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Based on its track record for identifying and discovering useful and accurate information, science and technology generated from it are an integral component of society. Science gives us mechanisms and approaches to understand and confront our biases and preconceptions. Because of its foundational importance in society, I think everybody should be involved in the scientific process, and the key component to science is conducting research. I think society as a whole would be better off if more people had meaningful research experience and so would encourage everybody to engage in a scientific and research experience.  

I’m interested in many thing, but based on my expertise, my research focuses on two areas: 

1. The evolution and regulation of metabolic pathways. This work is heavily computational and statistical in nature. Much of the work can be done in R, which is becoming increasingly a component of biological and biochemical research. Being able to code in a programming language like R is a skill that is becoming more common and useful for biologists and biochemists. Today, R can be used for topics like the analysis of protein structures, metabolic flux analysis, analyzing gene expression data, and studying ecological systems. In most undergraduate classes (including some of mine), the evolution of proteins is taught in the context of relationships between protein function and sequence. In reality, the situation is more complex. Many variables appear to play a role in protein evolution, including expression levels, protein stability, the protein’s position in biological networks (e.g. metabolism), the codons used to code for the protein, etc. I work on understanding how these different factors affect the evolution of different proteins, mostly enzymes involved in small molecule metabolism. 

2. The second area is the result of a collaboration with Dr. Aimee Eggler. Dr. Eggler is interested in how different molecules interact to affect processes in cells and the responses of cells. For example, if one molecule increases some cellular process and another molecule causes the same cell process to decrease, what happens if you grow cells in the presence of both molecules? In this case, you might expect them to cancel out, and there be no effect on the process. But in reality, the situation can be much more complex and depend on the concentrations of the molecules and the mechanism by which they affect the process (i.e. how do they cause the process to increase or decrease). 

Understanding how molecules interact to affect biological systems has implications to many things, including drug combinations, environmental pollution, etc. 

Biochemistry at its heart is a reductionist science. For this research, dealing with cellular systems can be slow, expensive, and the data generated from cellular systems tends to be noisy (e.g. high standard deviations). The cellular responses though are the result of responses of the component biological molecules. Studying simpler systems, like enzymes, can be faster (allowing for the collection of more data), cheaper, and generate data that is less noisy. Based on working with cellular systems, the mechanisms and variations resulting from the combination of even two molecules aren’t broadly well understood. I’m working on using simple enzyme systems to study the effects of combinations of molecules on their structure and function. This approach allows for the study of interactions beyond two molecules and coming up with methods to analyze and model the data that then can be applied to cellular systems. This work then can be used as a foundation to understand how larger and more complex systems respond to multiple molecules. This project is excellent for undergraduates because meaningful research can be fit into a busy schedule, and students get practice with important and core biochemical concepts related to enzyme kinetics and protein structure.  


Recent publications (evolution or interactions)

Publications related to evolution:

1. Palenchar PM, DeStefanis T. Transcriptional noise adjusted for expression levels reveals genes with high transcriptional noise that are highly expressed, functionally related, and co-regulated in yeast. Curr Genet. 2022 Oct 17. https://link.springer.com/article/10.1007/s00294-022-01255-x 

 2. Palenchar PM. The Influence of Codon Usage, Protein Abundance, and Protein Stability on Protein Evolution Vary by Evolutionary Distance and the Type of Protein. Protein J. 2022 Apr;41(2):216-229.  https://link.springer.com/article/10.1007/s10930-022-10045-w

3. Palenchar PM, Palenchar JB. The evolution of metabolic enzymes in Plasmodium and trypanosomatids as compared to Saccharomyces and Schizosaccharomyces. Mol Biochem Parasitol. 2012:184(1):13-9. http://www.sciencedirect.com/science/article/pii/S0166685112000783

4. Palenchar P, Mount M, Cusato D, Dougherty J. Using genome-wide protein sequence data to predict amino acid conservation. Protein J. 2008:27(6):401-7. http://www.springerlink.com/content/v8612l24240437m2/fulltext.pdf

5. Palenchar PM. Amino acid biases in the N- and C-termini of proteins are evolutionarily conserved and are conserved between functionally related proteins. Protein J. 2008:27(5):283-91. http://www.springerlink.com/content/lt38vh3j5118p3h2/fulltext.pdf

Publications related to interactions:

1. Repash EM, Pensabene KM, Palenchar PM, Eggler AL. Solving the Problem of Assessing Synergy and Antagonism for Non-Traditional Dosing Curve Compounds Using the DE/ZI Method: Application to Nrf2 Activators. Front Pharmacol. 2021 Jun 7;12:686201 https://www.frontiersin.org/articles/10.3389/fphar.2021.686201/full

2. Middler SL, Gomez S, Parker CD, Palenchar PM. The effects of combinatorial treatments with stress inducing molecules on growth of E. coli colonies. Curr Microbiol. 2011:63(6):588-95. http://link.springer.com/article/10.1007%2Fs00284-011-0021-3