Seeing the Results of a Mutation With a Vertex Weighted Hierarchical Graph

We represent the protein structure of scTIM with a graph-theoretic model. We construct a hierarchical graph with three layers - a top level, a midlevel and a bottom level. The top level graph is a representation of the protein in which its vertices each represent a substructure of the protein. In turn, each substructure of the protein is represented by a graph whose vertices are amino acids. Finally, each amino acid is represented as a graph where the vertices are atoms. We use this representation to model the effects of a mutation on the protein. Methods: There are 19 vertices (substructures) in the top level graph and thus there are 19 distinct graphs at the midlevel. The vertices of each of the 19 graphs at the midlevel represent amino acids. Each amino acid is represented by a graph where the vertices are atoms in the residue structure. All edges are determined by proximity in the protein\u27s 3D structure. The vertices in the bottom level are labelled by the corresponding molecular mass of the atom that it represents. We use graph-theoretic measures that incorporate vertex weights to assign graph based attributes to the amino acid graphs. The attributes of the corresponding amino acids are used as vertex weights for the substructure graphs at the midlevel. Graph-theoretic measures based on vertex weighted graphs are subsequently calculated for each of the midlevel graphs. Finally, the vertices of the top level graph are weighted with attributes of the corresponding substructure graph in the midlevel. Results: We can visualize which mutations are more influential than others by using properties such as vertex size to correspond with an increase or decrease in a graph-theoretic measure. Global graph-theoretic measures such as the number of triangles or the number of spanning trees can change as the result. Hence this method provides a way to visualize these global changes resulting from a small, seemingly inconsequential local change. Conclusions: This modelling method provides a novel approach to the visualization of protein structures and the consequences of amino acid deletions, insertions or substitutions and provides a new way to gain insight on the consequences of diseases caused by genetic mutations

A Predictive Model for Secondary RNA Structure Using Graph Theory and a Neural Network

Background: Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.Results: The network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees.Conclusions: There are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes

Total irredundance in graphs

AbstractA set S of vertices in a graph G is called a total irredundant set if, for each vertex v in G,v or one of its neighbors has no neighbor in S−{v}. We investigate the minimum and maximum cardinalities of maximal total irredundant sets

A predictive model for secondary RNA structure using graph theory and a neural network

Background: Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.Results: The network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees.Conclusions: There are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes

Developing Student Collaborations across Disciplines, Distances, and Institutions

Because quantitative biology requires skills and concepts from a disparate collection of different disciplines, the scientists of the near future will increasingly need to rely on collaborations to produce results. Correspondingly, students in disciplines impacted by quantitative biology will need to be taught how to create and engage in such collaborations. In response to this important curricular need, East Tennessee State University and Georgia Technological University/Emory University cooperated in an unprecedented curricular experiment in which theoretically oriented students at East Tennessee State designed biophysical models that were implemented and tested experimentally by biomedical engineers at the Wallace H. Coulter Department of Biomedical Engineering at Georgia Technological University and Emory University. Implementing the collaborations between two institutions allowed an assessment of the student collaborations from before the groups of students had met for the first time until after they had finished their projects, thus providing insight about the formation and conduct of such collaborations that could not have been obtained otherwise

Geographic Life History Differences Predict Genomic Divergence Better than Mitochondrial Barcodes or Phenotype

Species diversity can be inferred using multiple data types, however, results based on genetic data can be at odds with patterns of phenotypic variation. Tiger beetles of the Cicindelidia politula (LeConte, 1875) species complex have been taxonomically problematic due to extreme phenotypic variation within and between populations. To better understand the biology and taxonomy of this group, we used mtDNA genealogies and multilocus nuclear analyses of 34,921 SNPs to elucidate its evolutionary history and evaluate the validity of phenotypically circumscribed species and subspecies. Genetic analyses recovered two divergent species that are also ecologically distinct, based on adult life history. These patterns are incongruous with the phenotypic variation that informed prior taxonomy, and most subspecies were not supported as distinct evolutionary lineages. One of the nominal subspecies was found to be a cryptic species; consequently, we elevate C. p. laetipennis (Horn, 1913) to a full species. Although nuclear and mtDNA datasets recovered broadly similar evolutionary units, mito-nuclear discordance was more common than expected, being observed between nearly all geographically overlapping taxonomic pairs. Additionally, a pattern of ‘mitochondrial displacement’ was observed, where mitochondria from one species unidirectionally displace others. Overall, we found that geographically associated life history factors better predict genomic divergence than phenotype and mitochondrial genealogies, and consequently taxon identifications based on mtDNA (e.g., DNA barcodes) may be misleading

Single Ablative Fractional Resurfacing Laser Treatment For Forearm Actinic Keratoses: 6-Month Follow-Up Data From An Intrapatient Comparison Between Treated and Untreated Sites

Background and objectives: Actinic keratoses (AK) are common pre-cancerous lesions, which are associated with ultraviolet light exposure and aging. Wounding therapies such as fractionated laser resurfacing (FLR) have been previously demonstrated to effectively treat facial AK. However, the effectiveness of FLR on other sites commonly afflicted with AK has not been studied in detail. Previously, our group has reported that treatment of aged skin with wounding therapies including dermabrasion and ablative fractionated resurfacing results in the removal of senescent fibroblasts and normalizing the pro-carcinogenic acute ultraviolet B radiation responses associated with aged skin. The current studies were designed to test the effectiveness of FLR of the forearm skin of subjects aged 60 and older to remove AKs. Study design/materials and methods: Between February 2018 and March 2019, 30 subjects were enrolled in a study, in which they underwent a single FLR treatment of one extremity including the dorsal forearm, wrist, and dorsal hand. The number of AKs was recorded on both extremities at baseline, 3 and 6 months in a blinded fashion. Side effects of the FLR were documented. Results: A single FLR treatment resulted in a 62% reduction in the absolute number of AK in the treated arm at 6 months post-treatment. The laser treatment was well-tolerated without major complications. Conclusions: These studies demonstrate that FLR using settings, which have demonstrated to remove senescent fibroblasts and normalize the pro-carcinogenic UVB-response of aged skin is a potentially effective and safe field therapy treatment that should be studied for long-term efficacy for use in treating upper extremity AKs