Humans Seem To Have A Great Love Of Categorizing Organisms ✓ Solved

Humans It Would Seem Have A Great Love Of Categorizing Organizing

Humans have a great love of categorizing, organizing, and pigeon-holing things. This love affair extends to life-forms, of course – we have been attempting to group and name plants, animals, and insects as far back as 1500 BC. By studying the relationships of things, we can better understand behaviors and characteristics important to agriculture, medicine, animal husbandry – and of course, evolution itself. From your basic biology classes, you should remember that the act of classifying organisms is called taxonomy. The science that studies how those organisms evolved – and are related to one another - is called phylogeny.

In the early days of the scientific method, organisms were compared by their morphology – their physical structure and characteristics. While this works to a certain extent, it caused some honestly hilarious pairings. For example, there's a ruminant primate (monkeys and cows are not in fact directly related) – and if you compare the morphology of an octopus' eye to that of humans, you can see that they must be closely related! With the advent of DNA sequencing, scientists were able to go directly "to the source" for information on evolutionary history (phylogeny). Thanks to molecules like the small ribosomal subunit (16S in prokaryotes and 18S in eukaryotes), we have excellent unique identifiers for species.

To reconstruct phylogeny and create a phylogenetic tree, we start with a Multiple Sequence Alignment (MSA). The information can then be used to both identify and group the species taxonomically in a variety of ways. Let's take a look at three of the most common methods of creating phylogenetic trees – Distance, Parsimony, and Bayesian.

Distance Method

One of the simplest and oldest methods, the distance approach works by simply computing a distance matrix for each possible pairing of sequences. This forms a distance matrix calculation in which we can start drawing a phylogenetic tree. The distance approach, while simple, is widely utilized because of its computational ease, which makes it efficient for large numbers of species.

The problem with the distance approach is that it is very simplistic – it does not take into account any sort of evolutionary model of change. To improve accuracy, various models of mutation can be added to this method, such as the Jukes-Cantor method and the Kimura 2-parameter model, which weight the calculations based on assumed odds of different types of mutations.

Maximum Parsimony Method

Maximum parsimony seeks to minimize the number of evolutionary changes needed to explain the observed data. Algorithms are designed to recreate the evolutionary history of the organisms being analyzed with the goal of identifying the simplest tree topology. This means using informative sites in the alignment, which help infer ancestral states at the nodes of the tree.

Despite its advantages, maximum parsimony has its limitations. Because of the numerous possible tree configurations, heuristic methods are often used in practical applications to efficiently find the best tree configurations.

Maximum Likelihood Method

The Maximum Likelihood method is grounded in statistical principles. It iteratively evaluates the probability of the observed data fitting the various tree structures based on assumed model parameters. Although computationally intensive, it offers a robust evaluation of trees according to the chosen evolutionary model.

MLE ensures that researchers can derive meaningful inferences about tree topology while addressing the variability present in biological data. It treats individual mutations as independent events, thus requiring careful interpretation when drawing biological conclusions.

Bayesian Inference Method

Bayesian phylogenetic reconstruction employs probabilistic reasoning based on Bayes' theorem. It provides clarity on the estimated parameters and allows researchers to derive credibility intervals, representing the probability that a given tree corresponds accurately to the biological data observed.

Bayesian methods offer a distinct advantage over prior approaches by quantifying uncertainty in the estimated parameters of the phylogenetic model.

Conclusion

Determining which phylogenetic reconstruction method to use depends on the specific research question and data. For studies involving high taxa counts, a distance method may be advisable for expediency, while for fewer taxa, contrasting multiple methods is sensible to draw holistic conclusions. Popular software such as MrBayes, PAUP, and PHYLIP can be utilized to assess various approaches to phylogenetic tree construction.

References

• Manktelow, M. (2010). History of Taxonomy.
• Felsenstein, J. (1985). Confidence Limits on Phylogenies: An Approach Using the Bootstrap. Evolution.
• Huelsenbeck, J. P., & Ronquist, F. (2001). MRBAYES: Bayesian Inference of Phylogenetic Trees. Bioinformatics.
• Team, R. C. (2013). R: A Language and Environment for Statistical Computing.
• Hahn, M. W., & Han, M. V. (2008). Molecular Population Genetics. Springer.
• Kumar, S., & Gadagkar, S. R. (2005). Reconstructing Evolutionary Relationships in Phylogenetics: A Practical Guide. Springer.
• Rokas, A., & Carroll, S. B. (2006). More is More: Sequence Complexity in Phylogenetic Reconstruction. Trends in Ecology & Evolution.
• Swofford, D. L. (2003). PAUP: Phylogenetic Analysis Using Parsimony (and Other Methods), Version 4. Sinauer Associates.
• Yang, Z. (2006). Computational Molecular Evolution. Oxford University Press.
• Pagel, M. (1999). Inferring the Historical Patterns of Biological Evolution. Nature.