Student Research

See our student research work in bioinformatics

There are a number of opportunities for students to get research experience while enrolled as a student in the bioinformatics concentration, including being part of a student team or participating in sponsored research projects.


Interaction Relation Based Computation Approaches for Predicting Interaction Sites of Cytochrome   detail
and Photosystem I
January Wisniewski

January Wisniewski,  a computer science major, now graduate student of CISE program, conducted a research work jointly with the faculty members of TSU and UTK. The research topic is "Interaction-Relation Based Computation Approaches for Predicting Interaction Sites of Cytochrome and Photosystem I". The research addresses the problem of computationally predicting the interaction sites of protein pairs (donors and acceptors) that tap into photosynthetic processes to efficiently produce inexpensive hydrogen. The work was presented at the Bioinformatics conference at Georgia Tech, Nov 7 – 9, 2013

Dimension Reduction for Pattern Detection of Gene Expression Data     detail
Linda Emujakporue

Linda Emujakporue also a computer science major, now graduate student of CISE program, conducted a research that applies PCA method to reduce the dimension of microarray data. Microarray experiments involve the measurement of the expression level of many thousands of genes in biological samples. Gene expression data usually is high dimensional since each gene or sample represents one dimension in the data. One fundamental question is: What signatures or patterns of gene expression can be found in all the gene expression values and how they can be found accurately and time efficiently? Clustering can show the relationships between samples (such as normal vs, diseased cells), between genes, or both. However, it may not accurate and time efficient due to the nature of redundancy and high dimension in the data. In this research, as an exploratory technique used for high dimensional data analysis, Principal components analysis (PCA) is investigated. PCA is showed powerful in its ability to represent complex gene expression data sets succinctly. By removing the dependent or redundant data, PCA can reduce the dimensionality of data sets without loss important information. PCA will be used to assist clustering technique for finding patterns in gene expression data more accurately and time efficiently. In the presentation, the technique will be stepwise explained with selected gene expression data. (presented in 2014 TSU Research Symposium)

Novel Motif Detection Algorithms for Finding Protein-Protein Interaction Sites      detail
January Wisniewski

Protein–protein interactions refer to intentional physical contacts established between two or more proteins as a result of biochemical events and/or electrostatic forces. Where and how proteins interact with each other reflect important functionalities of proteins. In a previous research, we investigated the computational approaches for predicting the interaction sites of Photosystem I (PSI) and cytochrome c6 (cyt c6) based on amino acid interactions. PSI and crt c6 are the proteins functioning in natural photosynthesis. Through understanding potential interactions between PSI with cyt c6, hopefully, biology scientist may be able to produce hydrogen in high rate. 
In this research, we investigate the relation between interaction sites and motifs in PSI and cyt c6. In genetics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. We expect that the motif detection can help to find the interaction sites of PSI and cyt c6 more accurately. Motif detection is an NP-hard problem. We developed a number of motif detection algorithms with heuristic and/or divide-and-conquer techniques, respectively. The result shows that the algorithm combining heuristic and divide-and-conquer techniques can find the motifs faster with higher scores.    (presented in 2014 TSU Research Symposium)

Computational Approaches for Predicting Interaction Sites of Cytochrome and Photosystem I     detail
Pankaj Pmishra, Linda Emujakporue, K. Wehbi

Hydrogen is a particularly useful energy carrier for transportation. However, there are no sources of molecular hydrogen on the planet. An attractive solar-based approach is bio-hydrogen production, which utilizes protein components, Photosystem I (PSI) and cytochrome c6 (cyt c6), that function in natural photosynthesis. In aiming to increase hydrogen production, it is prudent to understand potential interactions between PSI with cyt c6, and how they affect protein-protein affinity, leading to changes in electron transfer, which would lead to overall H2 yield. For this research, protein sequences from these systems are analyzed by computational approaches, in which we propose to predict the interacting residues of the cyt c6-PSI protein pair. First, the interaction relation is mathematically modeled. Then, dynamic programming algorithms are proposed to efficiently calculate the interaction score and predict the interaction sites. The proposed algorithms are applied to 86 pairs of cyt c6 and PsaF residue sequences, which have electrostatic attraction with each other. Finally, the putative interaction sites are analyzed and other chemical properties such as net charge of the residue sequences are investigated. A preliminary comparison between computational and laboratory approach is also given.   (submitted to 5th International Conference on Bioinformatics and Computational Biology, March, 2013)


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