has the highest clustering level. This finding shows the predicting ability of the method in biology. We also proposed that chromosome 18 with the largest value of q-parameter for promoters of genes is more sensitive to dietary and lifestyle. We extended our study to compare the genome of some selected organisms and concluded that the clustering level of CGs increases in higher evolutionary organisms compared to lower ones.

Teaching at the edge of knowledge: Non-equilibrium statistical physics

NASA Astrophysics Data System (ADS)

Schmittmann, Beate

2007-03-01

As physicists become increasingly interested in biological problems, we frequently find ourselves confronted with complex open systems, involving many interacting constituents and characterized by non-vanishing fluxes of mass or energy. Faced with the task of predicting macroscopic behaviors from microscopic information for these non-equilibrium systems, the familiar Gibbs-Boltzmann framework fails. The development of a comprehensive theoretical characterization of non-equilibrium behavior is one of the key challenges of modern condensed matter physics. In its absence, several approaches have been developed, from master equations to thermostatted molecular dynamics, which provide key insights into the rich and often surprising phenomenology of systems far from equilibrium. In my talk, I will address some of these methods, selecting those that are most relevant for a broad range of interdisciplinary problems from biology to traffic, finance, and sociology. The ``portability'' of these methods makes them valuable for graduate students from a variety of disciplines. To illustrate how different methods can complement each other when probing a problem from, e.g., the life sciences, I will discuss some recent attempts at modeling translation, i.e., the process by which the genetic information encoded on an mRNA is translated into the corresponding protein.

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PubMed Central

Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

2010-01-01

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

Superstatistics model for T₂ distribution in NMR experiments on porous media.

PubMed

Correia, M D; Souza, A M; Sinnecker, J P; Sarthour, R S; Santos, B C C; Trevizan, W; Oliveira, I S

2014-07-01

We propose analytical functions for T2 distribution to describe transverse relaxation in high- and low-fields NMR experiments on porous media. The method is based on a superstatistics theory, and allows to find the mean and standard deviation of T2, directly from measurements. It is an alternative to multiexponential models for data decay inversion in NMR experiments. We exemplify the method with q-exponential functions and χ(2)-distributions to describe, respectively, data decay and T2 distribution on high-field experiments of fully water saturated glass microspheres bed packs, sedimentary rocks from outcrop and noisy low-field experiment on rocks. The method is general and can also be applied to biological systems. Copyright © 2014 Elsevier Inc. All rights reserved.

Biological tracer method

DOEpatents

Strong-Gunderson, Janet M.; Palumbo, Anthony V.

1998-01-01

The present invention is a biological tracer method for characterizing the movement of a material through a medium, comprising the steps of: introducing a biological tracer comprising a microorganism having ice nucleating activity into a medium; collecting at least one sample of the medium from a point removed from the introduction point; and analyzing the sample for the presence of the biological tracer. The present invention is also a method for using a biological tracer as a label for material identification by introducing a biological tracer having ice nucleating activity into a material, collecting a sample of a portion of the labelled material and analyzing the sample for the presence of the biological tracer.

Biological tracer method

DOEpatents

Strong-Gunderson, J.M.; Palumbo, A.V.

1998-09-15

The present invention is a biological tracer method for characterizing the movement of a material through a medium, comprising the steps of: introducing a biological tracer comprising a microorganism having ice nucleating activity into a medium; collecting at least one sample of the medium from a point removed from the introduction point; and analyzing the sample for the presence of the biological tracer. The present invention is also a method for using a biological tracer as a label for material identification by introducing a biological tracer having ice nucleating activity into a material, collecting a sample of a portion of the labelled material and analyzing the sample for the presence of the biological tracer. 2 figs.

Neurobiological Correlates in Forensic Assessment: A Systematic Review

PubMed Central

van der Gronde, Toon; Kempes, Maaike; van El, Carla; Rinne, Thomas; Pieters, Toine

2014-01-01

Background With the increased knowledge of biological risk factors, interest in including this information in forensic assessments is growing. Currently, forensic assessments are predominantly focused on psychosocial factors. A better understanding of the neurobiology of violent criminal behaviour and biological risk factors could improve forensic assessments. Objective To provide an overview of the current evidence about biological risk factors that predispose people to antisocial and violent behaviour, and determine its usefulness in forensic assessment. Methods A systematic literature search was conducted using articles from PsycINFO, Embase and Pubmed published between 2000 and 2013. Results This review shows that much research on the relationship between genetic predisposition and neurobiological alterations with aggression is performed on psychiatric patients or normal populations. However, the number of studies comparing offenders is limited. There is still a great need to understand how genetic and neurobiological alterations and/or deficits are related to violent behaviour, specifically criminality. Most studies focus on only one of the genetic or neurobiological fields related to antisocial and/or violent behaviour. To reliably correlate the findings of these fields, a standardization of methodology is urgently needed. Conclusion Findings from the current review suggest that violent aggression, like all forms of human behaviour, both develops under specific genetic and environmental conditions, and requires interplay between these conditions. Violence should be considered as the end product of a chain of life events, during which risks accumulate and potentially reinforce each other, displaying or triggering a specific situation. This systematic review did not find evidence of predispositions or neurobiological alterations that solely explain antisocial or violent behaviour. With better designed studies, more correlation between diverse fields, and more standardisation, it might be possible to elucidate underlying mechanisms. Thus, we advocate maintaining the current case-by-case differentiated approach to evidence-based forensic assessment. PMID:25330208

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

Teaching science content in nursing programs in Australia: a cross-sectional survey of academics.

PubMed

Birks, Melanie; Ralph, Nicholas; Cant, Robyn; Hillman, Elspeth; Chun Tie, Ylona

2015-01-01

Professional nursing practice is informed by biological, social and behavioural sciences. In undergraduate pre-registration nursing programs, biological sciences typically include anatomy, physiology, microbiology, chemistry, physics and pharmacology. The current gap in the literature results in a lack of information about the content and depth of biological sciences being taught in nursing curricula. The aim of this study was to establish what priority is given to the teaching of science topics in these programs in order to inform an understanding of the relative importance placed on this subject area in contemporary nursing education. This study employed a cross-sectional survey method. This paper reports on the first phase of a larger project examining science content in nursing programs. An existing questionnaire was modified and delivered online for completion by academics who teach science to nurses in these programs. This paper reports on the relative priority given by respondents to the teaching of 177 topics contained in the questionnaire. Of the relatively small population of academics who teach science to nursing students, thirty (n = 30) completed the survey. Findings indicate strong support for the teaching of science in these programs, with particular priority given to the basic concepts of bioscience and gross system anatomy. Of concern, most science subject areas outside of these domains were ranked as being of moderate or low priority. While the small sample size limited the conclusions able to be drawn from this study, the findings supported previous studies that indicated inadequacies in the teaching of science content in nursing curricula. Nevertheless, these findings have raised questions about the current philosophy that underpins nursing education in Australia and whether existing practices are clearly focused on preparing students for the demands of contemporary nursing practice. Academics responsible for the design and implementation of nursing curricula are encouraged to review the content of current programs in light of the findings of this research.

Anaerobic bioprocessing of low-rank coals. [Veillonella alcalescens and Propionibacterium acidipropionici

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jain, M.K.; Narayan, R.; Han, O.

1992-01-30

The overall goal of this project is to find biological methods to remove carboxylic functionalities from low-rank coals under ambient conditions and to assess the properties of these modified coals towards coal liquefaction. The main objectives of this quarter were: (1) continuation of microbial consortia development, (2) evaluation of the isolated organisms for decarboxylation, (3) selection of best performing culture (known cultures vs. new isolates), and (4) coal decarboxylation using activated carbon as blanks. The project began on September 12, 1990.

Blood lipid measurements. Variations and practical utility.

PubMed

Cooper, G R; Myers, G L; Smith, S J; Schlant, R C

1992-03-25

To describe the magnitude and impact of the major biological and analytical sources of variation in serum lipid and lipoprotein levels on risk of coronary heart disease; to present a way to qualitatively estimate the total intraindividual variation; and to demonstrate how to determine the number of specimens required to estimate, with 95% confidence, the "true" underlying total cholesterol value in the serum of a patient. Representative references on each source of variation were selected from more than 300 reviewed publications, most published within the past 5 years, to document current findings and concepts. Most articles reviewed were in English. Studies on biological sources of variation were selected using the following criteria: representative of published findings, clear statement of either significant or insignificant results, and acquisition of clinical and laboratory data under standardized conditions. Representative results for special populations such as women and children are reported when results differ from those of adult men. References were selected based on acceptable experimental design and use of standardized laboratory lipid measurements. The lipid levels considered representative for a selected source of variation arose from quantitative measurements by a suitably standardized laboratory. Statistical analysis of data was examined to assure reliability. The proposed method of estimating the biological coefficient of variation must be considered to give qualitative results, because only two or three serial specimens are collected in most cases for the estimation. Concern has arisen about the magnitude, impact, and interpretation of preanalytical as well as analytical sources of variation on reported results of lipid measurements of an individual. Preanalytical sources of variation from behavioral, clinical, and sampling sources constitute about 60% of the total variation in a reported lipid measurement of an individual. A technique is presented to allow physicians to qualitatively estimate the intraindividual biological variation of a patient from the results of two or more specimens reported from a standardized laboratory and to determine whether additional specimens are needed to meet the National Cholesterol Education Program recommendation that the intraindividual serum total cholesterol coefficient of variation not exceed 5.0. A National Reference Method Network has been established to help solve analytical problems.

How do the high school biology textbooks introduce the nature of science?

NASA Astrophysics Data System (ADS)

Lee, Young H.

2007-05-01

Although helping students to achieve an adequate understanding of the nature of science has been a consistent goal for science education for over half a century, current research reveals that the majority of students and teachers have naive views of the nature of science (Abd-El-khalick & Akerson, 2004; Bianchini & Colburn, 2000). This problem could be attributed not only to the complex nature of science, but also to the way the nature of science is presented to students during instruction. Thus, research must be conducted to examine how the science is taught, especially in science textbooks, which are a major instructional resource for teaching science. The aim of this study was to conduct a content analysis of the first chapter of four high school biology textbooks, which typically discusses "What is science?" and "What is biology?" This research used a content analysis technique to analyze the four high school biology textbooks, using a conceptual framework that has been used often for science textbook analysis. This conceptual framework consists of four themes of the nature of science: (a) science as a body of knowledge, (b) science as a way of thinking, (c) science as a way of investigating, and (d) the interaction of science, technology, and society. For this study, the four-theme-framework was modified to incorporate descriptors from national-level documents, such as Science for All Americans (AAAS, 1990) Benchmarks for Science Literacy (AAAS, 1993) and the National Science Education Standards (NRC, 1996), as well as science education research reports. A scoring procedure was used that resulted in good to excellent intercoder agreement with Cohen's kappa (k) ranging from .63 to .96. The findings show that the patterns of presentation of the four themes of the nature of science in the four high school biology textbooks are similar across the different locations of data, text, figures, and assessments. On the other hand, the pattern of presentation of the four themes is diverse by the publishing company. Some high school biology textbooks reflect a more reasonably balanced treatment of the four themes of the nature of science than other textbooks. The authors of most high school biology textbooks are attempting to convey an idea of biology and how scientific enterprise works by engaging students in investigations and revealing the thinking process of scientists. In addition to the quantitative analysis, a qualitative analysis was undertaken in an attempt to achieve a more comprehensive understanding of the nature of science in high school biology textbooks. A more experienced textbook analyst and nature of science researcher helped to confirm the conclusions by the investigator, who also examined the data sources from the introductory chapters of the biology textbooks. Examination of the first chapter of high school biology textbooks, using a qualitative analysis, reveals that each textbook describes the nature of science in a considerably different manner. While the approaches and emphases used to present scientific enterprise are remarkably different in each textbook, generally high school biology textbooks present several common topics, such as characteristics of life, scientific methods, biological issues, and tools used in science. Most of the high school biology textbooks present a narrow view of scientific methods as well as science, technology, and society. The qualitative/case study approach provided an insightful perspective of how science is presented to the user. Given the importance of textbooks on teaching and learning of science, it is recommended that teachers be informed of the findings of this study in order for them to understand how the first chapter presents the nature of science, which in turn may influence how they select biology textbooks and supplement this content Further, textbook publishers should also be informed of the results of the research in order to design future textbooks and materials to include a more authentic view of the nature of science.

Integrating cell biology and proteomic approaches in plants.

PubMed

Takáč, Tomáš; Šamajová, Olga; Šamaj, Jozef

2017-10-03

Significant improvements of protein extraction, separation, mass spectrometry and bioinformatics nurtured advancements of proteomics during the past years. The usefulness of proteomics in the investigation of biological problems can be enhanced by integration with other experimental methods from cell biology, genetics, biochemistry, pharmacology, molecular biology and other omics approaches including transcriptomics and metabolomics. This review aims to summarize current trends integrating cell biology and proteomics in plant science. Cell biology approaches are most frequently used in proteomic studies investigating subcellular and developmental proteomes, however, they were also employed in proteomic studies exploring abiotic and biotic stress responses, vesicular transport, cytoskeleton and protein posttranslational modifications. They are used either for detailed cellular or ultrastructural characterization of the object subjected to proteomic study, validation of proteomic results or to expand proteomic data. In this respect, a broad spectrum of methods is employed to support proteomic studies including ultrastructural electron microscopy studies, histochemical staining, immunochemical localization, in vivo imaging of fluorescently tagged proteins and visualization of protein-protein interactions. Thus, cell biological observations on fixed or living cell compartments, cells, tissues and organs are feasible, and in some cases fundamental for the validation and complementation of proteomic data. Validation of proteomic data by independent experimental methods requires development of new complementary approaches. Benefits of cell biology methods and techniques are not sufficiently highlighted in current proteomic studies. This encouraged us to review most popular cell biology methods used in proteomic studies and to evaluate their relevance and potential for proteomic data validation and enrichment of purely proteomic analyses. We also provide examples of representative studies combining proteomic and cell biology methods for various purposes. Integrating cell biology approaches with proteomic ones allow validation and better interpretation of proteomic data. Moreover, cell biology methods remarkably extend the knowledge provided by proteomic studies and might be fundamental for the functional complementation of proteomic data. This review article summarizes current literature linking proteomics with cell biology. Copyright © 2017 Elsevier B.V. All rights reserved.

General and Specific Predictors of Nicotine and Alcohol Dependence in Early Adulthood: Genetic and Environmental Influences

PubMed Central

Samek, Diana R; Keyes, Margaret A; Hicks, Brian M; Bailey, Jennifer; McGue, Matt; Iacono, William G

2014-01-01

Objective: This study builds on previous work delineating a hierarchical model of family environmental risk in relation to a hierarchical model of externalizing disorders (EXTs) by evaluating for gene–environment interplay in these relationships. The associations between parent–child relationship quality (conflict, bonding, and management) and substance-specific adolescent family environments (parental/sibling tobacco/alcohol use) in relation to young adult EXTs (age ∼22 years nicotine, alcohol, and other drug dependence; antisocial and risky sexual behavior) were evaluated. Method: The sample included 533 adopted offspring and 323 biological offspring. Because adopted youth do not share genes with their parents, a significant association between parent–child relationship quality and EXTs would provide evidence against passive gene–environment correlation (rGE). Significant associations between parental tobacco/alcohol use in relation to offspring nicotine/alcohol dependence in the adopted offspring support common environmental influence. Significant associations detected for the biological offspring only suggest common genetic influence. Results: For both adoptive and biological offspring, there was a significant association between parent–child relationship quality and EXTs. Parental tobacco/alcohol use was unrelated to EXTs. Sibling tobacco/alcohol use was related to EXTs, but only for the biological siblings. Parental tobacco use was associated with the residual variance in nicotine dependence in adopted offspring. Conclusions: Findings replicate a long-term influence of adolescent parent–child relationship quality on adult EXTs. Findings extend previous research by providing evidence against passive rGE in this association. The association between parental tobacco use and adult nicotine dependence appears to be environmentally mediated, but caution is warranted as we found this relationship only for adopted youth. PMID:24988261

Creating the Chemistry in Cellular Respiration Concept Inventory (CCRCI)

NASA Astrophysics Data System (ADS)

Forshee, Jay Lance, II

Students at our institution report cellular respiration to be the most difficult concept they encounter in undergraduate biology, but why students find this difficult is unknown. Students may find cellular respiration difficult because there is a large amount of steps, or because there are persistent, long-lasting misconceptions and misunderstandings surrounding their knowledge of chemistry, which affect their performance on cellular respiration assessments. Most studies of cellular respiration focus on student macro understanding of the process related to breathing, and matter and energy. To date, no studies identify which chemistry concepts are most relevant to students' development of an understanding of the process of cellular respiration or have developed an assessment to measure student understanding of them. Following the Delphi method, the researchers conducted expert interviews with faculty members from four-year, masters-, and PhD-granting institutions who teach undergraduate general biology, and are experts in their respective fields of biology. From these interviews, researchers identified twelve chemistry concepts important to understanding cellular respiration and using surveys, these twelve concepts were refined into five (electron transfer, energy transfer, thermodynamics (law/conservation), chemical reactions, and gradients). The researchers then interviewed undergraduate introductory biology students at a large Midwestern university to identify their knowledge and misconceptions of the chemistry concepts that the faculty had identified previously as important. The CCRCI was developed using the five important chemistry concepts underlying cellular respiration. The final version of the CCRCI was administered to n=160 introductory biology students during the spring 2017 semester. Reliability of the CCRCI was evaluated using Cronbach's alpha (=.7) and split-half reliability (=.769), and validity of the instrument was assessed through content validity via expert agreement, response process validity through student think-aloud interviews, and via the Delphi survey methodology. Included is a discussion of item function (difficulty, discrimination, and point-biserial correlation), persistent misconceptions and the interpretation, uses, and future directions of the CCRCI.

[Physical activity patterns of school adolescents: Validity, reliability and percentiles proposal for their evaluation].

PubMed

Cossío Bolaños, Marco; Méndez Cornejo, Jorge; Luarte Rocha, Cristian; Vargas Vitoria, Rodrigo; Canqui Flores, Bernabé; Gomez Campos, Rossana

2017-02-01

Regular physical activity (PA) during childhood and adolescence is important for the prevention of non-communicable diseases and their risk factors. To validate a questionnaire for measuring patterns of PA, verify the reliability, comparing the levels of PA aligned with chronological and biological age, and to develop percentile curves to assess PA levels depending on biological maturation. Descriptive cross-sectional study was performed on a sample non-probabilistic quota of 3,176 Chilean adolescents (1685 males and 1491 females), with a mean age range from 10.0 to 18.9 years. An analysis was performed on, weight, standing and sitting height. The biological age through the years of peak growth rate and chronological age in years was determined. Body Mass Index was calculated and a survey of PA was applied. The LMS method was used to develop percentiles. The values for the confirmatory analysis showed saturations between 0.517 and 0.653. The value of adequacy of Kaiser-Meyer-Olkin (KMO) was 0.879 and with 70.8% of the variance explained. The Cronbach alpha values ranged from 0.81 to 0.86. There were differences between the genders when aligned chronological age. There were no differences when aligned by biological age. Percentiles are proposed to classify the PA of adolescents of both genders according to biological age and sex. The questionnaire used was valid and reliable, plus the PA should be evaluated by biological age. These findings led to the development of percentiles to assess PA according to biological age and gender.

Fostering Students' Conceptual Knowledge in Biology in the Context of German National Education Standards

NASA Astrophysics Data System (ADS)

Förtsch, Christian; Dorfner, Tobias; Baumgartner, Julia; Werner, Sonja; von Kotzebue, Lena; Neuhaus, Birgit J.

2018-04-01

The German National Education Standards (NES) for biology were introduced in 2005. The content part of the NES emphasizes fostering conceptual knowledge. However, there are hardly any indications of what such an instructional implementation could look like. We introduce a theoretical framework of an instructional approach to foster students' conceptual knowledge as demanded in the NES (Fostering Conceptual Knowledge) including instructional practices derived from research on single core ideas, general psychological theories, and biology-specific features of instructional quality. First, we aimed to develop a rating manual, which is based on this theoretical framework. Second, we wanted to describe current German biology instruction according to this approach and to quantitatively analyze its effectiveness. And third, we aimed to provide qualitative examples of this approach to triangulate our findings. In a first step, we developed a theoretically devised rating manual to measure Fostering Conceptual Knowledge in videotaped lessons. Data for quantitative analysis included 81 videotaped biology lessons of 28 biology teachers from different German secondary schools. Six hundred forty students completed a questionnaire on their situational interest after each lesson and an achievement test. Results from multilevel modeling showed significant positive effects of Fostering Conceptual Knowledge on students' achievement and situational interest. For qualitative analysis, we contrasted instruction of four teachers, two with high and two with low student achievement and situational interest using the qualitative method of thematic analysis. Qualitative analysis revealed five main characteristics describing Fostering Conceptual Knowledge. Therefore, implementing Fostering Conceptual Knowledge in biology instruction seems promising. Examples of how to implement Fostering Conceptual Knowledge in instruction are shown and discussed.

Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival.

PubMed

Nicolau, Monica; Levine, Arnold J; Carlsson, Gunnar

2011-04-26

High-throughput biological data, whether generated as sequencing, transcriptional microarrays, proteomic, or other means, continues to require analytic methods that address its high dimensional aspects. Because the computational part of data analysis ultimately identifies shape characteristics in the organization of data sets, the mathematics of shape recognition in high dimensions continues to be a crucial part of data analysis. This article introduces a method that extracts information from high-throughput microarray data and, by using topology, provides greater depth of information than current analytic techniques. The method, termed Progression Analysis of Disease (PAD), first identifies robust aspects of cluster analysis, then goes deeper to find a multitude of biologically meaningful shape characteristics in these data. Additionally, because PAD incorporates a visualization tool, it provides a simple picture or graph that can be used to further explore these data. Although PAD can be applied to a wide range of high-throughput data types, it is used here as an example to analyze breast cancer transcriptional data. This identified a unique subgroup of Estrogen Receptor-positive (ER(+)) breast cancers that express high levels of c-MYB and low levels of innate inflammatory genes. These patients exhibit 100% survival and no metastasis. No supervised step beyond distinction between tumor and healthy patients was used to identify this subtype. The group has a clear and distinct, statistically significant molecular signature, it highlights coherent biology but is invisible to cluster methods, and does not fit into the accepted classification of Luminal A/B, Normal-like subtypes of ER(+) breast cancers. We denote the group as c-MYB(+) breast cancer.

Principal process analysis of biological models.

PubMed

Casagranda, Stefano; Touzeau, Suzanne; Ropers, Delphine; Gouzé, Jean-Luc

2018-06-14

Understanding the dynamical behaviour of biological systems is challenged by their large number of components and interactions. While efforts have been made in this direction to reduce model complexity, they often prove insufficient to grasp which and when model processes play a crucial role. Answering these questions is fundamental to unravel the functioning of living organisms. We design a method for dealing with model complexity, based on the analysis of dynamical models by means of Principal Process Analysis. We apply the method to a well-known model of circadian rhythms in mammals. The knowledge of the system trajectories allows us to decompose the system dynamics into processes that are active or inactive with respect to a certain threshold value. Process activities are graphically represented by Boolean and Dynamical Process Maps. We detect model processes that are always inactive, or inactive on some time interval. Eliminating these processes reduces the complex dynamics of the original model to the much simpler dynamics of the core processes, in a succession of sub-models that are easier to analyse. We quantify by means of global relative errors the extent to which the simplified models reproduce the main features of the original system dynamics and apply global sensitivity analysis to test the influence of model parameters on the errors. The results obtained prove the robustness of the method. The analysis of the sub-model dynamics allows us to identify the source of circadian oscillations. We find that the negative feedback loop involving proteins PER, CRY, CLOCK-BMAL1 is the main oscillator, in agreement with previous modelling and experimental studies. In conclusion, Principal Process Analysis is a simple-to-use method, which constitutes an additional and useful tool for analysing the complex dynamical behaviour of biological systems.

Engineering for the 21st century: synthetic biology.

PubMed

Munnelly, Kevin

2013-05-17

For years, scientists have hoped that biology would find its engineering counterpart--a series of principles that could be used as reliably as chemical engineering is for chemistry. Thanks to major advances in synthetic biology, those hopes may soon be realized.

Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.

PubMed

Hüffner, Falk; Komusiewicz, Christian; Niedermeier, Rolf; Wernicke, Sebastian

2017-01-01

Fixed-parameter algorithms are designed to efficiently find optimal solutions to some computationally hard (NP-hard) problems by identifying and exploiting "small" problem-specific parameters. We survey practical techniques to develop such algorithms. Each technique is introduced and supported by case studies of applications to biological problems, with additional pointers to experimental results.

Using information and communication technology (ICT) to the maximum: learning and teaching biology with limited digital technologies

NASA Astrophysics Data System (ADS)

Van Rooy, Wilhelmina S.

2012-04-01

Background: The ubiquity, availability and exponential growth of digital information and communication technology (ICT) creates unique opportunities for learning and teaching in the senior secondary school biology curriculum. Digital technologies make it possible for emerging disciplinary knowledge and understanding of biological processes previously too small, large, slow or fast to be taught. Indeed, much of bioscience can now be effectively taught via digital technology, since its representational and symbolic forms are in digital formats. Purpose: This paper is part of a larger Australian study dealing with the technologies and modalities of learning biology in secondary schools. Sample: The classroom practices of three experienced biology teachers, working in a range of NSW secondary schools, are compared and contrasted to illustrate how the challenges of limited technologies are confronted to seamlessly integrate what is available into a number of molecular genetics lessons to enhance student learning. Design and method: The data are qualitative and the analysis is based on video classroom observations and semi-structured teacher interviews. Results: Findings indicate that if professional development opportunities are provided where the pedagogy of learning and teaching of both the relevant biology and its digital representations are available, then teachers see the immediate pedagogic benefit to student learning. In particular, teachers use ICT for challenging genetic concepts despite limited computer hardware and software availability. Conclusion: Experienced teachers incorporate ICT, however limited, in order to improve the quality of student learning.

Nursing and the new biology: towards a realist, anti-reductionist approach to nursing knowledge.

PubMed

Nairn, Stuart

2014-10-01

As a system of knowledge, nursing has utilized a range of subjects and reconstituted them to reflect the thinking and practice of health care. Often drawn to a holistic model, nursing finds it difficult to resist the reductionist tendencies in biological and medical thinking. In this paper I will propose a relational approach to knowledge that is able to address this issue. The paper argues that biology is not characterized by one stable theory but is often a contentious topic and employs philosophically diverse models in its scientific research. Biology need not be seen as a reductionist science, but reductionism is nonetheless an important current within biological thinking. These reductionist currents can undermine nursing knowledge in four main ways. Firstly, that the conclusions drawn from reductionism go far beyond their data based on an approach that prioritizes biological explanations and eliminates others. Secondly, that the methods employed by biologists are sometimes weak, and the limitations are insufficiently acknowledged. Thirdly, that the assumptions that drive the research agenda are problematic, and finally that uncritical application of these ideas can be potentially disastrous for nursing practice. These issues are explored through an examination of the problems reductionism poses for the issue of gender, mental health, and altruism. I then propose an approach based on critical realism that adopts an anti-reductionist philosophy that utilizes the conceptual tools of emergence and a relational ontology. © 2014 John Wiley & Sons Ltd.

Beyond Correlation in the Detection of Climate Change Impacts: Testing a Mechanistic Hypothesis for Climatic Influence on Sockeye Salmon (Oncorhynchus nerka) Productivity.

PubMed

Tillotson, Michael D; Quinn, Thomas P

2016-01-01

Detecting the biological impacts of climate change is a current focus of ecological research and has important applications in conservation and resource management. Owing to a lack of suitable control systems, measuring correlations between time series of biological attributes and hypothesized environmental covariates is a common method for detecting such impacts. These correlative approaches are particularly common in studies of exploited fish species because rich biological time-series data are often available. However, the utility of species-environment relationships for identifying or predicting biological responses to climate change has been questioned because strong correlations often deteriorate as new data are collected. Specifically stating and critically evaluating the mechanistic relationship(s) linking an environmental driver to a biological response may help to address this problem. Using nearly 60 years of data on sockeye salmon from the Kvichak River, Alaska we tested a mechanistic hypothesis linking water temperatures experienced during freshwater rearing to population productivity by modeling a series of intermediate, deterministic relationships and evaluating temporal trends in biological and environmental time-series. We found that warming waters during freshwater rearing have profoundly altered patterns of growth and life history in this population complex yet there has been no significant correlation between water temperature and metrics of productivity commonly used in fisheries management. These findings demonstrate that pairing correlative approaches with careful consideration of the mechanistic links between populations and their environments can help to both avoid spurious correlations and identify biologically important, but not statistically significant relationships, and ultimately producing more robust conclusions about the biological impacts of climate change.

Beyond Correlation in the Detection of Climate Change Impacts: Testing a Mechanistic Hypothesis for Climatic Influence on Sockeye Salmon (Oncorhynchus nerka) Productivity

PubMed Central

Tillotson, Michael D.; Quinn, Thomas P.

2016-01-01

Detecting the biological impacts of climate change is a current focus of ecological research and has important applications in conservation and resource management. Owing to a lack of suitable control systems, measuring correlations between time series of biological attributes and hypothesized environmental covariates is a common method for detecting such impacts. These correlative approaches are particularly common in studies of exploited fish species because rich biological time-series data are often available. However, the utility of species-environment relationships for identifying or predicting biological responses to climate change has been questioned because strong correlations often deteriorate as new data are collected. Specifically stating and critically evaluating the mechanistic relationship(s) linking an environmental driver to a biological response may help to address this problem. Using nearly 60 years of data on sockeye salmon from the Kvichak River, Alaska we tested a mechanistic hypothesis linking water temperatures experienced during freshwater rearing to population productivity by modeling a series of intermediate, deterministic relationships and evaluating temporal trends in biological and environmental time-series. We found that warming waters during freshwater rearing have profoundly altered patterns of growth and life history in this population complex yet there has been no significant correlation between water temperature and metrics of productivity commonly used in fisheries management. These findings demonstrate that pairing correlative approaches with careful consideration of the mechanistic links between populations and their environments can help to both avoid spurious correlations and identify biologically important, but not statistically significant relationships, and ultimately producing more robust conclusions about the biological impacts of climate change. PMID:27123845

Characterisation of the Context-Dependence of the Gene Concept in Research Articles. Possible Consequences for Teaching Concepts with Multiple Meanings

NASA Astrophysics Data System (ADS)

Flodin, Veronica S.

2017-03-01

The purpose of this study is to interpret and qualitatively characterise the content in some research articles and evaluate cases of possible difference in meanings of the gene concept used. Using a reformulation of Hirst's criteria of forms of knowledge, articles from five different sub-disciplines in biology (transmission genetic, molecular biology, genomics, developmental biology and population genetics) were characterised according to knowledge project, methods used and conceptual contexts. Depending on knowledge project, the gene may be used as a location of recombination, a target of regulatory proteins, a carrier of regulatory sequences, a cause in organ formation or a basis for a genetic map. Methods used range from catching wild birds and dissecting beetle larvae to growing yeast cells in 94 small wells as well as mapping of recombinants, doing statistical calculations, immunoblotting analysis of protein levels, analysis of gene expression with PCR, immunostaining of embryos and automated constructions of multi-locus linkage maps. The succeeding conceptual contexts focused around concepts as meiosis and chromosome, DNA and regulation, cell fitness and production, development and organ formation, conservation and evolution. These contextual differences lead to certain content leaps in relation to different conceptual schemes. The analysis of the various uses of the gene concept shows how differences in methodologies and questions entail a concept that escapes single definitions and "drift around" in meanings. These findings make it important to ask how science might use concepts as tools of specific inquiries and to discuss possible consequences for biology education.

TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees.

PubMed

Mai, Uyen; Mirarab, Siavash

2018-05-08

Sequence data used in reconstructing phylogenetic trees may include various sources of error. Typically errors are detected at the sequence level, but when missed, the erroneous sequences often appear as unexpectedly long branches in the inferred phylogeny. We propose an automatic method to detect such errors. We build a phylogeny including all the data then detect sequences that artificially inflate the tree diameter. We formulate an optimization problem, called the k-shrink problem, that seeks to find k leaves that could be removed to maximally reduce the tree diameter. We present an algorithm to find the exact solution for this problem in polynomial time. We then use several statistical tests to find outlier species that have an unexpectedly high impact on the tree diameter. These tests can use a single tree or a set of related gene trees and can also adjust to species-specific patterns of branch length. The resulting method is called TreeShrink. We test our method on six phylogenomic biological datasets and an HIV dataset and show that the method successfully detects and removes long branches. TreeShrink removes sequences more conservatively than rogue taxon removal and often reduces gene tree discordance more than rogue taxon removal once the amount of filtering is controlled. TreeShrink is an effective method for detecting sequences that lead to unrealistically long branch lengths in phylogenetic trees. The tool is publicly available at https://github.com/uym2/TreeShrink .

Preferences for Explanation Generality Develop Early in Biology But Not Physics.

PubMed

Johnston, Angie M; Sheskin, Mark; Johnson, Samuel G B; Keil, Frank C

2017-04-11

One of the core functions of explanation is to support prediction and generalization. However, some explanations license a broader range of predictions than others. For instance, an explanation about biology could be presented as applying to a specific case (e.g., "this bear") or more generally across "all animals." The current study investigated how 5- to 7-year-olds (N = 36), 11- to 13-year-olds (N = 34), and adults (N = 79) evaluate explanations at varying levels of generality in biology and physics. Findings revealed that even the youngest children preferred general explanations in biology. However, only older children and adults preferred explanation generality in physics. Findings are discussed in light of differences in our intuitions about biological and physical principles. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Bringing RNA into View: RNA and Its Roles in Biology.

ERIC Educational Resources Information Center

Atkins, John F.; Ellington, Andrew; Friedman, B. Ellen; Gesteland, Raymond F.; Noller, Harry F.; Pasquale, Stephen M.; Storey, Richard D.; Uhlenbeck, Olke C.; Weiner, Alan M.

This guide presents a module for college students on ribonucleic acid (RNA) and its role in biology. The module aims to integrate the latest research and its findings into college-level biology and provide an opportunity for students to understand biological processes. Four activities are presented: (1) "RNA Structure: Tapes to Shapes"; (2) "RNA…

Apparatus and Methods for Manipulation and Optimization of Biological Systems

NASA Technical Reports Server (NTRS)

Sun, Ren (Inventor); Ho, Chih-Ming (Inventor); Wong, Pak Kin (Inventor); Yu, Fuqu (Inventor)

2014-01-01

The invention provides systems and methods for manipulating biological systems, for example to elicit a more desired biological response from a biological sample, such as a tissue, organ, and/or a cell. In one aspect, the invention operates by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The invention can be used, e.g., to optimize any biological system, e.g., bioreactors for proteins, and the like, small molecules, polysaccharides, lipids, and the like. Another use of the apparatus and methods includes is for the discovery of key parameters in complex biological systems.

Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

PubMed Central

Chen, Bor-Sen; Wu, Chia-Chou

2013-01-01

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. PMID:24709875

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

PubMed

Chen, Bor-Sen; Wu, Chia-Chou

2013-10-11

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Designing deoxidation inhibiting encapsulation of metal oxide nanostructures for fluidic and biological applications

NASA Astrophysics Data System (ADS)

Ghosh, Moumita; Ghosh, Siddharth; Seibt, Michael; Schaap, Iwan A. T.; Schmidt, Christoph F.; Mohan Rao, G.

2016-12-01

Due to their photoluminescence, metal oxide nanostructures such as ZnO nanostructures are promising candidates in biomedical imaging, drug delivery and bio-sensing. To apply them as label for bio-imaging, it is important to study their structural stability in a bio-fluidic environment. We have explored the effect of water, the main constituent of biological solutions, on ZnO nanostructures with scanning electron microscopy (SEM) and photoluminescence (PL) studies which show ZnO nanorod degeneration in water. In addition, we propose and investigate a robust and inexpensive method to encapsulate these nanostructures (without structural degradation) using bio-compatible non-ionic surfactant in non-aqueous medium, which was not reported earlier. This new finding is an immediate interest to the broad audience of researchers working in biophysics, sensing and actuation, drug delivery, food and cosmetics technology, etc.

Strategies for lowering attrition rates and raising NCLEX-RN pass rates.

PubMed

Higgins, Bonnie

2005-12-01

This study was designed to determine strategies to raise the NCLEX-RN pass rate and lower the attrition rate in a community college nursing program. Ex-post facto data were collected from 213 former nursing student records. Qualitative data were collected from 10 full-time faculty, 30 new graduates, and 45 directors of associate degree nursing programs in Texas. The findings linked the academic variables of two biology courses and three components of the preadmission test to completion of the nursing program. A relationship was found between one biology course, the science component of the preadmission test, the HESI Exit Examination score, and the nursing skills course to passing the NCLEX-RN. Qualitative data indicated preadmission requirements, campus counselors, remediation, faculty, test-item writing, and teaching method were instrumental in completion of the program and passing the NCLEX-RN.

Darwin in Mind: New Opportunities for Evolutionary Psychology

PubMed Central

Bolhuis, Johan J.; Brown, Gillian R.; Richardson, Robert C.; Laland, Kevin N.

2011-01-01

Evolutionary Psychology (EP) views the human mind as organized into many modules, each underpinned by psychological adaptations designed to solve problems faced by our Pleistocene ancestors. We argue that the key tenets of the established EP paradigm require modification in the light of recent findings from a number of disciplines, including human genetics, evolutionary biology, cognitive neuroscience, developmental psychology, and paleoecology. For instance, many human genes have been subject to recent selective sweeps; humans play an active, constructive role in co-directing their own development and evolution; and experimental evidence often favours a general process, rather than a modular account, of cognition. A redefined EP could use the theoretical insights of modern evolutionary biology as a rich source of hypotheses concerning the human mind, and could exploit novel methods from a variety of adjacent research fields. PMID:21811401

Do observed or perceived characteristics of the neighborhood environment mediate associations between neighborhood poverty and cumulative biological risk?

PubMed Central

Schulz, Amy J.; Mentz, Graciela; Lachance, Laurie; Zenk, Shannon N.; Johnson, Jonetta; Stokes, Carmen; Mandell, Rebecca

2013-01-01

Objective To examine contributions of observed and perceived neighborhood characteristics in explaining associations between neighborhood poverty and cumulative biological risk (CBR) in an urban community. Methods Multilevel regression analyses were conducted using cross-sectional data from a probability sample survey (n=919), and observational and census data. Dependent variable: CBR. Independent variables: Neighborhood disorder, deterioration and characteristics; perceived neighborhood social environment, physical environment, and neighborhood environment. Covariates: Neighborhood and individual demographics, health-related behaviors. Results Observed and perceived indicators of neighborhood conditions were significantly associated with CBR, after accounting for both neighborhood and individual level socioeconomic indicators. Observed and perceived neighborhood environmental conditions mediated associations between neighborhood poverty and CBR. Conclusions Findings were consistent with the hypothesis that neighborhood conditions associated with economic divestment mediate associations between neighborhood poverty and CBR. PMID:24100238

A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia.

PubMed

Demirci, Oguz; Clark, Vincent P; Calhoun, Vince D

2008-02-15

Schizophrenia is diagnosed based largely upon behavioral symptoms. Currently, no quantitative, biologically based diagnostic technique has yet been developed to identify patients with schizophrenia. Classification of individuals into patient with schizophrenia and healthy control groups based on quantitative biologically based data is of great interest to support and refine psychiatric diagnoses. We applied a novel projection pursuit technique on various components obtained with independent component analysis (ICA) of 70 subjects' fMRI activation maps obtained during an auditory oddball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90%. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and healthy control groups and may eventually prove useful as a diagnostic tool.

Nijmegen breakage syndrome.

PubMed Central

van der Burgt, I; Chrzanowska, K H; Smeets, D; Weemaes, C

1996-01-01

Nijmegen breakage syndrome (NBS), a rare autosomal recessive condition also known as ataxia telangiectasia (AT) variants V1 and V2, is characterised by microcephaly, typical facies, short stature, immunodeficiency, and chromosomal instability. We report the clinical, immunological, chromosomal, and cell biological findings in 42 patients who are included in the NBS Registry in Nijmegen. The immunological, chromosomal, and cell biological findings resemble those in AT, but the clinical findings are quite different. NBS appears to be a separate entity not allelic with AT. Images PMID:8929954

A random walk in physical biology

NASA Astrophysics Data System (ADS)

Peterson, Eric Lee

Biology as a scientific discipline is becoming evermore quantitative as tools become available to probe living systems on every scale from the macro to the micro and now even to the nanoscale. In quantitative biology the challenge is to understand the living world in an in vivo context, where it is often difficult for simple theoretical models to connect with the full richness and complexity of the observed data. Computational models and simulations offer a way to bridge the gap between simple theoretical models and real biological systems; towards that aspiration are presented in this thesis three case studies in applying computational models that may give insight into native biological structures.The first is concerned with soluble proteins; proteins, like DNA, are linear polymers written in a twenty-letter "language" of amino acids. Despite the astronomical number of possible proteins sequences, a great amount of similarity is observed among the folded structures of globular proteins. One useful way of discovering similar sequences is to align their sequences, as done e.g. by the popular BLAST program. By clustering together amino acids and reducing the alphabet that proteins are written in to fewer than twenty letters, we find that pairwise sequence alignments are actually more sensitive to proteins with similar structures.The second case study is concerned with the measurement of forces applied to a membrane. We demonstrate a general method for extracting the forces applied to a fluid lipid bilayer of arbitrary shape and show that the subpiconewton forces applied by optical tweezers to vesicles can be accurately measured in this way.In the third and final case study we examine the forces between proteins in a lipid bilayer membrane. Due to the bending of the membrane surrounding them, such proteins feel mutually attractive forces which can help them to self-organize and act in concert. These finding are relevant at the areal densities estimated for membrane proteins such as the MscL mechanosensitive channel. The findings of the analytical studies were confirmed by a Monte Carlo Markov Chain simulation using the fully two-dimensional potentials between two model proteins in a membrane.Living systems present us with beautiful and intricate structures, from the helices and sheets of a folded protein to the dynamic morphology of cellular organelles and the self-organization of proteins in a biomembrane and a synergy of theoretical and it in silico approaches should enable us to build and refine models of in vivo biological data.

Effective atomic numbers and electron density of dosimetric material

PubMed Central

Kaginelli, S. B.; Rajeshwari, T.; Sharanabasappa; Kerur, B. R.; Kumar, Anil S.

2009-01-01

A novel method for determination of mass attenuation coefficient of x-rays employing NaI (Tl) detector system and radioactive sources is described.in this paper. A rigid geometry arrangement and gating of the spectrometer at FWHM position and selection of absorber foils are all done following detailed investigation, to minimize the effect of small angle scattering and multiple scattering on the mass attenuation coefficient, μ/ρ, value. Firstly, for standardization purposes the mass attenuation coefficients of elemental foils such as Aluminum, Copper, Molybdenum, Tantalum and Lead are measured and then, this method is utilized for dosimetric interested material (sulfates). The experimental mass attenuation coefficient values are compared with the theoretical values to find good agreement between the theory and experiment within one to two per cent. The effective atomic numbers of the biological substitute material are calculated by sum rule and from the graph. The electron density of dosimetric material is calculated using the effective atomic number. The study has discussed in detail the attenuation coefficient, effective atomic number and electron density of dosimetric material/biological substitutes. PMID:20098566

Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eley, John G., E-mail: jeley@som.umaryland.edu; University of Texas Graduate School of Biomedical Sciences, Houston, Texas; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland

Purpose: This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. Methods and Materials: We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breastmore » by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. Results: For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Conclusions: Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.« less

Structure elucidation of two novel yak milk oligosaccharides and their DFT studies

NASA Astrophysics Data System (ADS)

Singh, Ashish Kumar; Ranjan, Ashok Kr.; Srivastava, Gaurav; Deepak, Desh

2016-03-01

Milk is a primary dynamic biological fluid responsible for development of neonates. Besides the other regular constituents it have oligosaccharides in it which are responsible for antitumor, anticancer, antigenic and immunostimulant activities. In our endeavor to find biologically active novel oligosaccharides, yak milk was taken, which is a rich source of oligosaccharide and its milk is used as antihypertensive, antioxidative and heart strengthening agent in folk medicine. For this purpose yak milk was processed by method of Kobata and Ginsburg followed by gel filtration HPLC and CC which resulted in the isolation of two novel milk oligosaccharides namely (I) Grunniose and (II) Vakose. The structure of purified milk oligosaccharides were elucidated with the help of chemical degradation, chemical transformation, spectroscopic techniques like NMR (1H, 13C and 2D-NMR), structure reporter group theory and mass spectrometry. The optimized geometry of compound Grunniose and Vakose, at B3LYP method and 6-311 + G basis set on Gaussian 09 program, show that the compound Grunniose is lower in energy as compared to compound Vakose.

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

PubMed Central

Lakhan, Shaheen Emmanuel

2006-01-01

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science. PMID:16846510

Network neighborhood analysis with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2007-01-15

The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures. The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes. An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).

Mutagenicity of burnt gun propellants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felton, J.S.; Lewis, P.; Knize, M.G.

1989-08-02

The use of the Ames/Salmonella assay as a workplace monitoring method is a long-standing practice at LLNL. This practice has led to the discovery of very mutagenic soot in and around a 4 inch test gun. To the authors' knowledge this is the first finding of mutagenic components in the residue from gun propellants, although there have been numerous reports of mutagenic compounds associated with high explosives -- compounds of entirely different chemical composition (Won et al., 1976). In addition, Ase et al., 1985, analyzed the propellant combustion products of both a M16 rifle and a 105 mm caliber gunmore » with HPLC and GC/MS methods, and found a number of PAHs with known toxicological effects. No biological analysis was done on the residues. Further investigation in our laboratory found that direct acting mutagens where produced upon open burning of the propellants. Small gauge firearms when tested also showed mutagenic residue. Preliminary efforts to identify the mutagenic components estimate that 2-3 compounds are responsible for the biological activity. The identity of these compounds is under investigation. 8 refs., 4 tabs.« less

Evidence Combination From an Evolutionary Game Theory Perspective.

PubMed

Deng, Xinyang; Han, Deqiang; Dezert, Jean; Deng, Yong; Shyr, Yu

2016-09-01

Dempster-Shafer evidence theory is a primary methodology for multisource information fusion because it is good at dealing with uncertain information. This theory provides a Dempster's rule of combination to synthesize multiple evidences from various information sources. However, in some cases, counter-intuitive results may be obtained based on that combination rule. Numerous new or improved methods have been proposed to suppress these counter-intuitive results based on perspectives, such as minimizing the information loss or deviation. Inspired by evolutionary game theory, this paper considers a biological and evolutionary perspective to study the combination of evidences. An evolutionary combination rule (ECR) is proposed to help find the most biologically supported proposition in a multievidence system. Within the proposed ECR, we develop a Jaccard matrix game to formalize the interaction between propositions in evidences, and utilize the replicator dynamics to mimick the evolution of propositions. Experimental results show that the proposed ECR can effectively suppress the counter-intuitive behaviors appeared in typical paradoxes of evidence theory, compared with many existing methods. Properties of the ECR, such as solution's stability and convergence, have been mathematically proved as well.

Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease*

PubMed Central

Dewhurst, Henry; Sundararaman, Niveda

2016-01-01

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable body of potential regulatory elements that impact hundreds of different biological processes important in eukaryotic biology and human health. PMID:27697855

Generation and characterization of biological aerosols for laser measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, Yung-Sung; Barr, E.B.

1995-12-01

Concerns for proliferation of biological weapons including bacteria, fungi, and viruses have prompted research and development on methods for the rapid detection of biological aerosols in the field. Real-time instruments that can distinguish biological aerosols from background dust would be especially useful. Sandia National Laboratories (SNL) is developing a laser-based, real-time instrument for rapid detection of biological aerosols, and ITRI is working with SNL scientists and engineers to evaluate this technology for a wide range of biological aerosols. This paper describes methods being used to generate the characterize the biological aerosols for these tests. In summary, a biosafe system hasmore » been developed for generating and characterizing biological aerosols and using those aerosols to test the SNL laser-based real-time instrument. Such tests are essential in studying methods for rapid detection of airborne biological materials.« less

DCB Funding

Cancer.gov

The Division of Cancer Biology (DCB) funds and supports extramural basic research that investigates the fundamental biology behind cancer. Find out more about DCB's grants process and funding opportunities.

The Molecular Biology of Adenoid Cystic Carcinoma

PubMed Central

Liu, Jia; Shao, Chunbo; Tan, Marietta L.; Mu, David; Ferris, Robert L.; Ha, Patrick K.

2011-01-01

Background Adenoid cystic carcinoma (ACC) is an unusual salivary gland malignancy that remains poorly understood. Standard treatment, including surgery with postoperative radiation therapy have attained reasonable local control rates, but the propensity for distant metastases has limited any improvement in survival over time. Our understanding of the molecular mechanisms driving adenoid cystic carcinoma is quite rudimentary, due to the infrequent nature of its occurrence. Methods An extensive literature review was performed on salivary gland adenoid cystic carcinoma and basic science research findings. Results This review highlights many findings that are emerging about the carcinogenesis of ACC including cytogenetics, tumor suppressor genes, oncogenes, epigenetic alterations, mitochondrial alterations, and biomarker studies. Conclusions While there have been many discoveries, much still remains unknown about this rare malignancy. PMID:22006498

Research Results Ultra-fast Energy Transfer from Monomer to Dimer within a Trimeric Molecule New Progress in Heterogeneous Catalysis Research Key Progress in Research on Terrestrial Carbon Cycle in China A New Progress in Research on the Mechanism of Bio-Invasion New Findings in Anti-viral infection and Control of Inflammation Major Headway in Avian Origin Research New Progress in Gold-Nanoparticle-Based Biochips Topological Insulator Research Made Important Progress Major Progress in Biodiversity Achieved New Developments of Direct Methods in Protein Crystallography Major Progress in China-UK Collaboration on the Causal Relationship between Volcanic Activity and Biological Distinction News in Brief: NSFC set up "Research Fund for Young Foreign Scholars" How Often Does Human DNA Mutate? Research Progress on Colossal Anisotropic Magneto Resistive Effect

NASA Astrophysics Data System (ADS)

2009-01-01

Ultra-fast Energy Transfer from Monomer to Dimer within a Trimeric Molecule New Progress in Heterogeneous Catalysis Research Key Progress in Research on Terrestrial Carbon Cycle in China A New Progress in Research on the Mechanism of Bio-Invasion New Findings in Anti-viral infection and Control of Inflammation Major Headway in Avian Origin Research New Progress in Gold-Nanoparticle-Based Biochips Topological Insulator Research Made Important Progress Major Progress in Biodiversity Achieved New Developments of Direct Methods in Protein Crystallography Major Progress in China-UK Collaboration on the Causal Relationship between Volcanic Activity and Biological Distinction News in Brief: NSFC set up "Research Fund for Young Foreign Scholars" How Often Does Human DNA Mutate? Research Progress on Colossal Anisotropic Magneto Resistive Effect

Using hybridization networks to retrace the evolution of Indo-European languages.

PubMed

Willems, Matthieu; Lord, Etienne; Laforest, Louise; Labelle, Gilbert; Lapointe, François-Joseph; Di Sciullo, Anna Maria; Makarenkov, Vladimir

2016-09-06

Curious parallels between the processes of species and language evolution have been observed by many researchers. Retracing the evolution of Indo-European (IE) languages remains one of the most intriguing intellectual challenges in historical linguistics. Most of the IE language studies use the traditional phylogenetic tree model to represent the evolution of natural languages, thus not taking into account reticulate evolutionary events, such as language hybridization and word borrowing which can be associated with species hybridization and horizontal gene transfer, respectively. More recently, implicit evolutionary networks, such as split graphs and minimal lateral networks, have been used to account for reticulate evolution in linguistics. Striking parallels existing between the evolution of species and natural languages allowed us to apply three computational biology methods for reconstruction of phylogenetic networks to model the evolution of IE languages. We show how the transfer of methods between the two disciplines can be achieved, making necessary methodological adaptations. Considering basic vocabulary data from the well-known Dyen's lexical database, which contains word forms in 84 IE languages for the meanings of a 200-meaning Swadesh list, we adapt a recently developed computational biology algorithm for building explicit hybridization networks to study the evolution of IE languages and compare our findings to the results provided by the split graph and galled network methods. We conclude that explicit phylogenetic networks can be successfully used to identify donors and recipients of lexical material as well as the degree of influence of each donor language on the corresponding recipient languages. We show that our algorithm is well suited to detect reticulate relationships among languages, and present some historical and linguistic justification for the results obtained. Our findings could be further refined if relevant syntactic, phonological and morphological data could be analyzed along with the available lexical data.

Simultaneous analysis of nuclear and mitochondrial DNA, mRNA and miRNA from backspatter from inside parts of firearms generated by shots at "triple contrast" doped ballistic models.

PubMed

Grabmüller, Melanie; Schyma, Christian; Euteneuer, Jan; Madea, Burkhard; Courts, Cornelius

2015-09-01

When a firearm projectile hits a biological target a spray of biological material (e.g., blood and tissue fragments) can be propelled from the entrance wound back towards the firearm. This phenomenon has become known as "backspatter" and if caused by contact shots or shots from short distances traces of backspatter may reach, consolidate on, and be recovered from, the inside surfaces of the firearm. Thus, a comprehensive investigation of firearm-related crimes must not only comprise of wound ballistic assessment but also backspatter analysis, and may even take into account potential correlations between these emergences. The aim of the present study was to evaluate and expand the applicability of the "triple contrast" method by probing its compatibility with forensic analysis of nuclear and mitochondrial DNA and the simultaneous investigation of co-extracted mRNA and miRNA from backspatter collected from internal components of different types of firearms after experimental shootings. We demonstrate that "triple contrast" stained biological samples collected from the inside surfaces of firearms are amenable to forensic co-analysis of DNA and RNA and permit sequence analysis of the entire mtDNA displacement-loop, even for "low template" DNA amounts that preclude standard short tandem repeat DNA analysis. Our findings underscore the "triple contrast" method's usefulness as a research tool in experimental forensic ballistics.

Improving information retrieval in functional analysis.

PubMed

Rodriguez, Juan C; González, Germán A; Fresno, Cristóbal; Llera, Andrea S; Fernández, Elmer A

2016-12-01

Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

PubMed

Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

2015-01-01

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

Analysis of a dynamic model of guard cell signaling reveals the stability of signal propagation

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, RéKa

Analyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. We identified the long-term behaviors of a multi-level, 70-node discrete dynamic model of the stomatal opening process in plants. We reduce the model's huge state space by reducing unregulated nodes and simple mediator nodes, and by simplifying the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models.

Discrete and morphometric traits reveal contrasting patterns and processes in the macroevolutionary history of a clade of scorpions.

PubMed

Mongiardino Koch, N; Ceccarelli, F S; Ojanguren-Affilastro, A A; Ramírez, M J

2017-04-01

Many palaeontological studies have investigated the evolution of entire body plans, generally relying on discrete character-taxon matrices. In contrast, macroevolutionary studies performed by neontologists have mostly focused on morphometric traits. Although these data types are very different, some studies have suggested that they capture common patterns. Nonetheless, the tests employed to support this claim have not explicitly incorporated a phylogenetic framework and may therefore be susceptible to confounding effects due to the presence of common phylogenetic structure. We address this question using the scorpion genus Brachistosternus Pocock 1893 as case study. We make use of a time-calibrated multilocus molecular phylogeny, and compile discrete and traditional morphometric data sets, both capturing the overall morphology of the organisms. We find that morphospaces derived from these matrices are significantly different, and that the degree of discordance cannot be replicated by simulations of random character evolution. Moreover, we find strong support for contrasting modes of evolution, with discrete characters being congruent with an 'early burst' scenario whereas morphometric traits suggest species-specific adaptations to have driven morphological evolution. The inferred macroevolutionary dynamics are therefore contingent on the choice of character type. Finally, we confirm that metrics of correlation fail to detect these profound differences given common phylogenetic structure in both data sets, and that methods incorporating a phylogenetic framework and accounting for expected covariance should be favoured. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

What is bioinformatics? A proposed definition and overview of the field.

PubMed

Luscombe, N M; Greenbaum, D; Gerstein, M

2001-01-01

The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Development Genetic Analysis of General Cognitive Ability from 1 to 12 Years in a Sample of Adoptees, Biological Siblings, and Twins.

ERIC Educational Resources Information Center

Bishop, E. G.; Cherny, Stacey S.; Corley, Robin; Plomin, Robert; DeFries, John C.; Hewitt, John K.

2003-01-01

Studied continuity and change in general cognitive ability from infancy to adolescence in adoptees (107 children), biological siblings (87 pairs), and twins (224 monozygotic and 189 dyzygotic pairs). Findings generally support previous findings about genetic and environmental factors, with the exception that in the transition to adolescence,…

Biological Aging - Criteria for Modeling and a New Mechanistic Model

NASA Astrophysics Data System (ADS)

Pletcher, Scott D.; Neuhauser, Claudia

To stimulate interaction and collaboration across scientific fields, we introduce a minimum set of biological criteria that theoretical models of aging should satisfy. We review results of several recent experiments that examined changes in age-specific mortality rates caused by genetic and environmental manipulation. The empirical data from these experiments is then used to test mathematical models of aging from several different disciplines, including molecular biology, reliability theory, physics, and evolutionary biology/population genetics. We find that none of the current models are consistent with all of the published experimental findings. To provide an example of how our criteria might be applied in practice, we develop a new conceptual model of aging that is consistent with our observations.

Familial transmission of chronic obstructive pulmonary disease in adoptees: a Swedish nationwide family study

PubMed Central

Zöller, Bengt; Li, Xinjun; Sundquist, Jan; Sundquist, Kristina

2015-01-01

Objectives Familial clustering of chronic obstructive pulmonary disease (COPD) is well established, but the familial risk of COPD has not been determined among adoptees. The aim was to determine whether the familial transmission of COPD is related to disease in biological and/or adoptive parents. Design Historic cohort study. Participants 80 214 (50% females). Methods The Swedish Multi-Generation Register was used to follow all Swedish-born adoptees born in 1932–2004 (n=80 214) between 1 January 1964 and 31 December 2010 for COPD (n=1978). The risk of COPD was estimated in adoptees with at least one biological parent with COPD but no adoptive parent with COPD (n=162) compared with adoptees without a biological or adoptive parent with COPD. The risk of COPD was also determined in adoptees with at least one adoptive parent but no biological parent with COPD (n=110), and in adoptees with both affected biological and adoptive parents (n=162). Primary outcome measure COPD in adoptees. Results Adoptees with COPD in at least one biological parent but no adoptive parent were more likely to have COPD than adoptees without a biological or adoptive parent with COPD (standardised incidence ratio, SIR=1.98 (95% CI 1.69 to 2.31)). The familial SIR for adoptees with both a biological parent and an adoptive parent with COPD was 1.68 (95% CI 1.39 to 2.00). Adoptees with at least one adoptive parent with COPD but no biological parent with COPD were not at an increased risk of COPD (SIR=1.12 (95% CI 0.92 to 1.35)). Conclusions The findings of the study show that the familial transmission of COPD is associated with COPD in biological but not adoptive parents, suggesting that genetic or early life factors are important in the familial transmission of COPD. PMID:25869691

Predicting missing links and identifying spurious links via likelihood analysis

NASA Astrophysics Data System (ADS)

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-03-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

Predicting missing links and identifying spurious links via likelihood analysis

PubMed Central

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-01-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

A question of dissemination: Assessing the practices and implications of research in tropical landscapes.

PubMed

Toomey, Anne H; Alvaro, María Eugenia Copa; Aiello-Lammens, Matthew; Loayza Cossio, Oscar; Barlow, Jos

2018-04-24

Current debates in the conservation sciences argue for better integration between research and practice, often citing the importance of the diffusion, dissemination and implementation of scientific knowledge for environmental management and policy. This paper focuses on a relatively well-researched protected area (Madidi National Park) in Bolivia in order to present different interpretations and understandings of the implications and availability of research findings. We draw on findings from quantitative and qualitative methods to determine the extent to which research carried out in the region was disseminated and/or implemented for management actions, and to understand subsequent implications for how local actors perceive the value of research and its role in management and conservation. We discuss the critical consequences of these findings for the future of conservation science and practice in biologically and culturally diverse landscapes, with an explicit call to action for academic institutions to support researchers in developing appropriate dissemination strategies.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Mesoscale Eddies Are Oases for Higher Trophic Marine Life

PubMed Central

Godø, Olav R.; Samuelsen, Annette; Macaulay, Gavin J.; Patel, Ruben; Hjøllo, Solfrid Sætre; Horne, John; Kaartvedt, Stein; Johannessen, Johnny A.

2012-01-01

Mesoscale eddies stimulate biological production in the ocean, but knowledge of energy transfers to higher trophic levels within eddies remains fragmented and not quantified. Increasing the knowledge base is constrained by the inability of traditional sampling methods to adequately sample biological processes at the spatio-temporal scales at which they occur. By combining satellite and acoustic observations over spatial scales of 10 s of km horizontally and 100 s of m vertically, supported by hydrographical and biological sampling we show that anticyclonic eddies shape distribution and density of marine life from the surface to bathyal depths. Fish feed along density structures of eddies, demonstrating that eddies catalyze energy transfer across trophic levels. Eddies create attractive pelagic habitats, analogous to oases in the desert, for higher trophic level aquatic organisms through enhanced 3-D motion that accumulates and redistributes biomass, contributing to overall bioproduction in the ocean. Integrating multidisciplinary observation methodologies promoted a new understanding of biophysical interaction in mesoscale eddies. Our findings emphasize the impact of eddies on the patchiness of biomass in the sea and demonstrate that they provide rich feeding habitat for higher trophic marine life. PMID:22272294

Diffraction Techniques in Structural Biology

PubMed Central

Egli, Martin

2016-01-01

A detailed understanding of chemical and biological function and the mechanisms underlying the molecular activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy, and neutron diffraction are well established and they have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last 20 years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches, and high-speed computing and visualization, now provide specialists and nonspecialists alike with a steady flow of molecular images of unprecedented detail. The present unit combines a general overview of diffraction methods with a detailed description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis, and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. PMID:27248784

Multi-Center Biologic Assignment Trial Comparing Reduced Intensity Allogeneic Hematopoietic Cell Transplant to Hypomethylating Therapy or Best Supportive Care in Patients Aged 50-75 with Intermediate-2 and High Risk Myelodysplastic Syndrome Blood and Marrow Transplant Clinical Trials Network #1102 Study Rationale, Design and Methods

PubMed Central

Saber, Wael; Le Rademacher, Jennifer; Sekeres, Mikkael; Logan, Brent; Lewis, Moira; Mendizabal, Adam; Leifer, Eric; Appelbaum, Frederick R.; Horowitz, Mary M; Nakamura, Ryotaro; Cutler, Corey S.

2014-01-01

The introduction of reduced intensity conditioning regimens (RIC) made it possible to offer allogeneic hematopoietic cell transplantation (alloHCT) to older patients with myelodysplastic syndromes (MDS). However, the relative risks and benefits of alloHCT compared to novel non-transplant therapies continue to be the source of considerable uncertainty. We will perform a prospective biologic assignment trial to compare RIC alloHCT to non-transplant therapies based on donor availability. Primary outcome is 3-year overall survival. Secondary outcomes include leukemia-free survival, quality of life, and cost-effectiveness. Four hundred patients will be enrolled over roughly 3 years. Planned subgroup analyses will evaluate key biologic questions, such as the impact of age & response to hypomethylating agents on treatment effects. Findings from this study potentially may set a new standard of care for older MDS patients who are considered candidates for alloHCT. PMID:24972249

Measuring the shapes of macromolecules – and why it matters

PubMed Central

Li, Jie; Mach, Paul; Koehl, Patrice

2013-01-01

The molecular basis of life rests on the activity of biological macromolecules, mostly nucleic acids and proteins. A perhaps surprising finding that crystallized over the last handful of decades is that geometric reasoning plays a major role in our attempt to understand these activities. In this paper, we address this connection between geometry and biology, focusing on methods for measuring and characterizing the shapes of macromolecules. We briefly review existing numerical and analytical approaches that solve these problems. We cover in more details our own work in this field, focusing on the alpha shape theory as it provides a unifying mathematical framework that enable the analytical calculations of the surface area and volume of a macromolecule represented as a union of balls, the detection of pockets and cavities in the molecule, and the quantification of contacts between the atomic balls. We have shown that each of these quantities can be related to physical properties of the molecule under study and ultimately provides insight on its activity. We conclude with a brief description of new challenges for the alpha shape theory in modern structural biology. PMID:24688748

Diffraction Techniques in Structural Biology

PubMed Central

Egli, Martin

2010-01-01

A detailed understanding of chemical and biological function and the mechanisms underlying the activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy and neutron diffraction are well established and have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last 20 years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches and high-speed computing and visualization, now provide specialists and non-specialists alike with a steady flow of molecular images of unprecedented detail. The present chapter combines a general overview of diffraction methods with a step-by-step description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. PMID:20517991

Diffraction Techniques in Structural Biology.

PubMed

Egli, Martin

2016-06-01

A detailed understanding of chemical and biological function and the mechanisms underlying the molecular activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy, and neutron diffraction are well established and they have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last twenty years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches, and high-speed computing and visualization, now provide specialists and nonspecialists alike with a steady flow of molecular images of unprecedented detail. The present unit combines a general overview of diffraction methods with a detailed description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis, and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

MotifMark: Finding regulatory motifs in DNA sequences.

PubMed

Hassanzadeh, Hamid Reza; Kolhe, Pushkar; Isbell, Charles L; Wang, May D

2017-07-01

The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two important steps in understanding the mechanisms of these biological activities. A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs. Despite their success, these technologies have their own limitations and fall short in precise characterization of motifs, and as a result, require further downstream analysis to extract useful and interpretable information from a haystack of noisy and inaccurate data. Here we propose MotifMark, a new algorithm based on graph theory and machine learning, that can find binding sites on candidate probes and rank their specificity in regard to the underlying transcription factor. We developed a pipeline to analyze experimental data derived from compact universal protein binding microarrays and benchmarked it against two of the most accurate motif search methods. Our results indicate that MotifMark can be a viable alternative technique for prediction of motif from protein binding microarrays and possibly other related high-throughput techniques.

Chaos and Robustness in a Single Family of Genetic Oscillatory Networks

PubMed Central

Fu, Daniel; Tan, Patrick; Kuznetsov, Alexey; Molkov, Yaroslav I.

2014-01-01

Genetic oscillatory networks can be mathematically modeled with delay differential equations (DDEs). Interpreting genetic networks with DDEs gives a more intuitive understanding from a biological standpoint. However, it presents a problem mathematically, for DDEs are by construction infinitely-dimensional and thus cannot be analyzed using methods common for systems of ordinary differential equations (ODEs). In our study, we address this problem by developing a method for reducing infinitely-dimensional DDEs to two- and three-dimensional systems of ODEs. We find that the three-dimensional reductions provide qualitative improvements over the two-dimensional reductions. We find that the reducibility of a DDE corresponds to its robustness. For non-robust DDEs that exhibit high-dimensional dynamics, we calculate analytic dimension lines to predict the dependence of the DDEs’ correlation dimension on parameters. From these lines, we deduce that the correlation dimension of non-robust DDEs grows linearly with the delay. On the other hand, for robust DDEs, we find that the period of oscillation grows linearly with delay. We find that DDEs with exclusively negative feedback are robust, whereas DDEs with feedback that changes its sign are not robust. We find that non-saturable degradation damps oscillations and narrows the range of parameter values for which oscillations exist. Finally, we deduce that natural genetic oscillators with highly-regular periods likely have solely negative feedback. PMID:24667178

A Bayesian Dose-finding Design for Oncology Clinical Trials of Combinational Biological Agents

PubMed Central

Cai, Chunyan; Yuan, Ying; Ji, Yuan

2013-01-01

Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which efficacy and toxicity monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a dose-finding design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. PMID:24511160

Stepparents and parenting stress: the roles of gender, marital quality, and views about gender roles.

PubMed

Shapiro, Danielle

2014-03-01

Previous research suggests that stepparenting can be stressful, although the mechanisms that contribute to the experience of parenting stress in stepfamilies are less clear. This study examines gender, marital quality, and views about gendered family roles as correlates of parenting stress among 310 stepmothers, stepfathers, and biological mothers and fathers. Findings suggest that stepparents, and especially stepmothers, experience higher levels of parenting stress than biological parents. Findings also suggest that less traditional views about gendered family roles and higher dyadic adjustment are associated with lower parenting stress for stepparents, particularly in combination. Stepparents reporting both of these protective factors were indistinguishable in terms of parenting stress from biological parents. These findings indicate potential pathways to mitigate the stress associated with stepparenting. © 2014 FPI, Inc.

Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states.

PubMed

Li, Wei Vivian; Razaee, Zahra S; Li, Jingyi Jessica

2016-01-11

The dynamics of epigenomic marks in their relevant chromatin states regulate distinct gene expression patterns, biological functions and phenotypic variations in biological processes. The availability of high-throughput epigenomic data generated by next-generation sequencing technologies allows a data-driven approach to evaluate the similarities and differences of diverse tissue and cell types in terms of epigenomic features. While ChromImpute has allowed for the imputation of large-scale epigenomic information to yield more robust data to capture meaningful relationships between biological samples, widely used methods such as hierarchical clustering and correlation analysis cannot adequately utilize epigenomic data to accurately reveal the distinction and grouping of different tissue and cell types. We utilize a three-step testing procedure-ANOVA, t test and overlap test to identify tissue/cell-type- associated enhancers and promoters and to calculate a newly defined Epigenomic Overlap Measure (EPOM). EPOM results in a clear correspondence map of biological samples from different tissue and cell types through comparison of epigenomic marks evaluated in their relevant chromatin states. Correspondence maps by EPOM show strong capability in distinguishing and grouping different tissue and cell types and reveal biologically meaningful similarities between Heart and Muscle, Blood & T-cell and HSC & B-cell, Brain and Neurosphere, etc. The gene ontology enrichment analysis both supports and explains the discoveries made by EPOM and suggests that the associated enhancers and promoters demonstrate distinguishable functions across tissue and cell types. Moreover, the tissue/cell-type-associated enhancers and promoters show enrichment in the disease-related SNPs that are also associated with the corresponding tissue or cell types. This agreement suggests the potential of identifying causal genetic variants relevant to cell-type-specific diseases from our identified associated enhancers and promoters. The proposed EPOM measure demonstrates superior capability in grouping and finding a clear correspondence map of biological samples from different tissue and cell types. The identified associated enhancers and promoters provide a comprehensive catalog to study distinct biological processes and disease variants in different tissue and cell types. Our results also find that the associated promoters exhibit more cell-type-specific functions than the associated enhancers do, suggesting that the non-associated promoters have more housekeeping functions than the non-associated enhancers.

Designing optimal stimuli to control neuronal spike timing

PubMed Central

Packer, Adam M.; Yuste, Rafael; Paninski, Liam

2011-01-01

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

On the brink of extinction: the future of translational physician-scientists in the United States.

PubMed

Furuya, Hideki; Brenner, Dean; Rosser, Charles J

2017-05-01

Over the past decade, we have seen an unparalleled growth in our knowledge of cancer biology and the translation of this biology into a new generation of therapeutic tools that are changing cancer treatment outcomes. With the continued explosion of new biologic discoveries, we find ourselves with a limited number of trained and engaged translational physician-scientists capable of bridging the chasm between basic science and clinical science. Here, we discuss the current state translational physician-scientists find themselves in and offer solutions to navigate during this difficult time.

Creative design inspired by biological knowledge: Technologies and methods

NASA Astrophysics Data System (ADS)

Tan, Runhua; Liu, Wei; Cao, Guozhong; Shi, Yuan

2018-05-01

Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market. To identify the technologies and methods that can facilitate the development of biologically inspired creative designs, this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields. Afterward, this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design (BID) process. This research then discusses the future development trends of the BID process before presenting the conclusions.

Chemical ecology of the emerald ash borer Agrilus planipennis.

PubMed

Crook, Damon J; Mastro, Victor C

2010-01-01

The emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae) is a serious invasive pest that has caused devastating mortality of ash trees (Fraxinus sp., Oleaceae) since it was first identified in North America in 2002. Shortly after its discovery, surveys were conducted, based on the visual inspection of trees. The shortcomings of visual surveys have led to a critical research need to find an efficient survey method for detecting A. planipennis infestations. Here, we present a review of research that has led to the development of effective trapping methods for A. planipennis. Studies on the insect's biology and behavior have led to the identification of several potential attractants as well as the design of a visually attractive trap. The ongoing challenge in developing an optimally efficient trapping methodology for A. planipennis will involve finding the best combination of variables, such as trap shape, trap color (or other visual properties), trap placement, lure components, as well as the ratios and release rates of those components.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series

PubMed Central

Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C.; Golino, Caroline A.; Kemper, Peter; Saha, Margaret S.

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features. PMID:27977764

A cluster merging method for time series microarray with production values.

PubMed

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Using the Scientific Method to Motivate Biology Students to Study Precalculus

ERIC Educational Resources Information Center

Fulton, James P.; Sabatino, Linda

2008-01-01

During the last two years we have developed a precalculus course customized around biology by using the scientific method as a framework to engage and motivate biology students. Historically, the precalculus and calculus courses required for the Suffolk County Community College biology curriculum were designed using examples from the physical…

On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types

PubMed Central

2014-01-01

Background Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees. Results The new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives. Conclusions The new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://geneweaver.org. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practical computational resource is adaptable to almost any applications environment in which bipartite graphs can be used to model relationships between pairs of heterogeneous entities. PMID:24731198

Characterizing the topology of probabilistic biological networks.

PubMed

Todor, Andrei; Dobra, Alin; Kahveci, Tamer

2013-01-01

Biological interactions are often uncertain events, that may or may not take place with some probability. This uncertainty leads to a massive number of alternative interaction topologies for each such network. The existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. In this paper, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. Using our mathematical representation, we develop a method that can accurately describe the degree distribution of such networks. We also take one more step and extend our method to accurately compute the joint-degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. Our method works quickly even for entire protein-protein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of node-degree and joint-degree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that power-law and log-normal models best describe degree distributions for both probabilistic and deterministic networks. Moreover, the inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected. We also show that probabilistic networks are more robust for node-degree distribution computation than the deterministic ones. all the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/projects/probNet/.

A computer simulation approach to quantify the true area and true area compressibility modulus of biological membranes.

PubMed

Chacón, Enrique; Tarazona, Pedro; Bresme, Fernando

2015-07-21

We present a new computational approach to quantify the area per lipid and the area compressibility modulus of biological membranes. Our method relies on the analysis of the membrane fluctuations using our recently introduced coupled undulatory (CU) mode [Tarazona et al., J. Chem. Phys. 139, 094902 (2013)], which provides excellent estimates of the bending modulus of model membranes. Unlike the projected area, widely used in computer simulations of membranes, the CU area is thermodynamically consistent. This new area definition makes it possible to accurately estimate the area of the undulating bilayer, and the area per lipid, by excluding any contributions related to the phospholipid protrusions. We find that the area per phospholipid and the area compressibility modulus features a negligible dependence with system size, making possible their computation using truly small bilayers, involving a few hundred lipids. The area compressibility modulus obtained from the analysis of the CU area fluctuations is fully consistent with the Hooke's law route. Unlike existing methods, our approach relies on a single simulation, and no a priori knowledge of the bending modulus is required. We illustrate our method by analyzing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine bilayers using the coarse grained MARTINI force-field. The area per lipid and area compressibility modulus obtained with our method and the MARTINI forcefield are consistent with previous studies of these bilayers.

A computer simulation approach to quantify the true area and true area compressibility modulus of biological membranes

NASA Astrophysics Data System (ADS)

Chacón, Enrique; Tarazona, Pedro; Bresme, Fernando

2015-07-01

We present a new computational approach to quantify the area per lipid and the area compressibility modulus of biological membranes. Our method relies on the analysis of the membrane fluctuations using our recently introduced coupled undulatory (CU) mode [Tarazona et al., J. Chem. Phys. 139, 094902 (2013)], which provides excellent estimates of the bending modulus of model membranes. Unlike the projected area, widely used in computer simulations of membranes, the CU area is thermodynamically consistent. This new area definition makes it possible to accurately estimate the area of the undulating bilayer, and the area per lipid, by excluding any contributions related to the phospholipid protrusions. We find that the area per phospholipid and the area compressibility modulus features a negligible dependence with system size, making possible their computation using truly small bilayers, involving a few hundred lipids. The area compressibility modulus obtained from the analysis of the CU area fluctuations is fully consistent with the Hooke's law route. Unlike existing methods, our approach relies on a single simulation, and no a priori knowledge of the bending modulus is required. We illustrate our method by analyzing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine bilayers using the coarse grained MARTINI force-field. The area per lipid and area compressibility modulus obtained with our method and the MARTINI forcefield are consistent with previous studies of these bilayers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thessen, Anne E.; Bunker, Daniel E.; Buttigieg, Pier Luigi

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies aremore » well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.« less

Emerging semantics to link phenotype and environment

PubMed Central

Bunker, Daniel E.; Buttigieg, Pier Luigi; Cooper, Laurel D.; Dahdul, Wasila M.; Domisch, Sami; Franz, Nico M.; Jaiswal, Pankaj; Lawrence-Dill, Carolyn J.; Midford, Peter E.; Mungall, Christopher J.; Ramírez, Martín J.; Specht, Chelsea D.; Vogt, Lars; Vos, Rutger Aldo; Walls, Ramona L.; White, Jeffrey W.; Zhang, Guanyang; Deans, Andrew R.; Huala, Eva; Lewis, Suzanna E.; Mabee, Paula M.

2015-01-01

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments. PMID:26713234

Emerging semantics to link phenotype and environment.

PubMed

Thessen, Anne E; Bunker, Daniel E; Buttigieg, Pier Luigi; Cooper, Laurel D; Dahdul, Wasila M; Domisch, Sami; Franz, Nico M; Jaiswal, Pankaj; Lawrence-Dill, Carolyn J; Midford, Peter E; Mungall, Christopher J; Ramírez, Martín J; Specht, Chelsea D; Vogt, Lars; Vos, Rutger Aldo; Walls, Ramona L; White, Jeffrey W; Zhang, Guanyang; Deans, Andrew R; Huala, Eva; Lewis, Suzanna E; Mabee, Paula M

2015-01-01

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

Emerging semantics to link phenotype and environment

DOE PAGES

Thessen, Anne E.; Bunker, Daniel E.; Buttigieg, Pier Luigi; ...

2015-12-14

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies aremore » well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.« less

Adalimumab Treatment in Biologically Naïve Crohn's Disease: Relationship with Ectopic MUC5AC Expression and Endoscopic Improvement

PubMed Central

Mizoshita, Tsutomu; Tanida, Satoshi; Tsukamoto, Hironobu; Ozeki, Keiji; Katano, Takahito; Nishiwaki, Hirotaka; Ebi, Masahide; Mori, Yoshinori; Kubota, Eiji; Kataoka, Hiromi; Kamiya, Takeshi; Joh, Takashi

2014-01-01

Background. Adalimumab (ADA) is effective for patients with Crohn's disease (CD). However, there have been few reports on ADA therapy with respect to its relationship with pathologic findings and drug efficacy in biologically naïve CD cases. Methods. Fifteen patients with active biologically naïve CD were treated with ADA. We examined them clinically and pathologically with ectopic MUC5AC expression in the lesions before and after 12 and 52 weeks of ADA therapy, retrospectively. Results. Both mean CD activity index scores and serum C-reactive protein values were significantly lower after ADA therapy (P < 0.001). In the MUC5AC negative group, all cases exhibited clinical remission (CR) and endoscopic improvement at 52 weeks. In MUC5AC positive groups, loss of MUC5AC expression was detected in cases having CR and endoscopic improvement at 52 weeks, while remnant ectopic MUC5AC expression was observed in those exhibiting no endoscopic improvement and flare up after 52 weeks. Conclusions. ADA leads to CR and endoscopic improvement in biologically naïve CD cases. In addition, ectopic MUC5AC expression may be a predictive marker of flare up and endoscopic improvement in the intestines of CD patients. PMID:24829572

Broad issues to consider for library involvement in bioinformatics*

PubMed Central

Geer, Renata C.

2006-01-01

Background: The information landscape in biological and medical research has grown far beyond literature to include a wide variety of databases generated by research fields such as molecular biology and genomics. The traditional role of libraries to collect, organize, and provide access to information can expand naturally to encompass these new data domains. Methods: This paper discusses the current and potential role of libraries in bioinformatics using empirical evidence and experience from eleven years of work in user services at the National Center for Biotechnology Information. Findings: Medical and science libraries over the last decade have begun to establish educational and support programs to address the challenges users face in the effective and efficient use of a plethora of molecular biology databases and retrieval and analysis tools. As more libraries begin to establish a role in this area, the issues they face include assessment of user needs and skills, identification of existing services, development of plans for new services, recruitment and training of specialized staff, and establishment of collaborations with bioinformatics centers at their institutions. Conclusions: Increasing library involvement in bioinformatics can help address information needs of a broad range of students, researchers, and clinicians and ultimately help realize the power of bioinformatics resources in making new biological discoveries. PMID:16888662

Differential effects of plant species on a mite pest (Tetranychus utricae) and its predator (Phytoseiulus persimilis): implications for biological control.

PubMed

Skirvin, D J; de Courcy Williams, M

1999-06-01

The influence of plant species on the population dynamics of the spider mite pest, Tetranychus urticae, and its predator, Phytoseiulus persimilis, was examined as a prerequisite to effective biological control on ornamental nursery stock. Experiments have been done to investigate how the development, fecundity and movement of T. urticae, and the movement of P. persimilis were affected by plant species. A novel experimental method, which incorporates plant structure, was used to investigate the functional response of P. persimilis. Development times for T. urticae were consistent with published data and did not differ with plant species in a biologically meaningful way. Plant species was shown to have a major influence on fecundity (P < 0.001) and movement of the pest mite (P < 0.01), but no influence on the movement of the predator. The movement of both pest and predator was shown to be related to the density of the adult pest mites on the plant (P < 0.001). Plant structure affected the functional response, particularly in relation to the ability of the predator to locate prey at low densities. The impact of these findings on the effective use of biological control on ornamental nursery stock is discussed.

High-refractive index of acrylate embedding resin clarifies mouse brain tissue

NASA Astrophysics Data System (ADS)

Zhou, Hongfu; Xiong, Yumiao; Wang, Yu; Wang, Xiaojun; Li, Pei; Gang, Yadong; Liu, Xiuli; Zeng, Shaoqun

2017-11-01

Biological tissue transparency combined with light-sheet fluorescence microscopy is a useful method for studying the neural structure of biological tissues. The development of light-sheet fluorescence microscopy also promotes progress in biological tissue clearing methods. The current clarifying methods mostly use liquid reagent to denature protein or remove lipids first, to eliminate or reduce the scattering index or refractive index of the biological tissue. However, denaturing protein and removing lipids require complex procedures or an extended time period. Therefore, here we have developed acrylate resin with a high refractive index, which causes clearing of biological tissue directly after polymerization. This method can improve endogenous fluorescence retention by adjusting the pH value of the resin monomer.

Data warehousing in molecular biology.

PubMed

Schönbach, C; Kowalski-Saunders, P; Brusic, V

2000-05-01

In the business and healthcare sectors data warehousing has provided effective solutions for information usage and knowledge discovery from databases. However, data warehousing applications in the biological research and development (R&D) sector are lagging far behind. The fuzziness and complexity of biological data represent a major challenge in data warehousing for molecular biology. By combining experiences in other domains with our findings from building a model database, we have defined the requirements for data warehousing in molecular biology.

Evolution, Biology, and Society: A Conversation for the 21st-Century Sociology Classroom

ERIC Educational Resources Information Center

Machalek, Richard; Martin, Michael W.

2010-01-01

Recently, a growing contingent of "evolutionary sociologists" has begun to integrate theoretical ideas and empirical findings derived from evolutionary biology, especially sociobiology, into a variety of sociological inquiries. Without capitulating to a naive version of either biological reductionism or genetic determinism, these researchers and…

Long Term Ex Vivo Culture and Live Imaging of Drosophila Larval Imaginal Discs.

PubMed

Tsao, Chia-Kang; Ku, Hui-Yu; Lee, Yuan-Ming; Huang, Yu-Fen; Sun, Yi Henry

Continuous imaging of live tissues provides clear temporal sequence of biological events. The Drosophila imaginal discs have been popular experimental subjects for the study of a wide variety of biological phenomena, but long term culture that allows normal development has not been satisfactory. Here we report a culture method that can sustain normal development for 18 hours and allows live imaging. The method is validated in multiple discs and for cell proliferation, differentiation and migration. However, it does not support disc growth and cannot support cell proliferation for more than 7 to 12 hr. We monitored the cellular behavior of retinal basal glia in the developing eye disc and found that distinct glia type has distinct properties of proliferation and migration. The live imaging provided direct proof that wrapping glia differentiated from existing glia after migrating to the anterior front, and unexpectedly found that they undergo endoreplication before wrapping axons, and their nuclei migrate up and down along the axons. UV-induced specific labeling of a single carpet glia also showed that the two carpet glia membrane do not overlap and suggests a tiling or repulsion mechanism between the two cells. These findings demonstrated the usefulness of an ex vivo culture method and live imaging.

Use of synchrotron tomography to image naturalistic anatomy in insects

NASA Astrophysics Data System (ADS)

Socha, John J.; De Carlo, Francesco

2008-08-01

Understanding the morphology of anatomical structures is a cornerstone of biology. For small animals, classical methods such as histology have provided a wealth of data, but such techniques can be problematic due to destruction of the sample. More importantly, fixation and physical slicing can cause deformation of anatomy, a critical limitation when precise three-dimensional data are required. Modern techniques such as confocal microscopy, MRI, and tabletop x-ray microCT provide effective non-invasive methods, but each of these tools each has limitations including sample size constraints, resolution limits, and difficulty visualizing soft tissue. Our research group at the Advanced Photon Source (Argonne National Laboratory) studies physiological processes in insects, focusing on the dynamics of breathing and feeding. To determine the size, shape, and relative location of internal anatomy in insects, we use synchrotron microtomography at the beamline 2-BM to image structures including tracheal tubes, muscles, and gut. Because obtaining naturalistic, undeformed anatomical information is a key component of our studies, we have developed methods to image fresh and non-fixed whole animals and tissues. Although motion artifacts remain a problem, we have successfully imaged multiple species including beetles, ants, fruit flies, and butterflies. Here we discuss advances in biological imaging and highlight key findings in insect morphology.

Nested Sampling for Bayesian Model Comparison in the Context of Salmonella Disease Dynamics

PubMed Central

Dybowski, Richard; McKinley, Trevelyan J.; Mastroeni, Pietro; Restif, Olivier

2013-01-01

Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike's Information Criterion (AIC), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A new, efficient method for the computation of posterior probabilities has recently been proposed and applied to complex problems from the physical sciences. Here we demonstrate how nested sampling can be used for inference and model comparison in biological sciences. We present a reanalysis of data from experimental infection of mice with Salmonella enterica showing the distribution of bacteria in liver cells. In addition to confirming the main finding of the original analysis, which relied on AIC, our approach provides: (a) integration across the parameter space, (b) estimation of the posterior parameter distributions (with visualisations of parameter correlations), and (c) estimation of the posterior predictive distributions for goodness-of-fit assessments of the models. The goodness-of-fit results suggest that alternative mechanistic models and a relaxation of the quasi-stationary assumption should be considered. PMID:24376528

Template-Independent Enzymatic Oligonucleotide Synthesis (TiEOS): Its History, Prospects, and Challenges.

PubMed

Jensen, Michael A; Davis, Ronald W

2018-03-27

There is a growing demand for sustainable methods in research and development, where instead of hazardous chemicals, an aqueous medium is chosen to perform biological reactions. In this Perspective, we examine the history and current methodology of using enzymes to generate artificial single-stranded DNA. By using traditional solid-phase phosphoramidite chemistry as a metric, we also explore criteria for the method of template-independent enzymatic oligonucleotide synthesis (TiEOS). As its key component, we delve into the biology of one of the most enigmatic enzymes, terminal deoxynucleotidyl transferase (TdT). As TdT is found to exponentially increase antigen receptor diversity in the vertebrate immune system by adding nucleotides in a template-free manner, researchers have exploited this function as an alternative to the phosphoramidite synthesis method. Though TdT is currently the preferred enzyme for TiEOS, its random nucleotide incorporation presents a barrier in synthesis automation. Taking a closer look at the TiEOS cycle, particularly the coupling step, we find it is comprised of additions > n+1 and deletions. By tapping into the physical and biochemical properties of TdT, we strive to further elucidate its mercurial behavior and offer ways to better optimize TiEOS for production-grade oligonucleotide synthesis.

Text-based phenotypic profiles incorporating biochemical phenotypes of inborn errors of metabolism improve phenomics-based diagnosis.

PubMed

Lee, Jessica J Y; Gottlieb, Michael M; Lever, Jake; Jones, Steven J M; Blau, Nenad; van Karnebeek, Clara D M; Wasserman, Wyeth W

2018-05-01

Phenomics is the comprehensive study of phenotypes at every level of biology: from metabolites to organisms. With high throughput technologies increasing the scope of biological discoveries, the field of phenomics has been developing rapid and precise methods to collect, catalog, and analyze phenotypes. Such methods have allowed phenotypic data to be widely used in medical applications, from assisting clinical diagnoses to prioritizing genomic diagnoses. To channel the benefits of phenomics into the field of inborn errors of metabolism (IEM), we have recently launched IEMbase, an expert-curated knowledgebase of IEM and their disease-characterizing phenotypes. While our efforts with IEMbase have realized benefits, taking full advantage of phenomics requires a comprehensive curation of IEM phenotypes in core phenomics projects, which is dependent upon contributions from the IEM clinical and research community. Here, we assess the inclusion of IEM biochemical phenotypes in a core phenomics project, the Human Phenotype Ontology. We then demonstrate the utility of biochemical phenotypes using a text-based phenomics method to predict gene-disease relationships, showing that the prediction of IEM genes is significantly better using biochemical rather than clinical profiles. The findings herein provide a motivating goal for the IEM community to expand the computationally accessible descriptions of biochemical phenotypes associated with IEM in phenomics resources.

Functional Abstraction as a Method to Discover Knowledge in Gene Ontologies

PubMed Central

Ultsch, Alfred; Lötsch, Jörn

2014-01-01

Computational analyses of functions of gene sets obtained in microarray analyses or by topical database searches are increasingly important in biology. To understand their functions, the sets are usually mapped to Gene Ontology knowledge bases by means of over-representation analysis (ORA). Its result represents the specific knowledge of the functionality of the gene set. However, the specific ontology typically consists of many terms and relationships, hindering the understanding of the ‘main story’. We developed a methodology to identify a comprehensibly small number of GO terms as “headlines” of the specific ontology allowing to understand all central aspects of the roles of the involved genes. The Functional Abstraction method finds a set of headlines that is specific enough to cover all details of a specific ontology and is abstract enough for human comprehension. This method exceeds the classical approaches at ORA abstraction and by focusing on information rather than decorrelation of GO terms, it directly targets human comprehension. Functional abstraction provides, with a maximum of certainty, information value, coverage and conciseness, a representation of the biological functions in a gene set plays a role. This is the necessary means to interpret complex Gene Ontology results thus strengthening the role of functional genomics in biomarker and drug discovery. PMID:24587272

A preliminary approach to creating an overview of lactoferrin multi-functionality utilizing a text mining method.

PubMed

Shimazaki, Kei-ichi; Kushida, Tatsuya

2010-06-01

Lactoferrin is a multi-functional metal-binding glycoprotein that exhibits many biological functions of interest to many researchers from the fields of clinical medicine, dentistry, pharmacology, veterinary medicine, nutrition and milk science. To date, a number of academic reports concerning the biological activities of lactoferrin have been published and are easily accessible through public data repositories. However, as the literature is expanding daily, this presents challenges in understanding the larger picture of lactoferrin function and mechanisms. In order to overcome the "analysis paralysis" associated with lactoferrin information, we attempted to apply a text mining method to the accumulated lactoferrin literature. To this end, we used the information extraction system GENPAC (provided by Nalapro Technologies Inc., Tokyo). This information extraction system uses natural language processing and text mining technology. This system analyzes the sentences and titles from abstracts stored in the PubMed database, and can automatically extract binary relations that consist of interactions between genes/proteins, chemicals and diseases/functions. We expect that such information visualization analysis will be useful in determining novel relationships among a multitude of lactoferrin functions and mechanisms. We have demonstrated the utilization of this method to find pathways of lactoferrin participation in neovascularization, Helicobacter pylori attack on gastric mucosa, atopic dermatitis and lipid metabolism.

Automated detection of discourse segment and experimental types from the text of cancer pathway results sections.

PubMed

Burns, Gully A P C; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H

2016-01-01

Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data's meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide accurate, automated methods for biocuration. We also suggest the need for finer-grained curation of experimental methods used when constructing molecular biology databases. © The Author(s) 2016. Published by Oxford University Press.

Vision and Change in Biology Undergraduate Education: Vision and Change from the Funding Front

ERIC Educational Resources Information Center

Holm, Bethany; Carter, Virginia Celeste; Woodin, Terry

2011-01-01

The purpose of this short article is to (a) briefly summarize the findings of two important recent resources concerning the future of biology in the 21st century; one, Vision and Change, A Call to Action [AAAS, 2009. AAAS, Washington, DC], concerned with undergraduate education in biology, the other, A New Biology for the 21st Century [National…

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

PubMed Central

2012-01-01

Background Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. PMID:22876834

Prosecutors' Perspectives on Biological Evidence and Injury Evidence in Sexual Assault Cases.

PubMed

Alderden, Megan; Cross, Theodore P; Vlajnic, Maja; Siller, Laura

2018-06-01

Little prior research has explored how prosecutors perceive and utilize biological and injury evidences in sexual assault cases. In this qualitative study, semistructured interviews were conducted with assistant district attorneys (ADAs) working in an urban district attorney's office in the northeastern United States. ADAs were asked to describe how biological and injury evidences could be probative and their strategies for using this evidence. The interviews suggest that prosecutors perceive the probative value of biological and injury evidences on a continuum, varying based on case characteristics. Prosecutors felt that undergoing a forensic medical examination in itself supported victims' credibility. Biological evidence bolstered victims' credibility if it matched the victim's account better than the defendant's. They perceived DNA evidence as helpful when it identified unknown suspects, confirmed identification of suspects by other means, or rebutted defendants' denial of sexual contact. DNA evidence was also helpful when victims were incapacitated, too traumatized to recall or talk about the assault, or too young to identify assailants, and when police used the information in interrogating suspects. The biggest limitation to biological evidence prosecutors cited was overcoming the consent defense. The ADAs reported they used DNA evidence even when it was not particularly probative, because it confirms the correct person is being prosecuted, it communicates the victim's and prosecution's seriousness, and it meets jury expectations in trials. Prosecutors found injury evidence useful because it corroborated victims' accounts and helped refute defendant claims of consensual sex. The findings may assist in educating others about biological and injury evidences in these cases, and could inspire professionals and advocates to work to develop and support a broad range of investigative methods.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

Biology and therapy of fibromyalgia. Functional magnetic resonance imaging findings in fibromyalgia

PubMed Central

Williams, David A; Gracely, Richard H

2006-01-01

Techniques in neuroimaging such as functional magnetic resonance imaging (fMRI) have helped to provide insights into the role of supraspinal mechanisms in pain perception. This review focuses on studies that have applied fMRI in an attempt to gain a better understanding of the mechanisms involved in the processing of pain associated with fibromyalgia. This article provides an overview of the nociceptive system as it functions normally, reviews functional brain imaging methods, and integrates the existing literature utilizing fMRI to study central pain mechanisms in fibromyalgia. PMID:17254318

The prevention and control of avian influenza: the avian influenza coordinated agriculture project.

PubMed

Cardona, C; Slemons, R; Perez, D

2009-04-01

The Avian Influenza Coordinated Agriculture Project (AICAP) entitled "Prevention and Control of Avian Influenza in the US" strives to be a significant point of reference for the poultry industry and the general public in matters related to the biology, risks associated with, and the methods used to prevent and control avian influenza. To this end, AICAP has been remarkably successful in generating research data, publications through an extensive network of university- and agency-based researchers, and extending findings to stakeholders. An overview of the highlights of AICAP research is presented.

Bone, Calcium and Spaceflight: A Living Systems Experiment Relating Animals and Plants the Effects of Calcium on Plant Growth and Development

NASA Technical Reports Server (NTRS)

Reiss-Bubenheim, Debra; Navarro, B. J.; Souza, Kenneth A. (Technical Monitor)

1994-01-01

This educational outreach activity provided students with information about ARC's role in conducting life sciences research in space. Students were introduced to the scientific method while conducting a plant experiment that was correlated to the flight animal experiment. Students made daily observations, collected data and reported on their findings. This classroom experiment providing a hands-on learning opportunity about terrestrial and space biology in which exposed the students to new fields of study for future endeavors.

SCOUT: simultaneous time segmentation and community detection in dynamic networks

PubMed Central

Hulovatyy, Yuriy; Milenković, Tijana

2016-01-01

Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging. PMID:27881879

Comparative methods for the analysis of gene-expression evolution: an example using yeast functional genomic data.

PubMed

Oakley, Todd H; Gu, Zhenglong; Abouheif, Ehab; Patel, Nipam H; Li, Wen-Hsiung

2005-01-01

Understanding the evolution of gene function is a primary challenge of modern evolutionary biology. Despite an expanding database from genomic and developmental studies, we are lacking quantitative methods for analyzing the evolution of some important measures of gene function, such as gene-expression patterns. Here, we introduce phylogenetic comparative methods to compare different models of gene-expression evolution in a maximum-likelihood framework. We find that expression of duplicated genes has evolved according to a nonphylogenetic model, where closely related genes are no more likely than more distantly related genes to share common expression patterns. These results are consistent with previous studies that found rapid evolution of gene expression during the history of yeast. The comparative methods presented here are general enough to test a wide range of evolutionary hypotheses using genomic-scale data from any organism.

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

NASA Astrophysics Data System (ADS)

PourkamaliAnaraki, Maryam; Sadeghi, Mehdi

2016-03-01

Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/

Ultrafast Method for the Analysis of Fluorescence Lifetime Imaging Microscopy Data Based on the Laguerre Expansion Technique

PubMed Central

Jo, Javier A.; Fang, Qiyin; Marcu, Laura

2007-01-01

We report a new deconvolution method for fluorescence lifetime imaging microscopy (FLIM) based on the Laguerre expansion technique. The performance of this method was tested on synthetic and real FLIM images. The following interesting properties of this technique were demonstrated. 1) The fluorescence intensity decay can be estimated simultaneously for all pixels, without a priori assumption of the decay functional form. 2) The computation speed is extremely fast, performing at least two orders of magnitude faster than current algorithms. 3) The estimated maps of Laguerre expansion coefficients provide a new domain for representing FLIM information. 4) The number of images required for the analysis is relatively small, allowing reduction of the acquisition time. These findings indicate that the developed Laguerre expansion technique for FLIM analysis represents a robust and extremely fast deconvolution method that enables practical applications of FLIM in medicine, biology, biochemistry, and chemistry. PMID:19444338

Biological Dialogues: How to Teach Your Students to Learn Fluency in Biology

ERIC Educational Resources Information Center

May, S. Randolph; Cook, David L.; May, Marilyn K.

2013-01-01

Biology courses have thousands of words to learn in order to intelligently discuss the subject and take tests over the material. Biological fluency is an important goal for students, and practical methods based on constructivist pedagogies can be employed to promote it. We present a method in which pairs of students write dialogues from…

Novel Strategy for Discrimination of Transcription Factor Binding Motifs Employing Mathematical Neural Network

NASA Astrophysics Data System (ADS)

Sugimoto, Asuka; Sumi, Takuya; Kang, Jiyoung; Tateno, Masaru

2017-07-01

Recognition in biological macromolecular systems, such as DNA-protein recognition, is one of the most crucial problems to solve toward understanding the fundamental mechanisms of various biological processes. Since specific base sequences of genome DNA are discriminated by proteins, such as transcription factors (TFs), finding TF binding motifs (TFBMs) in whole genome DNA sequences is currently a central issue in interdisciplinary biophysical and information sciences. In the present study, a novel strategy to create a discriminant function for discrimination of TFBMs by constituting mathematical neural networks (NNs) is proposed, together with a method to determine the boundary of signals (TFBMs) and noise in the NN-score (output) space. This analysis also leads to the mathematical limitation of discrimination in the recognition of features representing TFBMs, in an information geometrical manifold. Thus, the present strategy enables the identification of the whole space of TFBMs, right up to the noise boundary.

Synthetic polymers enable non-vitreous cellular cryopreservation by reducing ice crystal growth during thawing.

PubMed

Deller, Robert C; Vatish, Manu; Mitchell, Daniel A; Gibson, Matthew I

2014-01-01

The cryopreservation of cells, tissue and organs is fundamental to modern biotechnology, transplantation medicine and chemical biology. The current state-of-the-art method of cryopreservation is the addition of large amounts of organic solvents such as glycerol or dimethyl sulfoxide, to promote vitrification and prevent ice formation. Here we employ a synthetic, biomimetic, polymer, which is capable of slowing the growth of ice crystals in a manner similar to antifreeze (glyco)proteins to enhance the cryopreservation of sheep and human red blood cells. We find that only 0.1 wt% of the polymer is required to attain significant cell recovery post freezing, compared with over 20 wt% required for solvent-based strategies. These results demonstrate that synthetic antifreeze (glyco)protein mimics could have a crucial role in modern regenerative medicine to improve the storage and distribution of biological material for transplantation.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

PubMed Central

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

Magnetic Field Triggered Multicycle Damage Sensing and Self Healing.

PubMed

Ahmed, Anansa S; Ramanujan, R V

2015-09-08

Multifunctional materials inspired by biological structures have attracted great interest, e.g. for wearable/ flexible "skin" and smart coatings. A current challenge in this area is to develop an artificial material which mimics biological skin by simultaneously displaying color change on damage as well as self healing of the damaged region. Here we report, for the first time, the development of a damage sensing and self healing magnet-polymer composite (Magpol), which actively responds to an external magnetic field. We incorporated reversible sensing using mechanochromic molecules in a shape memory thermoplastic matrix. Exposure to an alternating magnetic field (AMF) triggers shape recovery and facilitates damage repair. Magpol exhibited a linear strain response upto 150% strain and complete recovery after healing. We have demonstrated the use of this concept in a reusable biomedical device i.e., coated guidewires. Our findings offer a new synergistic method to bestow multifunctionality for applications ranging from medical device coatings to adaptive wing structures.

Exploring Wound-Healing Genomic Machinery with a Network-Based Approach

PubMed Central

Vitali, Francesca; Marini, Simone; Balli, Martina; Grosemans, Hanne; Sampaolesi, Maurilio; Lussier, Yves A.; Cusella De Angelis, Maria Gabriella; Bellazzi, Riccardo

2017-01-01

The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties. Lastly, we perform a procedure for in-silico simulation of a treatment action in a biological pathway. The findings obtained by applying the developed pipeline, including gene expression analysis, confirms how a network-based bioinformatics method is able to prioritize candidate genes for in vitro analysis, thus speeding up the understanding of molecular mechanisms and supporting the discovery of potential drug targets. PMID:28635674

Use of dispersion modelling for Environmental Impact Assessment of biological air pollution from composting: Progress, problems and prospects.

PubMed

Douglas, P; Hayes, E T; Williams, W B; Tyrrel, S F; Kinnersley, R P; Walsh, K; O'Driscoll, M; Longhurst, P J; Pollard, S J T; Drew, G H

2017-12-01

With the increase in composting asa sustainable waste management option, biological air pollution (bioaerosols) from composting facilities have become a cause of increasing concern due to their potential health impacts. Estimating community exposure to bioaerosols is problematic due to limitations in current monitoring methods. Atmospheric dispersion modelling can be used to estimate exposure concentrations, however several issues arise from the lack of appropriate bioaerosol data to use as inputs into models, and the complexity of the emission sources at composting facilities. This paper analyses current progress in using dispersion models for bioaerosols, examines the remaining problems and provides recommendations for future prospects in this area. A key finding is the urgent need for guidance for model users to ensure consistent bioaerosol modelling practices. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Memorable Exemplification in Undergraduate Biology: Instructor Strategies and Student Perceptions

NASA Astrophysics Data System (ADS)

Oliveira, Alandeom W.; Bretzlaff, Tiffany; Brown, Adam O.

2018-03-01

The present study examines the exemplification practices of a university biology instructor during a semester-long course. Attention is given specifically to how the instructor approaches memorable exemplification—classroom episodes identified by students as a source of memorable learning experiences. A mixed-method research approach is adopted wherein descriptive statistics is combined with qualitative multimodal analysis of video recordings and survey data. Our findings show that memorable experiencing of examples may depend on a multiplicity of factors, including whether students can relate to the example, how unique and extreme the example is, how much detail is provided, whether the example is enacted rather than told, and whether the example makes students feel sad, surprised, shocked, and/or amused. It is argued that, rather than simply assuming that all examples are equally effective, careful consideration needs be given to how exemplification can serve as an important source of memorable science learning experiences.

Age estimation from canine volumes.

PubMed

De Angelis, Danilo; Gaudio, Daniel; Guercini, Nicola; Cipriani, Filippo; Gibelli, Daniele; Caputi, Sergio; Cattaneo, Cristina

2015-08-01

Techniques for estimation of biological age are constantly evolving and are finding daily application in the forensic radiology field in cases concerning the estimation of the chronological age of a corpse in order to reconstruct the biological profile, or of a living subject, for example in cases of immigration of people without identity papers from a civil registry. The deposition of teeth secondary dentine and consequent decrease of pulp chamber in size are well known as aging phenomena, and they have been applied to the forensic context by the development of age estimation procedures, such as Kvaal-Solheim and Cameriere methods. The present study takes into consideration canines pulp chamber volume related to the entire teeth volume, with the aim of proposing new regression formulae for age estimation using 91 cone beam computerized scans and a freeware open-source software, in order to permit affordable reproducibility of volumes calculation.

Treatability study of pesticide-based industrial wastewater.

PubMed

Shah, Kinnari; Chauhan, L I; Galgale, A D

2012-10-01

This paper finds out appropriate treatment methods for wastewater of an Organophosphorus viz, chloropyrifos pesticide manufacturing industry. The characterization of wastewater generated during trial production of chloropyrifos was carried out. Based on the characterization of wastewater, various treatability studies were conducted. The most desirable results were obtained with treatment scheme employing acidification, chlorination with NaOCl, suspended growth biological treatment, chemical precipitation for phosphorous removal and activated carbon treatment. Acidification of wastewater helps in by-product recovery as well as reduction in COD upto 36.26%. Chlorination followed by biological treatment was found to be effective to reduce the COD level by 62.06%. To comply with permissible limits prescribed by Effluent Channel Project Ltd.(ECPL)* and Gujarat Pollution Control Board (GPCB) for discharge of industrial effluent into channel, further treatment in the form of chemical precipitation (for phosphorous removal) and granular activated carbon is suggested.

Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hepburn, I.; De Schutter, E., E-mail: erik@oist.jp; Theoretical Neurobiology & Neuroengineering, University of Antwerp, Antwerp 2610

Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, which has led to the development of parallel methods that can take advantage of the power of modern supercomputers in recent years. We systematically test suggested components of stochastic reaction-diffusion operator splitting in the literature and discuss their effects on accuracy. We introduce an operator splitting implementation for irregular meshes that enhances accuracy with minimal performance cost. We test a range of models in small-scale MPI simulations from simple diffusion models to realisticmore » biological models and find that multi-dimensional geometry partitioning is an important consideration for optimum performance. We demonstrate performance gains of 1-3 orders of magnitude in the parallel implementation, with peak performance strongly dependent on model specification.« less

Transport properties and efficiency of elastically coupled particles in asymmetric periodic potentials

NASA Astrophysics Data System (ADS)

Igarashi, Akito; Tsukamoto, Shinji

2000-02-01

Biological molecular motors drive unidirectional transport and transduce chemical energy to mechanical work. In order to identify this energy conversion which is a common feature of molecular motors, many workers have studied various physical models, which consist of Brownian particles in spatially periodic potentials. Most of the models are, however, based on "single-particle" dynamics and too simple as models for biological motors, especially for actin-myosin motors, which cause muscle contraction. In this paper, particles coupled by elastic strings in an asymmetric periodic potential are considered as a model for the motors. We investigate the dynamics of the model and calculate the efficiency of energy conversion with the use of molecular dynamical method. In particular, we find that the velocity and efficiency of the elastically coupled particles where the natural length of the springs is incommensurable with the period of the periodic potential are larger than those of the corresponding single particle model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vogt, J R

A total of 75 papers were presented on nuclear methods for analysis of environmental and biological samples. Sessions were devoted to software and mathematical methods; nuclear methods in atmospheric and water research; nuclear and atomic methodology; nuclear methods in biology and medicine; and nuclear methods in energy research.

Integrative network alignment reveals large regions of global network similarity in yeast and human.

PubMed

Kuchaiev, Oleksii; Przulj, Natasa

2011-05-15

High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similarity measures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein-protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7% of proteins in the baker's yeast high-confidence PPI network participate in such a subnetwork that is fully contained in the human high-confidence PPI network. This is the first demonstration that species as diverse as yeast and human contain so large, continuous regions of global network similarity. We apply MI-GRAAL's alignments to predict functions of un-annotated proteins in yeast, human and bacteria validating our predictions in the literature. Furthermore, using network alignment scores for PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship. This is the first time that phylogeny is exactly reconstructed from purely topological alignments of PPI networks. Supplementary files and MI-GRAAL executables: http://bio-nets.doc.ic.ac.uk/MI-GRAAL/.

Quantification of heterogeneity observed in medical images.

PubMed

Brooks, Frank J; Grigsby, Perry W

2013-03-02

There has been much recent interest in the quantification of visually evident heterogeneity within functional grayscale medical images, such as those obtained via magnetic resonance or positron emission tomography. In the case of images of cancerous tumors, variations in grayscale intensity imply variations in crucial tumor biology. Despite these considerable clinical implications, there is as yet no standardized method for measuring the heterogeneity observed via these imaging modalities. In this work, we motivate and derive a statistical measure of image heterogeneity. This statistic measures the distance-dependent average deviation from the smoothest intensity gradation feasible. We show how this statistic may be used to automatically rank images of in vivo human tumors in order of increasing heterogeneity. We test this method against the current practice of ranking images via expert visual inspection. We find that this statistic provides a means of heterogeneity quantification beyond that given by other statistics traditionally used for the same purpose. We demonstrate the effect of tumor shape upon our ranking method and find the method applicable to a wide variety of clinically relevant tumor images. We find that the automated heterogeneity rankings agree very closely with those performed visually by experts. These results indicate that our automated method may be used reliably to rank, in order of increasing heterogeneity, tumor images whether or not object shape is considered to contribute to that heterogeneity. Automated heterogeneity ranking yields objective results which are more consistent than visual rankings. Reducing variability in image interpretation will enable more researchers to better study potential clinical implications of observed tumor heterogeneity.

A Standard Method To Inactivate Bacillus anthracis Spores to Sterility via Gamma Irradiation

PubMed Central

Cote, Christopher K.; Buhr, Tony; Bernhards, Casey B.; Bohmke, Matthew D.; Calm, Alena M.; Esteban-Trexler, Josephine S.; Hunter, Melissa; Katoski, Sarah E.; Kennihan, Neil; Klimko, Christopher P.; Miller, Jeremy A.; Minter, Zachary A.; Pfarr, Jerry W.; Prugh, Amber M.; Quirk, Avery V.; Rivers, Bryan A.; Shea, April A.; Shoe, Jennifer L.; Sickler, Todd M.; Young, Alice A.; Fetterer, David P.; Welkos, Susan L.; McPherson, Derrell; Fountain, Augustus W.

2018-01-01

ABSTRACT In 2015, a laboratory of the United States Department of Defense (DoD) inadvertently shipped preparations of gamma-irradiated spores of Bacillus anthracis that contained live spores. In response, a systematic evidence-based method for preparing, concentrating, irradiating, and verifying the inactivation of spore materials was developed. We demonstrate the consistency of spore preparations across multiple biological replicates and show that two different DoD institutions independently obtained comparable dose-inactivation curves for a monodisperse suspension of B. anthracis spores containing 3 × 1010 CFU. Spore preparations from three different institutions and three strain backgrounds yielded similar decimal reduction (D10) values and irradiation doses required to ensure sterility (DSAL) to the point at which the probability of detecting a viable spore is 10−6. Furthermore, spores of a genetically tagged strain of B. anthracis strain Sterne were used to show that high densities of dead spores suppress the recovery of viable spores. Together, we present an integrated method for preparing, irradiating, and verifying the inactivation of spores of B. anthracis for use as standard reagents for testing and evaluating detection and diagnostic devices and techniques. IMPORTANCE The inadvertent shipment by a U.S. Department of Defense (DoD) laboratory of live Bacillus anthracis (anthrax) spores to U.S. and international destinations revealed the need to standardize inactivation methods for materials derived from biological select agents and toxins (BSAT) and for the development of evidence-based methods to prevent the recurrence of such an event. Following a retrospective analysis of the procedures previously employed to generate inactivated B. anthracis spores, a study was commissioned by the DoD to provide data required to support the production of inactivated spores for the biodefense community. The results of this work are presented in this publication, which details the method by which spores can be prepared, irradiated, and tested, such that the chance of finding residual living spores in any given preparation is 1/1,000,000. These irradiated spores are used to test equipment and methods for the detection of agents of biological warfare and bioterrorism. PMID:29654186