Phylogenetic

Molecular Phylogenetics An introduction to computational methods and tools for analyzing evolutionary relationships Karen Dowell Math 500 Fall 2008 Molecular Phylogenetics Karen Dowell 1 Abstract Molecular phylogenetics applies a combination of molecular and statistical techniques to infer evolutionary relationships among organisms or genes.This review paper provides a general introduction to phylogenetics and phylogenetic trees, describes some of the most common computational methods used to infer phylogenetic information from molecular data, and provides an overview of some of the many different online tools available for phylogenetic analysis. In addition, several phylogenetic case studies are summarized to illustrate how researchers in different biological disciplines are applying molecular phylogenetics in their work. Introduction to Molecular PhylogeneticsThe similarity of biological functions and molecular mechanisms in living organisms strongly suggests that species descended from a common ancestor. Molecular phylogenetics uses the structure and function of molecules and how they change over time to infer these evolutionary relationships. This branch of study emerged in the early 20th century but didn’t begin in earnest until the 1960s, with the advent of protein sequencing, PCR, electrophoresis, and other molecular biology techniques.Over the past 30 years, as computers have become more powerful and more generally accessible, and computer algorithms more sophisticated, researchers have been able to tackle the immensely complicated stochastic and probabilistic problems that define evolution at the molecular level more effectively. Within past decade, this field has been further reenergized and redefined as whole genome sequencing for complex organisms has become faster and less expensive. As mounds of genomic data becomes publically available, molecular phylogenetics is continuing to grow and find new applications. 4, 10, 17, 20, 22] The primary objective of molecular phylogenetic studies is to recover the order of evolutionary events and represent them in evolutionary trees that graphically depict relationships among species or genes over time. This is an extremely complex process, further complicated by the fact that there is no one right way to approach all phylogenetic problems. Phylogenetic data sets can consist of hundreds of different species, each of which may have varying mutation rates and patterns that influence evolutionary change.Consequently, there are numerous different evolutionary models and stochastic methods available. The optimal methods for a phylogenetic analysis depend on the nature of the study and data used. [5, 19, 20] Molecular Evolution: Beyond Darwin Evolution is a process by which the traits of a population change from one generation to another. In On the Origin of Species by Means of Natural Selection, Darwin proposed that, given overwhelming evidence from his extensive comparative analysis of living specimens and fossils, all living organisms descended from a common ancestor.The book’s only illustration (see Figure 1) is a tree-like structure that suggests how slow and successive modifications could lead to the extreme variations seen in species today. [11, 27] Molecular Phylogenetics Karen Dowell 2 Figure 1. Evolution Defined Graphically. The sole illustration in Darwin’s Origin of the Species uses a tree-like structure to describe evolution. This drawing shows ancestors at the limbs and branches of the tree, more recent ancestors at its twigs, and contemporary organisms at its buds. [34] Darwin’s theory of evolution is based on three underlying principles: ariation in traits exist among individuals within a population, these variations can be passed from one generation to the next via inheritance, and that some forms of inherited traits provide individuals a higher chance of survival and reproduction than others. [11] Although Darwin developed his theory of evolution without any knowledge of the molecular basis of life, it has since been determined that evolution is actually a molecular process based on genetic information, encoded in DNA, RNA, and proteins. At a molecular level, evolution is driven by the same types of mechanisms Darwin observed at the species level.One molecule undergoes diversification into many variations. One or more of those variants can be selected to be reproduced or amplified throughout a population over many generations. Such variations at the molecular level can be caused by mutations, such as deletions, insertions, inversions, or substitutions at the nucleotide level, which in turn affect protein structure and biological function. [11, 22] What is a Phylogeny? According to modern evolutionary theory, all organisms on earth have descended from a common ancestor, which means that any set of species, extant or extinct, is related.This relationship is called a phylogeny, and is represented by phyloge netic trees, which graphically represent the evolutionary history related to the species of interest (see Figure 2). Phylogenetics infers trees from observations about existing organisms using morphological, physiological, and molecular characteristics. Figure 2. Phylogeny of Mammalia. This phylogenetic tree shows the evolutionary relationships among six orders of Mammalian species (taxa). Taxa listed in grey are extinct. The â€Å"tree of life† represents a phylogeny of all organisms, living and extinct.Other, more specialized species and molecular phylogenies are used to support comparative studies, test biogeographic hypotheses, evaluate mode and timing of speciation, infer amino acid sequence of extinct proteins, track the evolution of diseases, and even provide evidence in criminal cases. [19] Molecular Phylogenetics Karen Dowell 3 Understanding Phylogenetic Trees Before exploring statistical and bioinformatic methods for estimating phylogenetic trees from molecular data , it’s important to have a basic familiarity of the terms and elements common to these types of trees. See Figure 3. ) Figure 3. Basic elements of a phylogenetic tree. Phylogenetic trees are composed of branches, also known as edges, that connect and terminate at nodes. Branches and nodes can be internal or external (terminal). The terminal nodes at the tips of trees represent operational taxonomic units (OTUs). OTUs correspond to the molecular sequences or taxa (species) from which the tree was inferred. Internal nodes represent the last common ancestor (LCA) to all nodes that arise from that point.Trees can be made of a single gene from many taxa (a species tree) or multi-gene families (gene trees). [1, 10] A tree is considered to be â€Å"rooted† if there is a particular node or outgroup (an external point of reference) from which all OTUs in the tree arises. The root is the oldest point in the tree and the common ancestor of all taxa in the analysis. In the absence of a known outgroup, the root can be placed in the middle of the tree or a rootless tree may be generated. Branches of a tree can be grouped together in different ways. (See Figure 4. ) Figure 4.Groups and associations of taxonomical units in trees. A monophyletic group consists of an internal LCA node and all OTUs arising from it. All members within the group are derived from a common ancestor and have inherited a set of unique common traits. A paraphyletic group excludes some of its descendents (for examples all mammals, except the marsupialia Molecular Phylogenetics Karen Dowell 4 taxa). And a polyphyletic group can be a collection of distantly related OTUs that are associated by a similar characteristic or phenotype, but are not directly descended from a common ancestor. 1, 17] Trees and Homology Evolution is shaped by homology, which refers to any similarity due to common ancestry. Similarly, phylogenetic trees are defined by homologous relationships. Paralogs are homologous s equences separated by a gene duplication event. Orthologs are homologous sequences separated by a speciation event (when one species diverges into two). Homologs can be either paralogs or orthologs. [1, 11, 22] Molecular phylogenetic trees are drawn so that branch length corresponds to amount of evolution (the percent difference in molecular sequences) between nodes. 1, 19] Figure 5. Understanding paralogs and orthologs. Paralogs are created by gene duplication events. (See Figure 5. ) Once a gene has been duplicated, all subsequent species in the phylogeny will inherit both copies of the gene, creating orthologs. Interestingly, evolutionary divergence of different species may result in many variations of a protein, all with similar structures and functions, but with very different amino acid sequences. Phylogenetic studies can trace the origin of such proteins to an ancestral protein family or gene. [1, 22] Figure 6. Mirror Phylogenies.Gene A and Gene A1 are paralogs, whereas all i nstances of Gene A are orthologs of each other in different Canid species. One way to ensure that paralogs and orthologs are appropriately referenced in a phylogenetic tree, and guard against misrepresentation due to missing or incomplete taxonomic information is to generate mirror phylogenies (see Figure 6) in which paralogs serve as each other’s outgroup. [1, 4, 19, 22] Estimating Molecular Phylogenetic Trees Molecular phylogenetic trees are generated from character datasets that provides evolutionary content and context.Character data may consist of biomolecular sequence alignments of DNA, RNA, or amino acids, molecular markers, such as single nucleotide polymorphisms (SNPs) or restriction fragment length polymorphisms (RFLPs), morphology data, or information on gene order and content. Evolution is modeled as a process that changes the state of a character, such as the type of nucleotide (AGTC) at a Molecular Phylogenetics Karen Dowell 5 specific location in a DNA sequence ; each character is a function that maps a set of taxa to distinct states. 1, 19] Note that most of the examples in this paper use DNA sequences as character data, but trees can be accurately estimated from many different types of molecular data. Figure 7. Evolution of a DNA Sequence Figure 7 illustrates how a molecular sequence might evolve over time as a result of multiple mutations that results small, but evolutionarily important changes in a nucleotide sequence. At the protein level, these changes may not initially affect protein structure or function, but over time, they may eventually shape a new purpose for a protein within divergent species. 10, 19, 22] OTUs can be used to build an unrooted phylogenetic tree that clearly depicts a path of evolutionary change. Steps in Phylogenetic Analysis Although the nature and scope of phylogenetic studies may vary significantly and require different datasets and computational methods, the basic steps in any phylogenetic analysis remain t he same: assemble and align a dataset, build (estimate) phylogenetic trees from sequences using computational methods and stochastic models, and statistically test and assess the estimated trees. 4, 19, 20] Assemble and Align Datasets The first step is to identify a protein or DNA sequence of interest and assemble a dataset consisting of other related sequences. For example, to explore relationships among different members of the Notch family of proteins, one might select DNA sequences for Notch1 through Notch4, in different species, such as human, dog, rat, and mouse, then perform a multiple sequence alignment to identify homologies. [1, 10, 13, 19, 20] There are a number of free, online tools available to simplify and streamline this process. DNA sequences of interest can be retrieved using NCBI BLAST or similar search tools.When evaluating a set of related sequences retrieved in a BLAST search, pay close attention to the score and E-value. A high score indicates the subject seque nce retrieved with closely related to the sequence used to initiate the query. The smaller the E-value, the higher the probability that the homology reflects a true evolutionary relationship, as opposed to sequence similarity due to chance. As a general rule, sequences with E-values less than 10-5 are homologs of a query sequence. [10] Once sequences are selected and retrieved, multiple sequence alignment is created.This involves arranging a set of sequences in a matrix to identify regions of homology. Typically, gaps (one or more spaces in the alignment) are introduced in one or more sequences to represent insertions or deletions in the molecular code that may have occurred over time. Effective multiple sequence alignment hinged on gap analysis—determining where to insert gaps and how large to make them. There are many websites and software programs, such as ClustalW, MSA, MAFFT, and T-Coffee, designed to perform multiple sequence on a given set of molecular data. ClustalW i s currently the most mature and most widely used. 1, 10. 19] Molecular Phylogenetics Karen Dowell 6 Building Phylogenetic Trees To build phylogenetic trees, statistical methods are applied to determine the tree topology and calculate the branch lengths that best describe the phylogenetic relationships of the aligned sequences in a dataset. Many different methods for building trees exist and no single method performs well for all types of trees and datasets. The most common computational methods applied include distance-matrix methods, and discrete data methods, such as maximum parsimony and maximum likelihood. 4, 17, 20] There are several software packages, such as Paup*, PAML, PHYLIP, that apply most popular methods. [4] Paup* is a commercially available program that implements a wide variety of methods for phylogenetic inference, including maximum likelihood analysis for DNA data using different models. Paup* also includes a set of exact and heuristic methods for searching optimal trees. PAML (Phylogenetic Analysis by Maximum Likelihood) is open-access set of programs for phylogenetic analysis and evolutionary model comparison.PAML includes many advanced models—DNA- and AAbased models as well as codon-based models that can be used to detect positive selection. Many of the programs in PAML can model heterogeneity of evolutionary rates among sequence sites using ? distributions, and evolutionary dynamics of different sequence regions (concatenated gene sequences). PHYLIP is another large suite of open-access programs for phylogenetic inference that estimates trees using numerous methods, including pairwise distance, maximum parsimony, and maximum likelihood.The maximum likelihood programs can handle a few simple stochastic models and have good tree searching capabilities. PHYLIP is generally considered good educational software for novice phylogeneticists. Distance-Matrix Methods Distance matrix methods compute a matrix of pairwise â€Å"distances† between sequences that approximate evolutionary distance. Distance-based methods tend to be in polynomial time and are quite fast in practice. These methods use clustering techniques to compute evolutionary distances, such as the number of nucleotide or amino acid substitutions between sequences, for all pairs of taxa.They then construct phylogenetic trees using algorithms based on functional relationships among distance values. There are several different distance-matrix methods, including the Unweighted Pair-Group Method with Arithmetic Mean (UPGMA), which uses a sequential clustering algorithm; the Transformed Distance Method, which uses an outgroup as a reference, then applies UPGMA; the Neighbor-Relations Method, which applies 4point condition to adjust the distance matrix, then applies UPGMA; and the Neighbor-Joining Method, which arranges OTUs in a star, the finds neighbors sequentially to minimize total length of tree. 4, 17] The following section on the UPGMA method prov ides a more detailed example of how distance-matrix methods work. UPGMA Method UPGMA produces rooted trees for which the edge lengths can be viewed as times measured by a molecular clock with a constant rate. This method uses a sequential clustering algorithm to identify two OTUs that are most similar (meaning they have the shortest evolutionary distance and are most similar in sequence) and treat them as a single new composite OTU. This process is repeated iteratively until only two OTUs remain.The algorithm defines the distance (d) between two clusters Ci and Cj as the average distance between pairs of sequences from each cluster: Molecular Phylogenetics Karen Dowell 7 Where |Ci| and |Cj| are the number of sequences in clusters i and j. This sequential clustering process is visually described in Figure 8. In this example, the two most homologous sequences are 1 and 2. They are clustered into a new composite parent node (6), and the branch lengths (t1 and t2) are defined as 1/2d1,2 . The next step is to search for the closest pair among remaining sequences and node 6.Pair 4 and 5 are identified and clustered into a new parent node (7), and the branch length for t4 and t5 is calculated. [4, 17] Figure 8. Sequential clustering of sequences using the UPGMA method. [17] In this interactive process, parent node 8 is created from pairs 7 and 3, and parent node 9 is created by clustering nodes 6 and 8. [4, 17] Thus, all sequences are clustered into a single evolutionary tree. The total time (t9) can be calculated as: D6,8 = 1/6 (d1,3 + d1,4 + d1,5 + d2,3 + d2,4 +d2,5)Discrete Data Methods Discrete data methods examine each column of a multiple sequence alignment dataset separately and search for the tree that best represents all this information. Although distance-based methods tend to be much faster than discrete data methods, they typically yield little information beyond the basic tree structure. Discrete data analyses, on the other hand, are information rich. The se methods produce a separate tree for each column in the alignment, so it is possible to trace the evolution for specific elements within a given sequence, such as catalytic sites or regulatory regions. 10, 17, 19, 20) Commonly used discrete data methods include maximum parsimony, which searches for the most parsimonious tree that requires the least number of evolutionary changes to explain differences observed, maximum likelihood, which requires a probabilistic model for the process of nucleotide substitution, and Bayesian MCMC, which also requires a stochastic model of evolution, but creates a probability distribution on a set of trees or aspects of evolutionary history. [17, 19, 20] Discrete data methods are generally considered to produce the best estimates of evolutionary history.However, these methods can be computationally expensive, and it can take weeks or months to obtain a reasonable level of accuracy for moderate to large datasets with 100 or more OTUs. [19] Molecular P hylogenetics Maximum Parsimony Karen Dowell 8 Among the most widely used tree-estimation techniques, maximum parsimony applies a set of algorithms to search for the tree that requires the minimum number of evolutionary changes observed among the OTUs in the study. For example, Figure 9 lists four sample sequences from which phylogenetic trees could be inferred using maximum parsimony.Site Seq 1 2 3 4 1 A A A A 2 A G G G 3 G C A A 4 A C T G 5 G G A A 6 T T T T 7 G G C C 8 C C C C 9 A G A G Figure 9. Sample sequences for a maximum parsimony study [17] Maximum parsimony algorithms identify phylogenetically informative sites, meaning the site favors some trees over others. Consider the sequences in Figure 9: Site 1 is not informative, because all sequences at that site (in column 1) are A (Adenine), and no change in state is required to match any one sequence (1-4) to another.Similarly, Site 2 is not informative because all three trees require one change and there is no reason to favor one tree over another. Site 3 is not informative because all three trees require two changes. (See Figure 10). Figure 10. Site 3 trees all require one evolutionary change. [17] Site 4 is not informative because all three trees require three changes. No one tree can be identified as parsimonious. (See Figure 10 Figure 11. Site 4 trees all require three evolutionary changes. [17] Site 5 is informative because one tree requires only one nucleotide change, whereas the other two trees require 2 changes.In Figure 12, the first tree on the left, which requires only one nucleotide change, is identified as the maximum parsimony tree. Figure 12. Site 5 trees vary in the number of evolutionary changes required. [17] Molecular Phylogenetics Maximum Likelihood Karen Dowell 9 The maximum likelihood method requires a probabalistic model of evolution for estimating nucleotide substitution. This method evaluates competing hypotheses (trees and parameters) by selecting those with the highest likeliho od, meaning those that render the observed data most plausible. The ikelihood of a hypothesis is defined as the probability of the data given that hypothesis. In phylogeny reconstruction, the hypotheses are the evolutionary tree (its topology and branch lengths) and any other parameters of the evolutionary model. [17, 20] The likelihood calculations required for evolutionary trees are far from straightforward and usually require complex computations that must allow for all possible unobserved sequences at the LCA nodes of hypothesized trees. This method specifies the transition probability from one nucleotide state to another in a time interval in each branch.For example, for a one-parameter model with rate of substitution ? per site per unit time, the probability that the nucleotide at time t is i is: The probability that the nucleotide at time t is j is: To set up a likelihood function, given x as the ancestral node and y and z as internal nodes, the probability of observing nucle otides i, j, k, l at the tips of the tree is computed as: Pxl(t1+t2+t3)Pxy(t1)Pyk(t2+t3)Pyz(t2)Pzi(t3)Pzj(t3) For the ancestral node (root) x, the probability of having nucleotide l in sequence 4 is calculated as: Pxl(t1+t2+t3)Because x, y, and z can be any one of four nucleotides (ACGT), it is necessary to sum over all possibilities to obtain the probability of observing the configuration of nucleotides i, j, k, l, in sequences 1, 2, 3, 4, for a given hypothetical tree (see Figure 13. ). This likelihood probability is calculated as: h(I,j,k,l)= [? gxPxl(t1+t2+t3)] [? Pxy(t1)Pyk(t2+t3)] [? Pyz(t2)Pzi(t3) Pzj(t3)] The appropriate likelihood function depends on the hypothetical tree and the evolutionary model used. (See Figure 13. ) [17] Figure 13. Different types of model trees for the derivation of the maximum likelihood function. 17] Molecular Phylogenetics Stochastic Models of Evolution Karen Dowell 10 Evolutionary changes in molecular sequences result from mutations, some of whic h occur by chance, others by natural selection. Rates of change can also differ among OTUs, depending on several factors ranging from GC content to genome size. To accurately estimate phylogenetic trees, assumptions must be made about the substitution process and those assumptions must be stated in the form of a stochastic evolutionary model. These probabilistic models are used to rank trees according to likelihood: P(data|tree).From a Bayesian perspective, they rank trees according to a posterior probability: P(tree|data). [17, 20] The objective of probabilistic models is to find likelihood or posterior probability of a particular taxonomic feature, then define and compute: P(x? |T,t ? ) Where x ? is xj for j=1†¦n, T is a tree with n leaves with sequence j at leaf j, and t ? are tree edge lengths. [17] A few popular stochastic models of evolution include the single parameter Jukes-Cantor (JC) method, Kimura 2-parameter (K2P), Hasegawa-Kishino-Yano (HKY), and Equal-Input.Some s oftware programs, such as Paup*, will automatically use a default model for the tree estimation method chosen. The JC method is the easiest one to comprehend, because it assumes that if a site changes its state, it changes with equal probability to the other states. This is not very realistic, however, as some sites are known to evolve more rapidly than others, and some sites may be invariable and not allowed to change at all. Determining how best to select the appropriate model is a topic of another paper (or papers) as there is no one model that incorporates all mutation rules and patterns across different species and macromolecules. 4, 17, 20] Hidden Markov Models Profile hidden Markov models (HMMs) are a form of Bayesian network that provides statistical models of the consensus structure of a sequence family. Gary Churchill at The Jackson Lab was the first evolutionary geneticist to propose using profile HMMs to model rates of evolution. Many software packages and web services n ow apply HMMs to estimate phylogenetic relationships. [8] In the HMM format, each position in the model corresponds to a site in the sequence alignment. For each position, there are a number of possible states, each of which corresponds to a different rate of evolution.In addition, transitions between all possible rate-states at adjacent positions. Transition probabilities capture any tendency for patterns of rates to occur in successive sites. [2, 4] Assessing Trees Tree estimating algorithms generate one or more optimal trees. This set of possible trees is subjected to a series of statistical tests to evaluate whether one tree is better than another – and if the proposed phylogeny is reasonable. Common methods for assessing trees include the Bootstrap and Jackknife Resampling methods, and analytical methods, such as parsimony, distance, and likelihood.To illustrate how these methods are used, consider the steps involved in a bootstrap analysis. Bootstrap Analysis A bootstra p is a statistical method for assessing trees that takes its name from the fact that it can â€Å"pull itself up by its bootstraps† and generate meaningful statistical distributions from almost nothing. Using bootstrap analysis, distributions that would otherwise be difficult to calculate exactly are estimated by repeated creation and analysis of artificial datasets. In a Non-parametric bootstrap, artificial datasets Molecular Phylogenetics Karen Dowell 11 generated by resampling from original data.In a parametric bootstrap, data is simulated according to hypothesis tested. The objective of any bootstrap analysis is to test whether the whole dataset supports the tree. [1, 4, 17] Figure 14 illustrates the basic steps in any bootstrap analysis. Sample datasets are automatically generated from an original dataset. Trees are then estimated from each sample dataset. The results are compiled and compared to determine a bootstrap consensus tree. Figure 14. Steps in a phylogenetic tr ee bootstrap analysis. [1] Phylogenetic Analysis Tools There are several good online tools and databases that can be used for phylogenetic analysis.These include PANTHER, P-Pod, PFam, TreeFam, and the PhyloFacts structural phylogenomic encyclopedia. Each of these databases uses different algorithms and draws on different sources for sequence information, and therefore the trees estimated by PANTHER, for example, may differ significantly from those generated by P-Pod or PFam. As with all bioinformatics tools of this type, it is important to test different methods, compare the results, then determine which database works best (according to consensus results, not researcher bias) for studies involving different types of datasets.In addition, to the phylogenetic programs already mentioned in this paper, a comprehensive list of more than 350 software packages, web-services, and other resources can be found here: http://evolution. genetics. washington. edu/phylip/software. html. PANTHER ( pantherdb. org) Protein ANalysis Through Evolutionary Relationships, known by its acronym PANTHER, is a library of protein families and subfamilies indexed by function. Panther version 6. 1 contains 5547 protein families. Molecular Phylogenetics Karen Dowell 12It categorizes proteins by evolutionary related proteins (families) and related proteins with same function (subfamilies). [8, 21, 26] PANTHER is composed of both a library and index. The library is a collection of â€Å"books† that represent a protein family as a collection of multiple sequence alignments, HMMs, and a family phylogenetic tree. Functional divergence within the tree is represented by dividing the parent tree into child trees and HMMs based on shared functions. These subfamilies enable database curators to more accurately capture functional divergence of protein sequences as inferred from genomic DNA. 25, 26] PANTHER database entries are annotated to molecular function, biological process and pathway with a proprietary PANTHER/X ontology system, which is supposed to be easier to understand than the more global standard Gene Ontology (GO). Database entries in PANTHER are generated through clustering of UniProt database using a BLAST-based similarity score. Trees are automatically generated based on multiple sequence alignments and parameters of the protein family HMMs using the Tree Inferred from Profile Score (TIPS) clustering algorithm.Scientific curators review all family trees, annotate each tree, and determine how best to divide them into subtrees using a tree-attribute viewer that tabulates annotations for sequences in a tree. In addition, trees and subfamilies are manually cross-checked and validated by curators. [25, 26] P-POD (ortholog. princeton. edu) The Princeton Protein Orthology Database (P-POD) combines results from multiple comparative methods with curated information culled from the literature.Designed to be a resource for experimental biologists seeking evolutionary information on genes on interest, P-POD employs a modular architecture, based on their Generic Model Organism Database (GMOD). P-POD can be accessed from their web service or downloaded to run on local computer systems. [12] P-POD accepts FASTA-formatted protein sequences as input, and performs comparative genomic analyses on those sequences using OrthoMCL and Jaccard clustering methods. The P-POD database contains both phylogenetic information and manually curated experimental results.The site also provides many links to sites rich in human disease and gene information. This tool may be particularly helpful for bioinformaticists and statisticians developing comparative genomic database tools and resources. Pfam (pfam. sanger. ac. uk/) PFam is a collection of protein families represented by multiple sequence alignments and HMMs. It contains models of protein clans, families, domains, and motifs, and uses HMMs representing conserved functional and structural domains. It is a large, widely used, actively curated mature database that has been available online since 1995.Pfam can be used to retrieve the domain architectures for a specific protein by conducting a search using a protein sequence against the Pfam library of HMMs. This database is also helpful for proteomes and protein domain architecture analysis. [6, 8, 24] There are two versions of the Pfam database: Pfam–B is generated automatically from ProDom, using PsiBLAST, an open access bioinformatics tool available through NCBI for identifying weak, but biologically relevant sequence similarities. Pfam-A is hand-curated from custom multiple sequence alignments. Pfam protein domain families are clustered with Mkdom2, and aligned with ProDomAlign.ProDom is a comprehensive set of protein domain families automatically generated from the SWISSPROT and TrEMBL sequence databases. Mkdom2 is a ProDom program used to make ProDom family clusters. Protein domain families in ProDom were aligned using an improved parallelized program called Molecular Phylogenetics Karen Dowell 13 ProDomAlign, developed in C++ using OpenMP. ProDomAlign is based on MultAlign, a program well suited for aligning very large sequence families with thousands of associated sequences. As of early 2008, Pfam matched 72 percent of known proteins sequences, and 95 percent of proteins for which there is a known structure.Within the Pfam database, 75 percent of sequences will have one match to Pfam-A, 19 percent to Pfam-B. There are also two versions of Pfam-A and Pfam-B. Pfam-ls handles global alignments, and Pfam-fs is optimized for local alignments. Interestingly, Pfam entries can be classified as â€Å"unknown,† but that doesn’t mean the protein is undocumented. Unknown entries can be proteins for which some information is known, but it has not been fully researched or cannot be adequately annotated. For example, Pfam entry PFO1816 is a LeucineRich Repeat Variant (LRV), which has a known structure (1LRV ) available in the Protein Databank (pdb. rg). LRV repeat regions, which are found in many different proteins, are often involved in cell adhesion, DNA repair, and hormone reception—but identification of an LRV within a sequence encoding a protein doesn’t specifically reveal the protein’s function. For studies involving a large number of protein searches, it may be more convenient to run Pfam locally on a client machine. The standalone Pfam system requires the HMMER2 software, the Pfam HMM libraries and a couple of additional files from the Pfam website to be installed on the client machine. HMMER is a freely distributable implementation of profile HMM software for protein sequence analysis. ) Once the initial search is complete, researchers can go to the Pfam website to further analyze select number of sequences using additional features on website. [6, 8, 24] TreeFam (TreeFam. org) TreeFam is a curated database of phylogenetic trees and orthology predictions f or all animal gene families that focuses on gene sets from animals with completely sequenced genomes. Orthologs and paralogs are inferred from phylogenetic tree of gene family.Release 4 contains curated trees for 1314 families and automatically generated trees for another 14351 families. [16, 23] Like Pfam, TreeFam is a two-part database: TreeFam-B contains automatically generated trees, and TreeFam-A consists of manually curated trees. To automatically generate trees, an algorithm selects clusters of genes to create TreeFam-B â€Å"seeds† from core species with high-quality reference genome sequences, first using BLAST to rapidly assemble an initial list of possible matches, then HMMER to expand and filter probable sequence matches for each TreeFam B seed family.The filtered alignment is fed into a neighbor-joining algorithm and a tree is constructed based on amino acid mismatch distances. For TreeFam version 4, the most current release, five â€Å"clean† family trees were built for each TreeFam B seed, two using a maximum likelihood tree generated using PHYML (one based on the protein alignment, the other on codon alignment), three using a neighbor joining tree, using different distance measurements based on codon alignments. 16, 23] Scientific curators then manually any correct errors (based on information in the literature) in automatically generated TreeFam-B trees. Curated TreeFam-B trees then become seeds for TreeFam-A trees. Clean TreeFam-A trees are build using three merging algorithms and bootstrapping to find the consensus tree of seven trees: two constrained maximum likelihood trees based on protein and codon alignment, and five unconstrained neighbor-joining trees generated using different distance measurements based on codon alignments.For both TreeFam-B and TreeFam-A families, orthologs and paralogs are inferred only from clean trees using Duplication/Loss Inference (DLI) algorithm that requires a species tree (NCBI taxonomy tree). [16, 23] Molecular Phylogenetics PhyloFacts (phylogenomics. berkeley. edu/phylofacts) Karen Dowell 14 PhyloFacts is an online phylogenomic encyclopedia for protein functional and structural classification. It contains more than 57,000 â€Å"books† for protein superfamilies and structural domains.Each book contains heterogenous data for protein families, including multiple sequence alignments, one or more phylogenetic trees, predicted 3-D protein structures, predicted functional subfamilies, taxonomic distributions, GO annotations, and PFAM domains. HMMs constructed for each family and subfamily permit novel sequences to be classified to different functional classes. [14] Unlike other databases mentioned in this paper, PhyloFacts seeks to correct and clarify annotation errors associated with computational methods for predicting protein function based on sequence homology.It uses a consensus approach that integrates many different prediction methods and sources of experimental data over an evolutionary tree. By applying evolutionary and structural clustering of proteins, PhyloFacts is able to analyze disparate datasets using multiple methods, identify potential errors in database annotations, and provide a mechanism for improving the accuracy of functional annotation in general. [14] PhyloFacts can be used to search for protein structure prediction or functional classification for a particular protein sequence.Researchers may also browse through protein family books and multiple sequence alignments, phylogenetic trees, HMMs and other pertinent information for proteins of interest. This webservice also provides many links to literature and other information sources. [14] Applied Molecular Phylogenetics Molecular phylogenetic studies have many diverse applications. As the amount of publically available molecular sequence data grows and methods for modeling evolution become more sophisticated and accessible, more and more biologists are incorporating phylog enetic analyses into their research trategy. Here’s a sampling of how molecular phylogenetics might be applied. Tracing the evolution of man In one case study, molecular phylogenetic techniques were used to compare and analyze variation in DNA sequences using modern human and Neanderthal mitochondrial DNA (mtDNA). For this study, 206 modern human mtDNAs and parts of two Neanderthal mtDNAs sequences derived from skeletal remains were used to generate an initial dataset. Genetic distance was first estimated using the Jukes-Cantor single parameter model.Then the Kimura 2-Parameter model was used to distinguish between transition (replacement of one purine with another purine or one pyrimidine with another pyrimidine) and transversion (replacement of one purine with a pyrimidine or vice versa) probabilities with Kimura 2parameter model. A phylogenetic tree representing primate evolution was generated using pairwise genetic distances between primate Hypervariable regions I and II of mtDNA. [3] Chasing an epidemic: SARS Using publically available genomic data, it is possible to reconstruct the progression of the SARS epidemic over time and geographically.To conduct this phylogenetic analysis, researchers used the neighborjoining method to construct a phylogenetic tree of spike proteins in various coronaviruses and identify the viral host (a Himalyan palm civet). They then obtained 13 SARs genome sequences with documented information on the date and location of the sample. The neighbor-joining method and a distance matrix based on Jukes-Cantor model, were used to generate an epidemic tree, from which it was possible to identify the origin (date and location) of the virus by observing progression of mutations over time. 3] Molecular Phylogenetics Barking up the right tree Karen Dowell 15 Phylogenetics is increasingly incorporated into biological and biomedical research papers. When the canine genome was published, researchers used sequence data to estimate a co mprehensive phylogeny of the canid family. Figure 15. Phylogenetic Tree of the Canid family This canid family phylogenetic tree is based on 15 kb of exon and intron sequence. It was constructed using the maximum parsimony method and represents the single most parsimonious tree.A good example of how phylogenies are referenced in the literature, this tree includes bootstrap values and Bayesian posterior probability values listed above and below internodes, respectively. Dashes indicate bootstrap values below 50%. In addition, divergence time in millions of years (Myr) is indicated for three nodes. [18] Seeing the Forest from the Trees Molecular phylogenetics is a broad, diverse field with many applications, supported by multiple computational and statistical methods. The sheer volumes of genomic data currently available (and rapidly growing) render molecular phylogenetics a key component of much biological research.Genome-scale studies on gene content, conserved gene order, gene expre ssion, regulatory networks, metabolic pathways, functional genome annotation can all be enriched by evolutionary studies based on phylogenetic statistical analyses. [19, 25 27] Molecular phylogenies have fast become an integral part of biological research, pharmaceutical drug design, and bioinformatics techniques for protein structure prediction and multiple sequence alignment. Although not all molecular biologists and bioinformaticians may be familiar with the techniques describedMolecular Phylogenetics Karen Dowell 16 in this paper, this is a rapidly growing and expanding field and there is ongoing need for novel algorithms to solve complex phylogeny reconstruction problems. References 1. Baldauf, SL (2003) â€Å"Phylogeny for the faint of heart: a tutorial. † Trends in Genetics, 19(6):345-351. 2. Brown, D, K Sjolander (2006) â€Å"Functional Classification Using Phylogenomic Inference. † PLos Computational Biology, 2(6):0479-0483. 3. Cristianini, N, and M Hahn (2007 ) Introduction to Computational Genomics: A Case Studies Approach.Cambridge University Press: Cambridge. 4. Durbin, R, S Eddy, A Krogh, G Mitchison (1998) Biological Sequence Analysis. Cambridge University Press: Cambridge. 5. Ewens, WJ, R Grant (2005) Statistical Methods in Bioinformatics. Springer Science and Business Media: New York. 6. Finn, RD, J Tate, J Mistry, PC Coggill, SJ Sammut, HR Hotz, G Ceric, K Forslund, SR Eddy, ELL Sonnhammer, A Bateman (2008) â€Å"The Pfam protein families database. † Nucleic Acids Research, 36:D281288. 7. Gabaldon, T (2008) â€Å"Large-scale assignment of orthology: back to phylogenetics? Genome Biology, 9:235. 1-235. 6. 8. Gollery, M. (2008) Handbook of Hidden Markov Models in Bioinformatics. CRC Press, Taylor & Francis Group: London. 9. Goodstadt, L, CP Ponting (2006) â€Å"Phylogenetic Reconstruction of Orthology, Paralogy, and Conserved Synteny for Dog and Human. † PLoS Computational Biology, 2(9):1134-1150. 10. Hall, BG. (2004 ) Phylogenetic Trees Made Easy: A How-To Manual, 2nd ed. Sinauer Associates, Inc. : Sunderland, MA. 11. Hartwell, LH, L Hood, ML Goldberg, AE Reynolds, LM Silver, RC Veres (2008) Genetics: From Genes to Genomes, 3rd Ed.McGraw-Hill: New York. 12. Heinicke, S, MS Livstone, C Lu, R Oughtred, F Kang, SV Angiuoli, O White, D Botstein, K Dolinski (2007) â€Å"The Princeton Protein Orthology Database (P-POD): A Comparative Genomics Analysis Tool for Biologists. † PLoS ONE, 8:e766. 1-15. 13. Kortschak, RD, R Tamme (2001) â€Å"Evolutionary analysis of vertebrate Notch genes. † Dev Genes Evol, 211:350-354. 14. Krishnamurthy, N, DP Brown, D Kirshner, K Sjolander (2006) â€Å"PhyloFacts: an online structural phylogenomic encyclopedia for protein functional and structural classification. † Genome Biology, 7:R83. -13. 15. Kuzniar, A, RCHJ van Ham, S Pongor, JAM Leunissen (2008) â€Å"The quest for orthologs: finding the corresponding gene across genomes. † Trends in G enetics, 24(11):539-551. Molecular Phylogenetics Karen Dowell 17 16. Li, H, A Coghlan, J Ruan, LJ Coin, JK Heriche, L Osmotherly, R Li, T Liu, Z Zhang, L Bolund, GKS Wong, W Zheng, P Dehal, J Wang, R Durbin (2006) â€Å"TreeFam: a curated database of phylgenetic trees of animal gene families. † Nucleic Acids Research, 34:D573-580. 17. Li, WH (1997) Molecular Evolution. Sinauer Associates: Sunderland, MA. 18.Lindblad-Toh, K, CM Wade, TS Mikkelsen, EK Karlsson, DB Jaffe, M Kamal, M Clamp, JL Chang, EJ Kulbokas III, MC Zody, E Mauceli, X Xie, M Breen, RK Wayne, EA Ostrander, CP Ponting, F Galibert, DR Smith, PJ deJong, E Kirkness, P Alvarez, T Biagi, W Brockman, J Butler, C Chin, A Cook, J Cuff, MJ Daly, D DeCaprio, S Gnerre, M Grabherr, M Kellis, M Kleber, C Bardeleben, L Goodstadt, A Heger, C Hitte, L Kim, KP Koepfli, HG Parker, JP Pollinger, SMJ Searle, NB Sutter, R Thomas, C Webber, ES Lander (2005) â€Å"Genome Sequence, Comparative Analysis and Haplotype Structure of the Domestic Dog.Nature, 438:803-819. 19. Linder, CR, T Warnow (2005) â€Å"An overview of phylogeny reconstruction. † In the Handbook of Computational Molecular Biology, Chapman and Hall/CRC Computer & Information Science. 20. Lio, P, N Goldman (1998) â€Å"Models of Molecular Evolution and Phylogeny. † Genome Research, 8:12331244. 21. Mi, H, N Guo, A Kejariwal, PD Thomas (2007) â€Å"PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Research, 35:D247-252. 22. Patthy, Laszlo. (1999) Protein Evolution. Blackwell Science, Ltd: Malden, MA. 23. Ruan, J, H Li Z Chen, A Coghlan, LJM Coin, Y Guo, JK Heriche, Y Hu, K Kristiansen, R Li, T Liu, A Mose, J Qin, S Vang, AJ Vilella, A Ureta-Vidal, L Bolund, J Wang, R Durbin (2008) â€Å"TreeFam: 2008 Update. † Nucleic Acids Research, 36:D735-740. 24. Sammut, SJ, RD Finn, A Bateman (2008) â€Å"Pfam 10 years on: 10000 families and still growing. â €  Briefings in Bioinformatics, 9(3):210-219. 5. Thomas, PD, A Kejariwal, N Guo, H Mi, MJ Campbell, A Muruganujan, B Lazareva-Ulitsky (2006) â€Å"Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools. † Nucleic Acids Research, 34:W645-650. 26. Thomas, PD, MJ Campbell, A Kejariwal, H Mi, B Karlak, R Daverman, K Diemer, A Muruganujan, A Narechania. â€Å"PANTHER: A Library of Protein Families and Subfamilies Indexed by Function. † Genome Research, 13:2129-2141. 27.Warnow, T (2004) â€Å"Computational Methods in Phylogenetics† Computational Systems Biology Conference, Stanford, CA 28. Whelan, S, P Lio, N Goldman (2001) â€Å"Molecular phylogenetics: state of the art methods for looking into the past. † Trends in Genetics, 17(5):262-272. Molecular Phylogenetics Karen Dowell 18 Appendix Website Resources Phylogeny Programs. A University of Washington site formerly supported by the National Science Foundation. http://www. evolution. genetics. washington. edu/phylip/software. tml TreeFam Tree Families Database. http://wwww. treefam. org Protein Analysis Through Evolutionary Relationships (PANTHER) Classification System. http://www. pantherdb. org. 29. Pfam Database of Protein Families. http://pfam. sanger. ac. uk 30. Princeton Protein Orthology Database (P-POD). http://ppod. princeton. edu 31. Wikipedia. http://en. wikipedia. org/wiki/Tree_of_life(science) Cover Page The cover image is from a phylogeny of canid species that appeared in Lindblad-Toh et al, 2005. [18]

Enzyme Structure and Functions:

ENZYME STRUCTURE AND FUNCTIONS: Enzymes are biological catalysts. They increase the rate of reactions by a factor of between 106 to 1012 times, allowing the chemical reactions that make life possible to take place at normal temperatures Definition of enzyme: A protein with catalytic properties due to its power of specific activation is defined as an enzyme. STRUCTURE Enzymes are proteins their function depends on its complexity. The reaction takes place in a small part of the enzyme called the active site, while the rest of the protein acts as â€Å"scaffolding†.The shape and the chemical environment inside the active site permits a chemical reaction to proceed more easily Many enzymes need cofactors (or coenzymes) to work properly. Tightly bound cofactors are called prosthetic groups Cofactors that are bound and released easily are called coenzymes These can be metal ions (such as Fe2+, Mg2+, Cu2+) or organic molecules (such as haem, biotin, FAD, NAD or coenzyme A). Many of t hese are derived from dietary vitamins, which is why they are so important. The complete active enzyme with its cofactor is called a holoenzyme, while just the protein part without its cofactor is called the apoenzyme.HW DOES AN ENZYME WORK? 1) REACTION MECHANISM 2) MOLECULAR GEOMETRY REACTION MECHANISM: In any chemical reaction, a substrate (S) is converted into a product (P) In an enzyme-catalysed reaction, the substrate first binds to the active site of the enzyme to form an enzyme-substrate (ES) complex, then the substrate is converted into product whilst attached to the enzyme, and finally the product is released, thus allowing the enzyme to start all over again An example is the action of the enzyme sucrase hydrolysing sucrose into glucose and fructose.MOLECULAR GEOMETRY The substrate molecule is complementary in shape to that of the active site. It was thought that the substrate exactly fitted into the active site of the enzyme molecule like a key fitting into a lock (the now discredited ‘lock and key’ theory). This explains enzyme specificity This explains the loss of activity when enzymes denature The Induced Fit Hypothesis  : * Some proteins can change their shape (conformation) When a substrate combines with an enzyme, it induces a change in the enzyme’s conformation * The active site is then moulded into a precise conformation * Making the chemical environment suitable for the reaction * The bonds of the substrate are stretched to make the reaction easier (lowers activation energy) ENERGY CHANGES  : Energy needed for initial reaction is known as ACTIVATION ENERGY. The larger the activation energy is, the slower the reaction will be.This is because only a few substrate molecules will have sufficient energy to overcome the activation energy barrier. Enzymes reduce the activation energy of a reaction so that the kinetic energy of most molecules exceeds the activation energy required and so they can react. Factors affecting Enzy mes substrate concentration pH temperature enzyme concentration inhibitors SUBSTARTE CONCENTRATION The rate of an enzyme-catalysed reaction is also affected by substrate concentration.As the substrate concentration increases, the rate increases because more substrate molecules can collide with active sites, so more enzyme-substrate complexes form. At higher concentrations the enzyme molecules become saturated with substrate, and there are few free active sites, so adding more substrate doesn't make much difference The maximum rate at infinite substrate concentration is called vmax, and the substrate concentration that gives a rate of half vmax is called KM.These quantities are useful for characterising an enzyme. A good enzyme has a high vmax and a low KM. pH Enzymes have an optimum pH at which they work fastest. For most enzymes this is about pH 7-8 (normal body pH), but a few enzymes can work at extreme pH. The pH affects the charge of the amino acids at the active site, so the pr operties of the active site change and the substrate can no longer bind. TEMPERATURE: Enzymes have an optimum temperature at which they work fastest.For mammalian enzymes this is about 40 °C. Up to the optimum temperature the rate increases geometrically with temperature. Above the optimum temperature the rate decreases as more of the enzyme molecules denature. The thermal energy breaks the hydrogen bonds holding the secondary and tertiary structure of the enzyme together, so the enzyme loses its shape Q10 (the temperature coefficient) = the increase in reaction rate with a 10 °C rise in temperature. ENZYME CONCENTRATIONAs the enzyme concentration increases the rate of the reaction also increases, because there are more enzyme molecules (and so more active sites), available to catalyse the reaction therefore more enzyme-substrate complexes form INHIBITORS Inhibitors inhibit the activity of enzymes, reducing the rate of their reactions. 2 TYPES: Competitive and non competitive CO MPETITIVE: A competitive inhibitor molecule has a similar structure to the substrate molecule, and so it can fit into the active site of the enzyme. It therefore competes with the substrate for the active site, so the reaction is slower.Increasing the concentration of substrate restores the reaction rate and the inhibition is usually temporary and reversible. NON COMPETITIVE: A non-competitive inhibitor molecule is quite different in structure from the substrate and does not fit into the active site. It binds to another part of the enzyme molecule, changing the shape of the whole enzyme, including the active site, so that it can no longer bind substrate molecules. Non-competitive inhibitors therefore simply reduce the amount of active enzyme.

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Warfare in Medieval Japan - Essay Example During the Nara period, conscription policy did not distinguish between a warrior and a peasant, which resulted in an army that suffered from poor organization because of the presence of unwilling farmers and government failure to devise regular system of promotion or command. Prior to 789 reforms in Japan’s army system with new regulations that necessitated the use of trained warriors together with men specially selected for their strength and prowess, imperial army was humiliated in 724. The year saw Yemeshi people revolt killing the governor of the province of Mutsu and seizing much of Japan’s mainland. Military power was the province of samurai families with two clans comprising many warrior households became the main security of the Imperial and the clans were Minamoto and Taira (Ratty and Westbrook 80). Another clan, the Fujiwara at the time dominated both Imperial court and the nation, grew its impact with soft life and intricate politics in the capital, which co ntained the seeds of disaster (Brinkley 4). However, the Fujiwara experienced their its first danger in 930 when the Fujiwara attempted the customary maneuver of putting a child in throne, a son eight years old named Shujaku, since the clan had selected Tadahira for the role (Kane 48). During Tadhira’s rule, Taira Masakado a warrior in Japan aspired to be a governor in his native province; however, Tadhira appointed his companion in a debauch manner, a man named Sadabumi. This made Masakado return to his home in disgust and even though he did not instantly get involved in a revolt, he was involved in family feuds. From his actions, Masakado became an outlaw to the Imperial court and instead of submitting to punishment of rulers that he detested, he raised a revolt. In 1159 rivalry in Fujiwara family created another upheaval with a dispute that concerned imperial succession with emperor Go-shirakawa abdicating in favor of his son and Fujiwara Shinzei supporting the ex-emperor. In an open battle, once the Taira household assembled its army the households met in the battle where Taira and Shinzei won a stupendous victory. Thus, the Fujiwara no longer ruled by their power and Taira Kiyomori, a man whose martial skills twice shaped the nation’s future, took advantage of the situation and crushed several conspiracies against his life (Kane 49). The Gen and Pei war that ended in 1183 with a naval battle at Dan-no-ura, saw Minamoto forces’ 800 ships attack Taira’s fleet of 500 in a battle that involved gaining the upper hand. When Minamooto secured defection of Taira admiral Tomomori, the Taira suffered total defeat as their samurai struggled to protect the ship carrying the infant emperor (Van Bergen 41). Emperor Antoku took the throne and the victorious Yoritomi paid long visits to the imperial capital seeking the office of Sei-i-tai-Shogun, the supreme military leader of the country, which he later gained. Though in the past emperors app ointed military leaders for brief periods in order to see the nation through certain crises, Minamoto Yoritomi ensured the office became permanent (Kane 50). Hideyoshi managed to impose control as well as peace and government, which partly lay in his diplomatic skills. Moreover, Hidoyeshi fought several fights campaigns against internal enemies, which ended not in military victories but with treaties in which rival clans received an active stake in the fresh Japanese order. Hideyoshi success lay in the ruthless

A Research Proposal on the Role of the Chief Executive Officer Essay Example for Free

A Research Proposal on the Role of the Chief Executive Officer Essay Questions One of the major problems in business is the notion of whether companies should be concerned with other issues than profitability. Adam Smith in 1863 claimed that the process of achieving the overall good for the society is something that will happen inevitably happen because of his idea of the invisible hand of the market. However, more contemporary ideas assert otherwise as they believed that there are a number of conditions that would hinder the invisible hand to work effectively (Mohr and Webb, 2002). The concept of Corporate Social Responsibility (CSR) is deeply rooted on the commitment of organizations to continue their business in an ethical manner. It is in this respect that organizations are said to necessarily contribute to the overall economic development of ones country while at the same time improving the quality of life of not only its employees and its families but also the society where it belonged (Watts and Holme, 1999). One of the central concerns with regard to the necessity of the implementation of CSR by a particular organization is the impact of a particular company’s decisions and actions within the society together with their responsibility in the aforementioned. As such this means that when aligning certain organizational goals or projects, it would be better if organizations will evaluate first their actions and make sure that they are in accordance to the welfare of the greater good (Parsons, 1954). As such, the impact of an organizations decision within the society is very vital in CSR. It should be emphasized as well that an organizations duty should span more than the economic and legal aspects but also assume the good of the majority. Archie Carroll said that an organizations social responsibility is something that includes the interplay of four important factors. These are economic performance, adherence with the law, ethical responsibility, good corporate citizenship, and improving the societys quality of life (Carrol and Buchholtz, 2003). However despite companies claim for CSR implementation, a significant number of evidence tells that every year, there are numerous companies that are charged for violating environmental laws (Kassinis and Panayiotou, 2006, p. 68). Problems The success of a company’s CSR and the its effect on the company’s image has been viewed by a number of studies to be directly correlated on the role of the Chief Executive Officer (CEO) (PR News, 2007). It has been said by Kassinis and Panayiotou (2006) that the role of the CEO is very vital since they are the ones who are primarily responsible for the boards decision-management functions and the even the extent of corporate wrongdoing. The interpretation of the CEO in terms of various environmental issues that could have affect their firms and its choice of environmental strategies have also a significant implication on the overall image and performance of a particular company. Studies such as those conducted by PR News Wire in 2008 claimed that to belong to Fortune’s Worlds Most Admired Companies, the CEOs role together with his or her capacity to create a strategy or hire specific experts who can effectively handle CSR concerns, such as hiring a competitive Chief Communications Officer (CCO,) is very vital. Companies which belong to Fortunes annual awards are often evaluated based on their reputation. According to PR News in 2007, it is often the case that CEOs are the ones who are held accountable in terms of the failure to protect the company image in whenever a crisis arises. The study of PR News revealed that in out of 950 global business executives in 11 countries, 68% of the results attributed unethical behavior to the CEO, and 60% cited environmental violations and product recalls to the CEO as well. The influence of the perceptions of various stakeholders, regulators, communities and employees has been viewed by Kassinis and Panayiotou (2006) as critical to the welfare of the firm as they are centrally involved in enforcing the laws and other policies that companies must adhere to. Figure 1: Relationship Between CSR and Stakeholders Source: Tokoro (2007) The figure above shows the direct relationship of stakeholders to CSR in terms of the restrictions that they impose, the resource deals that they pass and the overall value creation of the organization. Gap in Research Even if the claim on the role of CEOs in terms of dealing with issues of CSR and company reputation, other studies suggests that CSR strategies and policies are instead delegated to the shareholders (Kassinis and Panayiotou, 2006, p. 67). It is often the case that the demands of the shareholders are oftentimes in conflict with the interest of customers, suppliers, governments, unions, competitors, local communities, and the general public (Sims, 2003, p. 40). The table below shows an overview of perceptions of Table 1: Stakeholders View of Corporate Responsibility Stakeholders Nature of Stakeholder Claim Shareholders Participation in distribution of profits, additional stock offerings, assets on liquidation; vote of stock; inspection of company books; transfer of stock; election of board of directors; and such additional rights as have been established in the contract with the corporation. Employees Economic, social, and psychological satisfaction in the place of employment. Freedom from arbitrary and capricious behavior on the part of company officials. Share in fringe benefits, freedom to join union and participate in collective bargaining, individual freedom in offering up their services through an employment contract. Adequate working conditions. Customers Service provided with the product; technical data to use the product; suitable warranties; spare parts to support the product during use; RD leading to product improvement; facilitation of credit. Creditors Legal proportion of interest payments due and return of principal from the investment. Security of pledged assets; relative priority in event of liquidation. Management and owner prerogatives if certain conditions exist with the company (such as default of interest payments). Suppliers Continuing source of business; timely consummation of trade credit obligations; professional relationship in contracting for, purchasing, and receiving goods and services. Unions Recognition as the negotiating agent for employees. Opportunity to perpetuate the union as a participant in the business organization. Competitors Observation of the norms of competitive conduct established by society and the industry. Business statesmanship on the part of peers. Governments Taxes (income, property, and so on); adherence to the letter and intent of public policy dealing with the requirements of fair and free competition; discharge of legal obligations of businesspeople (and business organizations); adherence to antitrust laws. Local communities Place of productive and healthful environment in the community. Participation of company officials in community affairs, provision of regular employment, fair play, reasonable portion of purchases made in the local community, interest in and support of local government, support of cultural and charitable projects. The general public Participation in and contribution to society as a whole; creative communications between governmental and business units designed for reciprocal understanding; assumption of fair proportion of the burden of government and society. Fair price for products and advancement of the state-of-the-art technology that the product line involves. Source: Sims, 2003, p. 41 For instance, consumers expect that the company should be able to carry out their business in a responsible manner; on the other hand, stakeholders expect that their investments would be returned. In other instances, customers are looking forward a return on what they paid for, while suppliers look for dependable buyers. The government wanted companies to follow legislations, while unions seek benefits for their members. The competitors, expected companies to do their business in a fair manner and local communities wanted the aforementioned to be responsible citizens. Finally, the general public expects organizations to improve the over all quality of human life, while shareholders might view this proposition as utopian (Sims, 2003). The figure below shows the dynamics of stakeholder interactions. Figure 2: Value Creation Through Dialogue with Stakeholders Source: Tokoro (2007) As such, it is in this respect that it could be said that CEOs and a particular company’s responsiveness to the demands of CSR and eventually creating a strong image is something could not be the sole determining factor for a successful CSR. Instead, the question of whether CEOs are only implementing the demands of the company’s stakeholders, or the CEOs only attending to consumer, suppliers, government, community and general public demand should also be taken into close consideration. Deficiency As most researches often attribute the success or failure of a CSR strategy to the CEO, the role and influence of other stakeholders in the organization are not often viewed as significant variables worthy of consideration. Only most recent researches are significantly attributing stakeholder roles in terms of its relationship to CSR. Albeit, based from the researcher’s survey of various secondary data, there are hardly any robust literatures stating the influence of stakeholders to the CEO and eventually the latter’s decision on how to implement its CSR program. Purpose The study is vital in order to not only contribute to the existing studies on the role of CEOs and a successful CSR program; but also to further strengthen the claim on the relationship of CSR and a favorable company image. More importantly, subtle factors that might have influenced CEO decisions, strategies and policies such as those coming from company stakeholders will be taken into close consideration and in addition, will be taken as important variables for the research. Although studies on the direct relationship of company stakeholders and CSR and presented by various researches, the role of the stakeholders in terms of influencing the CEO in its CSR decisions are seldom taken into consideration. As such it is with this respect that the research seeks to significantly contribute to the scholarly studies devoted in order to analyze such dynamics. Research Questions Main Question For the purpose of this research, the study wanted to know: What is the role of the CEO in terms of promoting the Corporate Social Responsibility (CSR) programs of their organization and its relation to building a favorable image? Subquestions Specifically, the research wanted to know: 1. What is the relationship between a successful CSR program and the role of the CEO? 2. What is the relationship between a successful CSR program and a favorable brand image? 3. What is the role of the following in terms of influencing the CSR strategies of a particular organization: a. Shareholders b. Consumers c. Suppliers d. General Public 4. How did company shareholders, consumers, suppliers and the general public influence the strategy of the CEO in terms of implementing its CSR program? Methodology Research Tradition For the purpose of this research, the study will be employing both quantitative and qualitative research methods. Â  It is often the case that quantitative research employs the method that is based on testing of theories. It uses measurement of numbers, and statistical analysis to perform its studies. The idea behind quantitative research is often to ascertain that a generalized theory or the prediction of a theory will be confirmed by the use of numbers. The aforementioned normally starts with a research question or a hypothesis in addition to other theories that are needed to be tested. The approach of quantitative research includes the use of formal and generally recognized instruments (O’brien 1998). In addition to this, the quantitative tradition of research focuses on conducting experiments with an underlying expectation that a consensus would be arrived at. This method usually aims to arrive at a predictable generalization, and a causal explanation. Quantitative research can create a controlled environment in order to attain inductive analysis. The goal of this research tradition is to establish a consensus by reducing data to numerical indications, hence finally identifying if certain generalizations are valid or invalid (O’brien 1998). In this research method it is very relevant that the researcher must maintain its independence from the research object; and consequently, the research outcome is expected to be not value affected (O’brien 1998). The quantitative methodology also tests cause and effect by using deductive logic. When done correctly a quantitative research should be able to predict, and explain the theory in question (O’brien 1998). On the other hand, the Qualitative research focuses primarily on words rather than numbers. The main research instrument for such a type of tradition is the process of involvement of the researcher to the people whom he or she studies (Dyamon and Holloway, 2002). In relation with this, the viewpoints of the participants are also taken into much account. The Qualitative research tradition focuses on small-scale studies wherein deep explorations are being conducted in order to provide a detailed and holistic description and explanation of a specific subject matter. Rather than focusing on a single or two isolated variables, the aforementioned takes into account interconnected activities, experiences, beliefs and values of people, hence adopting a multiple dimension for study. This tradition of research is also flexible in a sense that certain factors are being explored due to not necessarily adhering to a strict method of data gathering. It also captures certain processes wherein changes in sequence of events, behaviors and transformation among cultures are closely taken into consideration. More importantly, a qualitative research is normally carried out in venues that are within a respondents natural environment such as schools, offices, homes, etc. This allowed participants to be more at ease and be able to express their ideas freely (Dyamon and Holloway, 2002). Data Gathering The data gathering will consist of secondary and primary data collections. Ghauri, Gronhaug and Kristianslund (1995) emphasized the importance of secondary data collection most especially through desk or library research. Secondary data collection normally includes data that were collected by another researcher or writer. It is often the case that they are lifted from books of recent publications, journals, magazines, newspapers and even trusted websites such as those of private organizations, non-government organizations, government organizations and the likes. The review of related literature will provide a scholarly perspective on the subject matter and at the same time made the researcher aware of both previous and contemporary research on the subject matter. For the purpose of this research, the author will be using scholarly journals and articles, books and magazines specifically focusing on the oil and gas industry; and freight industry in the Middle East, most specifically Turkey. The scholarly literatures will be primarily taken from EBSCO Host, JSTOR and Questia Media America, an exclusive on-line library. For the primary data collection for quantitative data, the study will be conducting surveys among consumers, suppliers and general public using questions of ordinal measurement using Likert scales for General Electric. Surveys include the process of using questionnaires with the aim of making an estimation of the perceptions of the subjects of the study. Surveys are considered advantageous because it could be used to study a huge number of subjects (Ghauri, Gronhaug and Kristianslund, 1995). On the other hand, interviews will be conducted among selected GE shareholders regarding their perception on the role of the CEO and implementation of the company’s CSR. Data Gathering Methods and their Justification For the purpose of this research, the researcher will be using self-administered questionnaires. Self-administered questionnaires often times offer a higher response rate and are also relatively cost effective (Ghauri, Gronhaug and Kristianslund, 1995). Foremost of its advantage rests on the notion that the process of data gathering could be more personal and also the researcher will be able to clarify certain notions that could be unclear in the survey form. However, one distinct disadvantage of such a method is the difficulty of administrating the survey to multiple respondents all at the same time. In addition, the self-administered data gathering could be very time consuming as well. The research will also be conducting an interview in order to collect the qualitative data necessary for the research. Interviews are very relevant most specially in getting data that could be a rich source of information that surveys could not provide (Ghauri, Gronhaug and Kristianslund, 1995). For the purpose of interviewing, various stakeholders from General Electric Corporation will be asked with regard to their perceptions of how GE should be employing its CSR, and their perceptions on the role of the CEO in terms of effectively implementing its CSR and the company’s image. Questionnaire Design The questionnaire design for the survey will be made in a detailed, precise and logical construction of close-ended questions. In addition with this, the questions will also be made in accordance with the research question and the objectives of the research (Oppenheim, 1992). The questions will be formulated using an ordinal scale and will be close-ended in nature. Such is relevant so that respondents would only have to encircle or check the designated number of their corresponding responses (Oppenhein, 1992). In addition to this, close-ended questions are very easy to answer and could enable the researcher create a summated value that could be use for data analysis. The questions that will be used in the interview will be tailored in such a manner that would directly answer concerns that are in accordance of the objectives of the study. The questions for the shareholders will be specifically created in a manner where there will be an open flow of information and exchange of ideas. The details on how consumers, suppliers and general public wanted the company to act together with its policies and possible ethical practices will be included in the survey. In this respect, questions will be formulated with a closed-ended nature. Sampling For the purpose of this research, the researcher will conduct a survey based on simple random sampling (SRS) which will include randomly choosing participants coming from consumers, suppliers and general public. On the other hand, the research will be employing purposive sampling methods in terms of choosing the stakeholders of General Electric who can participate in the study. Target Population According to Ghauri, Gronhaug and Kristianslund (1995) research should cater to a target population that has all the necessary information for the research such as sampling elements, sampling units, and area of coverage. For the purpose of this study, the author is trying to identify the role of consumers, suppliers and the general public. As such, the study will be asking 120 respondents to participate in the survey of which will primarily come from consumers and suppliers of General Electric as well as the general public who are concerned with General Electric and its operations. Reliability and Validity The studys reliability and validity go hand in hand as patterns of measurement are both dependent on the aforementioned (Zikmund, 1994). Reliability primarily focuses on the internal consistency and the repeatability of the variables within the research. On the other hand, validity centers on the correctness and appropriateness of the question that one intends to measure (Ghauri, Gronhaug and Kristianslund, 1995). According to Chisnall (1997), validity is generally considered and established through the relationship of the instrument to the content, criterion or construct that it attempts to measure. A lack of validity can lead to incorrect conclusion. In order to make sure that the instrument that will be used are reliable and valid, the researcher will assure that such is patterned based on the objectives of the study, the secondary data and also on the feedback that was given based on the pilot study that will be conducted. Analysis of Data Data information gathered from the surveys and interviews and secondary data from the other studies found will be used for the analysis that would answer the research question. Charts and comparisons of data will be used as analysis tools. Statistics used will be based on the survey results from the questionnaire made by the researcher. Statistical Products and Service Solutions (SPSS) will also be used to determine the stand of the respondents regarding a particular question formulated in the survey (Griego and Morgan, 2000, p. 2). References Carroll A. and Buchholtz A.K., (2003). Business and Society: Ethics and Stakeholder Management, 5th ed. Mason, O.: South-Western. Chisnall P. M., (1997). Marketing Research, 5ed., Berkshire: McGraw-Hill. Woodruff H. (1995), Services Marketing. London: Pitman Publishing Daymon C. and Holloway I., (2002). Qualitative Research Methods in Public Relations and Marketing Communications. London: Routledge. Ghauri, P., Gronhaug, K. and Kristianslund, I., (1995). Research Methods In Business Studies: A Practical Guide. Great Britain: Prentice Hall. Griego O. and Morgan G. (2000). SPSS for Windows: An Introduction to Use and Interpretation in Research. Mahwah, NJ: Lawrence Erlbaum Associates. Kassinis G. and Panayiotou, A. (2006). Perceptions Matter: CEO Perceptions and Firm Environmental Performance. The Journal of Corporate Citizenship, (23), p. 67. Mohr L.A. and Webb D. J., (2001). Do Consumers Expect Companies to Be Socially Responsible? the Impact of Corporate Social Responsibility on Buying Behavior. Journal of Consumer Affairs. (35) (1). OBrien, Gerard J. (1998) The Role of Implementation in Connectionist Explanation, Psychology, (9) 6, p.3. Oppenhein, A. N, (1992). Questionnaire Design Interviewing and Attitude Measurement. London: Pinter. Parsons, Talcott (1954). Essays in Sociological Theory. Revised Edition. New York: Free Press. PR News Wire (2008). Corporate Communications Officers in Worlds Most Admired Companies Have Longer Tenures, Fewer Rivals and Report to the CEO; New Study Underscores Critical and Evolving Role of the CCO -; Forecasts CCOs Shifting Focus To Reputation, Social Responsibility and Social Media in 2008. Accessed in the PR News Wire Database. PR News (2007). Quick Study: CEOs Bear Responsibility; Customer Relations Is Dysfunctional; Social Media Invades. PR News. Potomac, (63), 9, p. 1 PR News. (2006). Changing Face Of CSR: New Trends Redefine Doing Well By Doing Good. PR News. Potomac, (62) 42, p. 1 Sims, R., (2003). Ethics and Corporate Social Responsibility: Why Giants Fall. Westport, CT: Praeger. Tokoro N (2007). Stakeholders and Corporate Social Responsibility (CSR): A New Perspective on the Structure of Relationships. Asian Business Management, 6 (2), pp.143-162. Watts P. and Holme R. (1999). Meeting Changing Expectations: Corporate Social Responsibility Available: http://www.wbcsd.org/publications/csrpub.htm [accessed 5June 2008]. Zikmund, G. W. (1994). Exploring Marketing Research. Dryden.

The hunting trip Essay Example for Free

The hunting trip Essay Finally, the time was here. It was fall break, and I had nothing to do but sit around and enjoy life. This area was covered with dirt, and no matter how hard I tried, I would get filthy. I couldnt wait to get home to take a nice shower. It was a nice place to sit around and clear my mind. In the middle of September, my mother and I would go to the store to buy tons of food for hunting trip. At last, after being at the store for hours, we would be on our way to meet my dad and my brothers. Hunting trip was the highlight of my year. For five years, we would go to the exact same campsite. At this campsite, the air was fresh, the sun was out, and all I could hear was the beautiful sound of the calm river flowing downstream. Our campsite was setup right next to the clear stream. My brothers and I would always throw rocks and sticks into the river. We loved to watch the huge fish jump far above the water, and dive back in head first. We never stayed in a tent because of all the moisture that the river would give off. In the mornings, when we did stay in the tent, my family and I would wake up feeling drenched with water. We had a new camper that was big enough to fit five people. The trailer was white with a stripe going down the side. It had a huge dining table that turned into a double bed. There was a bunk bed, which also has departments in for different storage. There was a hallway that leads to a king size bed where my parents slept. All of the sheets are dirty from all the dirt that was brought into the trailer. In the mornings, I could feel the moisture of the cold air from the inside of the camper. Every morning, my dad would wake up to go hunting. My nostrils would fill from the luscious smell of the brew from the freshly made coffee that my mom would make for my dad. He would always take my brothers hunting with him. Mom and I called them the three mighty hunters. At about 9:00 every morning, my mom and I would start to cook breakfast for the boys. My favorite breakfast to cook was egg casserole. We would get all of the ingredients together to start off our creation. We never knew what we would put in the dish; we would just start throwing different things in there. The smell of bacon sizzling on the frying pan would make my mouth water. Finally, after finishing the creation of food, we would put it into the oven to cook. While waiting for the boys to get back, I would get a horrifying feeling in my stomach. Did they get something? Did a bear find them? I was always so nervous. I would just sit in front of the campfire waiting impatiently for their return. The sun would gleam on my rosy cheeks as I sat there. Looking at all of the green around me would always bring a smile to my face. There were so many different types, I never had a favorite. I could hear the blissful birds chirping in the blue, clear sky. If I was lucky, the birds would be interrupted by the call of an elk shouting out to the cows. I loved that noise! Every time I heard it, I would go get my mom in the camper so she could hear the large animals call from nature. After hours of waiting for my brothers and my dad, they finally returned to camp. They were filthy with mud from the four-wheeler flicking it up onto their faces. They were usually wet from the moisture of the entire plantation that they would walk into. I could tell that they were tired because they were always out of breath. We would take our breakfast outside, and, as a family, we would sit around the campfire. The lawn chairs that we would sit on were so uncomfortable. They were starting to rip on the bottom. Every time that my dad would get back from a hunt, he would tell the whole hunt from start to finish. Every hunt consisted of, Jonathan needs to learn how to walk quietly. Learn to pick up your feet son. All those two would do was complain. Jonathan would reply, That was Jesse, not me. In the early afternoon, my dad would let me take the four-wheeler out for a spin. I would start up the engine, and hear the loud roar that the machine made. I shifted in high gear, and was on my way for an adventure. I would drive about 33 mph, just enough for the chilly wind to blow through my hair. Every so often, a bug would hit my face; I hated that feeling. It felt as if a tiny pebble whacked me on my cheek. As I drove, I couldnt believe the gorgeous forest scene that laid directly in front of me. As I looked in the trees, I could see horns from a buck, and his doe wondering around him. I knew that I had to get back because my dad needed to start his afternoon hunt. When I got back, my dad, surprisingly, asked me to come along instead of the boys. I was hesitant at first, but I knew that if I went, I could see a lot of different parts of nature. I put on a camouflage suit, and covered my face with dark paint. My stomach was tangled in knots. I was so excited to be a part of hunting. A lot of girls dont hunt, but I wanted to be one of the few that did. I wanted to learn all of the techniques of hunting from my dad. We started off toward the top of the mountain on the four wheeler. My dad said that there were big elk on the top. After a few miles, we got off the four wheeler, and started walking into the deep, dark forest. The sun was starting to set, and the breeze was getting more frigid. I had two long sleeve shirts on and a huge jacket to keep me warm. Fifteen minutes into the walk, I heard a loud bugle directly in front of us. It sounded as if a monster was growling. By the noise, I knew that this animal had to be huge. My dad called back with his cow call. It was a high pitch grunt. The elk instantly bugled back. My dad and I were both so nervous. As we looked harder and harder into the woods, we could barely see the animal. My dad took his binoculars from his backpack, and gave me them so I could see what he really looked like. His horns were the biggest things I have ever seen. They had a dark brown base, with off-white tips. His body was a tan color with dark brown around his neck. He would stick his horns by a tree, and start scratching at it as if he was trying to sharpen them. His eyes looked so clueless and clear. I think that he was hesitant because he was still thinking about the cow call. A few minutes later, the elk slowly walked back into the forest. My dad said that it was getting too dark to shoot anything, so we had to head back to campsite. When we got back, I couldnt wait to tell my mom what I had seen. I screamed and yelled in excitement. I kept stumbling over my words, trying to tell too many things at once. As soon as I started making more sense, my moms face looked flourished by all of the thrilling news. I still couldnt believe that I actually saw that magnificent animal. As the night went on, that one moment kept racing through my mind. That night, we cooked hotdogs on the end of a stick, and stuck them in the flaming fire. My face was burning from the scalding fire. It was pitch black outside, and I could hear the little crickets start to sing their pleasant tunes. While we were all eating a delicious dinner, my dad tried to scare us by telling a frightening story. It never worked for me! He would go off about big foot and how he was over twelve feet tall. My brothers were still pretty young, so look in their eyes looked like they were both terrified. During his story, I just looked around the campsite. I couldnt see anything but the bright, full moon beaming above us. The stars were cluttered in all different formations. It was exquisite! After spending about five days on a hunting trip, I knew why I loved it so much. Even though I would get filthy from all of the dirt, I still loved it. At this certain place, I could get away from everything that was bad in my life, or even take a break from the pressure of school and sports. Here, I could hang out with my family without thinking about cleaning the house to make my mom happy, or feeding the dogs so my dad didnt have to do it. At this specific location, my family and I forgot about the stress from work or school, and just had the best time of our lives. That is why this place was, and will always be, the most important to me.