Biomedical subjects

Kellie J Sims

Publications and source records attributed to Kellie J Sims.

4 recordsLinked to original sources

PubMed2005

Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae.

Mathematical models have become a necessary tool for organizing the rapidly increasing amounts of large-scale data on biochemical pathways and for advanced evaluation of their structure and regulation. Most of these models have addressed specific pathways using either stoichiometric or flux-balance analysis, or fully kinetic Michaelis-Menten representations, metabolic control analysis, or biochemical systems theory. So far, the predictions of kinetic models have rarely been tested using direct experimentation. Here, we validate experimentally a biochemical systems theoretical model of sphingolipid metabolism in yeast. Simulations of metabolic fluxes, enzyme deletion and the effects of inositol (a key regulator of phospholipid metabolism) led to predictions that show significant concordance with experimental results generated post hoc. The model also allowed the simulation of the effects of acute perturbations in fatty-acid precursors of sphingolipids, a situation that is not amenable to direct experimentation. The results demonstrate that modelling now allows testable predictions as well as the design and evaluation of hypothetical 'thought experiments' that may generate new metabolomic approaches.

Computer Simulation↗

PubMed2004

Integration of kinetic information on yeast sphingolipid metabolism in dynamical pathway models.

For the first time, kinetic information from the literature was collected and used to construct integrative dynamical mathematical models of sphingolipid metabolism. One model was designed primarily with kinetic equations in the tradition of Michaelis and Menten whereas the other two models were designed as alternative power-law models within the framework of Biochemical Systems Theory. Each model contains about 50 variables, about a quarter of which are dependent (state) variables, while the others are independent inputs and enzyme activities that are considered constant. The models account for known regulatory signals that exert control over the pathway. Standard mathematical testing, repeated revisiting of the literature, and numerous rounds of amendments and refinements resulted in models that are stable and rather insensitive to perturbations in inputs or parameter values. The models also appear to be compatible with the modest amount of experimental experience that lends itself to direct comparisons. Even though the three models are based on different mathematical representations, they show dynamic responses to a variety of perturbations and changes in conditions that are essentially equivalent for small perturbations and similar for large perturbations. The kinetic information used for model construction and the models themselves can serve as a starting point for future analyses and refinements.

Models, Biological↗

PubMed2004

Analysis of dynamic labeling data.

Comprehensive assessments of the organization and regulation of metabolic pathways cannot be limited to steady-state measurements alone but require dynamic time series data. One experimental means of generating such data consists of radioactively labeling precursors and measuring their fate over time. While labeling experiments belong to the standard repertoire of biological laboratory techniques, corresponding mathematical tools for analyzing the non-linear dynamics of tracers are scarce. The article addresses this issue, using Biochemical Systems Theory as the modeling framework. The description of the dynamics of labeled metabolites alone is difficult, but it is demonstrated that these difficulties are easily overcome by setting up dynamic models in two or three blocks, one for the kinetics of the total pools, the second just for the labeled portions, and the third, optional, block for the remaining unlabeled components. Since the dynamic model is not limited in complexity and can account for linear pathways, converging and diverging branches, cycles, and the various observed modes of regulation, the proposed method of non-linear tracer analysis is rather general and permits simulations of most standard labeling experiments, both at steady state and during transients.

Computer Simulation↗

PubMed2004

Yeast sphingolipid metabolism: clues and connections.

This review of sphingolipid metabolism in the budding yeast Saccharomyces cerevisiae contains information on the enzymes and the genes that encode them, as well as connections to other metabolic pathways. Particular attention is given to yeast homologs, domains, and motifs in the sequence, cellular localization of enzymes, and possible protein-protein interactions. Also included are genetic interactions of special interest that provide clues to the cellular biological roles of particular sphingolipid metabolic pathways and specific sphingolipids.

Models, Biological↗