An improved multi-parametric programming algorithm for flux balance analysis of metabolic networks

Flux balance analysis has proven an effective tool for analyzing metabolic networks. In flux balance analysis, reaction rates and optimal pathways are ascertained by solving a linear program, in which the growth rate is maximized subject to mass-balance constraints. A variety of cell functions in response to environmental stimuli can be quantified using flux balance analysis by parameterizing the linear program with respect to extracellular conditions. However, for most large, genome-scale metabolic networks of practical interest, the resulting parametric problem has multiple and highly degenerate optimal solutions, which are computationally challenging to handle. An improved multi-parametric programming algorithm based on active-set methods is introduced in this paper to overcome these computational difficulties. Degeneracy and multiplicity are handled, respectively, by introducing generalized inverses and auxiliary objective functions into the formulation of the optimality conditions. These improvements are especially effective for metabolic networks because their stoichiometry matrices are generally sparse; thus, fast and efficient algorithms from sparse linear algebra can be leveraged to compute generalized inverses and null-space bases. We illustrate the application of our algorithm to flux balance analysis of metabolic networks by studying a reduced metabolic model of Corynebacterium glutamicum and a genome-scale model of Escherichia coli. We then demonstrate how the critical regions resulting from these studies can be associated with optimal metabolic modes and discuss the physical relevance of optimal pathways arising from various auxiliary objective functions. Achieving more than five-fold improvement in computational speed over existing multi-parametric programming tools, the proposed algorithm proves promising in handling genome-scale metabolic models.Comment: Accepted in J. Optim. Theory Appl. First draft was submitted on August 4th, 201

Data reduction in television signals for brandwidth compression

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Bias in the Evaluation Process: Influences of Speaker Order, Speaker Quality, and Gender on Rater Error in the Performance Based Course

This study examines how variations in speaker order increase the potential for rater error in the performance based course. Seventy-six undergraduate raters were randomly assigned to one of eight treatment groups and asked to grade eight-week training course. Speaker order and presentation quality varied across groups and an ANOVA was used to examine significant differences across rater assessments, feedback quality and rater gender. Significant main effects were identified in each of the eight treatment groups suggesting that speaker order influenced rater scoring

Stretching the Academic Dollar: The Appropriateness of Utilizing Instructor Assistants in the Basic Course

As more universities across the country are feeling the pressures of providing an increasingly rigid financial accountability to tax payers and state legislatures, speech and communication departments find themselves in a precarious position. Namely, how can communication departments teach the budding number of students enrolled in their courses with little increase in budget, while continuing to produce effective speakers? One common answer to this dilemma involves the use of graduate students, and in some cases undergraduate students, as teaching assistants in the basic course. This study examines the efficacy of using undergraduate instructor assistants in the basic course at a large Midwestern University and addresses potential stumbling blocks in training, such as speaker order and rater error. Thirty-eight undergraduate instructor assistants were randomly assigned to one of four treatment groups and asked to grade four 10-minute persuasive speeches following their eight-week training course. An ANCOVA was used to examine significant differences across presentation grades for speakers in each group, while an ANOVA was used to determine differences in the quality of comments based on speaker order. No significant differences were identified in either analysis suggesting that when properly trained, undergraduate instructor assistants can grade consistently across multiple groups regardless of speaker order

ASSIMILATOR: A new tool to inform selection of associated genetic variants for functional studies

Motivation: Fine-mapping experiments from genome-wide association studies (GWAS) are underway for many complex diseases. These are likely to identify a number of putative causal variants, which cannot be separated further in terms of strength of genetic association due to linkage disequilibrium. The challenge will be selecting which variant to prioritize for subsequent expensive functional studies. A wealth of functional information generated from wet lab experiments now exists but cannot be easily interrogated by the user. Here, we describe a program designed to quickly assimilate this data called ASSIMILATOR and validate the method by interrogating two regions to show its effectiveness. Availability: http://www.medicine.manchester.ac.uk/musculoskeletal/research/arc/genetics/bioinformatics/assimilator/. Contact: [email protected]