Our multidisciplinary approach to a Bioinformatics Minor at the University of Arkansas at Little Rock (UALR) allows students from Information Science, Computer Science, and Biology programs to participate in the growing field of bioinformatics. Two separate tracks allow students to complent the skills that they learn in their majors with skills in either information science or the life sciences. This eighteen credit hour minor begins with all students enrolling in an Introduction to Bioinformatics course that is followed by eleven credit hours of complentary courses. Then, in a final, senior level, survey format Bioinformatics Theory and Applications course, they once again converge to apply their training to a sester-long, multidisciplinary team project. This minor allows students to choose a wide array of careers in the medical and health sciences fields and in the analysis of biological data. It is also an ideal program to prepare the students for graduate programs in bioinformatics.

BACKGROUND: Structure matching plays an important part in understanding the functional role of biological structures. Bioinformatics assists in this effort by reformulating this process into a problem of finding a maximum common subgraph between graphical representations of these structures. Among the many different variants of the maximum common subgraph problem, the maximum common induced subgraph of two graphs is of special interest. RESULTS: Based on current research in the area of parameterized computation, we derive a new lower bound for the exact algorithms of the maximum common induced subgraph of two graphs which is the best currently known. Then we investigate the upper bound and design techniques for approaching this problem, specifically, reducing it to one of finding a maximum clique in the product graph of the two given graphs. Considering the upper bound result, the derived lower bound result is asymptotically tight. CONCLUSION: Parameterized computation is a viable approach with great potential for investigating many applications within bioinformatics, such as the maximum common subgraph problem studied in this paper. With an improved hardness result and the proposed approaches in this paper, future research can be focused on further exploration of efficient approaches for different variants of this problem within the constraints imposed by real applications.

BACKGROUND: Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies. RESULT: We designed a Cross-Linked Peptide Mapping (CLPM) algorithm which takes advantage of all of the information available in the experiment including the amino acid sequence from each protein, the identity of the crosslinker, the identity of the digesting enzyme, the level of missed cleavage, and possible chemical modifications. The algorithm does in silico digestion and crosslinking, calculates all possible mass values and matches the theoretical data to the actual experimental data provided by the mass spectrometry analysis to identify the crosslinked peptides. CONCLUSION: Identifying peptides by their masses can be an efficient starting point for direct sequence confirmation. The CLPM algorithm provides a powerful tool in identifying these potential interaction sites in combination with chemical crosslinking and mass spectrometry. Through this cost-effective approach, subsequent efforts can quickly focus attention on investigating these specific interaction sites.

The control of concurrent access to shared resources is an important feature of both centralized and distributed operating systems. In conventional systems, exclusive access is the rule while concurrent access is the exception. Dataflow computer systems, along with an applicative style of programming, provide an execution environment in which this philosophy is reversed. In these latter systems, it is necessary to reexamine the manner in which synchronization of access to shared resources is specified and implemented. A basic design for a dataflow resource manager is reviewed, illustrating the clear separation between access mechanism and scheduling policy. The semantics of the access mechanism is based solely on the principle of data dependency. Specifications are presented for a general scheduler to further constrain or order accesses to the resource. Using ``open path expressions'' as a very high-level specification language for synchronization, it is shown how to automatically synthesize a scheduler as a distributed network of communicating modules.