Biomedical subjects

Prasad S Dhurjati

Publications and source records attributed to Prasad S Dhurjati.

3 recordsLinked to original sources

PubMed2003

eXPatGen: generating dynamic expression patterns for the systematic evaluation of analytical methods.

MOTIVATION: Experimental gene expression data sets, such as those generated by microarray or gene chip experiments, typically have significant noise and complicated interconnectivities that make understanding even simple regulatory patterns difficult. Given these complications, characterizing the effectiveness of different analysis techniques to uncover network groups and structures remains a challenge. Generating simulated expression patterns with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of different analysis methods. A simulation-based approach can systematically evaluate different gene expression analysis techniques and provide a basis for improved methods in dynamic metabolic network reconstruction. RESULTS: We have developed an on-line simulator, called eXPatGen, to generate dynamic gene expression patterns typical of microarray experiments. eXPatGen provides a quantitative network structure to represent key biological features, including the induction, repression, and cascade regulation of messenger RNA (mRNA). The simulation is modular such that the expression model can be replaced with other representations, depending on the level of biological detail required by the user. Two example gene networks, of 25 and 100 genes respectively, were simulated. Two standard analysis techniques, clustering and PCA analysis, were performed on the resulting expression patterns in order to demonstrate how the simulator might be used to evaluate different analysis methods and provide experimental guidance for biological studies of gene expression. AVAILABILITY: http://www.che.udel.edu/eXPatGen/

Algorithms↗

PubMedDate not supplied

Decreased protein expression and intermittent recoveries in BiP levels result from cellular stress during heterologous protein expression in Saccharomyces cerevisiae.

Cells are inherently robust to environmental perturbations and have evolved to recover readily from short-term exposure to heat, pH changes, and nutrient deprivation during times of stress. The stress of unfolded protein accumulation has been implicated previously in low protein yields during heterologous protein expression. Here we describe the dynamics of the response to this stress, termed the unfolded protein response (UPR), during the expression of the single chain antibody 4-4-20 (scFv) in Saccharomyces cerevisiae. Expression of scFv decreased the growth rate of yeast cells whether the scFv was expressed from single-copy plasmids or integrated into the chromosome. However, the growth rates recovered at longer expression times, and surprisingly, the recovery occurred more quickly in the high-copy integration strains. The presence of a functional UPR pathway was necessary for a recovery of normal growth rates. During the growth inhibition, the UPR pathway appeared to be activated, resulting in decreased intracellular scFv levels and intermittent recovery of the chaperone BiP within the endoplasmic reticulum. Intracellular scFv was observed primarily in the endoplasmic reticulum, consistent with activation of the UPR pathway. Although the intracellular scFv levels dropped over the course of the expression, this was not a result of scFv secretion. A functional UPR pathway was necessary for the drop in intracellular scFv, suggesting that the decrease was a direct response of UPR activation. Taken together, these results suggest that control of heterologous gene expression to avoid UPR activation will result in higher production levels.

Cell Line↗

PubMedDate not supplied

A dynamic mathematical model to clarify signaling circuitry underlying programmed cell death control in Arabidopsis disease resistance.

Plant cells undergo programmed cell death in response to invading pathogens. This cell death limits the spread of the infection and triggers whole plant antimicrobial and immune responses. The signaling network connecting molecular recognition of pathogens to these responses is a prime target for manipulation in genetic engineering strategies designed to improve crop plant disease resistance. Moreover, as alterations to metabolism can be misinterpreted as pathogen infection, successful plant metabolic engineering will ultimately depend on controlling these signaling pathways to avoid inadvertent activation of cell death. Programmed cell death resulting from infection of Arabidopsis thaliana with Pseudomonas syringae bacterial pathogens was chosen as a model system. Signaling circuitry hypotheses in this model system were tested by construction of a differential-equations-based mathematical model. Model-based simulations of time evolution of signaling components matched experimental measurements of programmed cell death and associated signaling components obtained in a companion study. Simulation of systems-level consequences of mutations used in laboratory studies led to two major improvements in understanding of signaling circuitry: (1) Simulations supported experimental evidence that a negative feedback loop in salicylic acid biosynthesis postulated by others does not exist. (2) Simulations showed that a second negative regulatory circuit for which there was strong experimental support did not affect one of two pathways leading to programmed cell death. Simulations also generated testable predictions to guide future experiments. Additional testable hypotheses were generated by results of individually varying each model parameter over 2 orders of magnitude that predicted biologically important changes to system dynamics. These predictions will be tested in future laboratory studies designed to further elucidate the signaling network control structure.

Apoptosis↗