Biomedical subjects

Eberhard O Voit

Publications and source records attributed to Eberhard O Voit.

24 records · Page 2Linked to original sources

PubMed2003

Biochemical and genomic regulation of the trehalose cycle in yeast: review of observations and canonical model analysis.

The physiological hallmark of heat-shock response in yeast is a rapid, enormous increase in the concentration of trehalose. Normally found in growing yeast cells and other organisms only as traces, trehalose becomes a crucial protector of proteins and membranes against a variety of stresses, including heat, cold, starvation, desiccation, osmotic or oxidative stress, and exposure to toxicants. Trehalose is produced from glucose 6-phosphate and uridine diphosphate glucose in a two-step process, and recycled to glucose by trehalases. Even though the trehalose cycle consists of only a few metabolites and enzymatic steps, its regulatory structure and operation are surprisingly complex. The article begins with a review of experimental observations on the regulation of the trehalose cycle in yeast and proposes a canonical model for its analysis. The first part of this analysis demonstrates the benefits of the various regulatory features by means of controlled comparisons with models of otherwise equivalent pathways lacking these features. The second part elucidates the significance of the expression pattern of the trehalose cycle genes in response to heat shock. Interestingly, the genes contributing to trehalose formation are up-regulated to very different degrees, and even the trehalose degrading trehalases show drastically increased activity during heat-shock response. Again using the method of controlled comparisons, the model provides rationale for the observed pattern of gene expression and reveals benefits of the counterintuitive trehalase up-regulation.

Feedback, Physiological↗

PubMed2003

Design principles and operating principles: the yin and yang of optimal functioning.

Metabolic engineering has as a goal the improvement of yield of desired products from microorganisms and cell lines. This goal has traditionally been approached with experimental biotechnological methods, but it is becoming increasingly popular to precede the experimental phase by a mathematical modeling step that allows objective pre-screening of possible improvement strategies. The models are either linear and represent the stoichiometry and flux distribution in pathways or they are non-linear and account for the full kinetic behavior of the pathway, which is often significantly effected by regulatory signals. Linear flux analysis is simpler and requires less input information than a full kinetic analysis, and the question arises whether the consideration of non-linearities is really necessary for devising optimal strategies for yield improvements. The article analyzes this question with a generic, representative pathway. It shows that flux split ratios, which are the key criterion for linear flux analysis, are essentially sufficient for unregulated, but not for regulated branch points. The interrelationships between regulatory design on one hand and optimal patterns of operation on the other suggest the investigation of operating principles that complement design principles, like a user's manual complements the hardwiring of electronic equipment.

Biotechnology↗

PubMed2003

Neural-network-based parameter estimation in S-system models of biological networks.

The genomic and post-genomic eras have been blessing us with overwhelming amounts of data that are of increasing quality. The challenge is that most of these data alone are mere snapshots of the functioning organism and do not reveal the organizational structure of which the particular genes and metabolites are contributors. To gain an appreciation of their roles and functions within cells and organisms, genomic and metabolic data need to be integrated in systems models that allow the testing of hypotheses, generate experimentally testable predictions, and ultimately lead to true explanations. One type of data that is particularly well suited for such integration consists of time profiles, which show gene activities, metabolite concentrations, or protein prevalences at dense series of time points. We show with a specific example how such time series can be analyzed and evaluated, if some structural information about the data is available, even if this information is incomplete. The method consists of three components. The first is a particularly suitable mathematical modeling framework, namely Biochemical Systems Theory, in which parameters are direct indicators of the organization of the underlying phenomenon, the second is the training of an artificial neural network for data smoothing and complementation, and the third is a technique for reinterpreting differential equations in a fashion that facilitates parameter estimation. A prototype webtool for these analyses is available at https://bioinformatics.musc.edu/webmetabol/.

Biology↗

PubMed2002

Metabolic modeling: a tool of drug discovery in the post-genomic era.

The Human Genome Project, DNA microarrays, proteome chips and metabolic profiles, among others, are generating unprecedented amounts of valuable information about the genetic and metabolic responses of organisms to stimuli. This wealth of information poses a great scientific challenge, namely the development of novel, effective methods for functional analysis and interpretation. It is proposed here that biomathematical systems models must accompany data generation and management. A particularly effective framework for this purpose is canonical modeling based on Biochemical Systems Theory. The key concept of this theory is the formulation of biological phenomena as systems of differential equations, in which all processes are represented as products of power-law functions.

Biochemical Phenomena↗

PubMed2002

Probabilistic risk assessment of reproductive effects of polychlorinated biphenyls on bottlenose dolphins (Tursiops truncatus) from the Southeast United States Coast.

High levels of polychlorinated biphenyls (PCBs) have been reported in the tissues of some species of marine mammals. The high concentrations are of concern because a growing body of experimental evidence links PCBs to deleterious effects on reproduction, endocrine homeostasis, and immune system function. Much of the recent research has focused on determining the exposure of marine mammal populations to PCBs, but very little effort has been devoted to the actual risk assessments that are needed to determine the expected impacts of the documented exposures. We describe a novel risk assessment approach that integrates measured tissue concentrations of PCBs with a surrogate dose-response relationship and leads to predictions of health risks for marine mammals as well as to the uncertainties associated with these predictions. Specifically, we use PCB tissue residue data from three populations of bottlenose dolphins (Tursiops truncatus), study the feasibility of published dose-response data from a surrogate species. and combine this information to estimate the risk of detrimental reproductive effects in female dolphins. Our risk analyses for dolphin populations near Beaufort (NC, USA), Sarasota (FL, USA), and Matagorda Bay (TX, USA) indicate a high likelihood that reproductive success, primarily in primiparous females, is being severely impaired by chronic exposure to PCBs. Excess risk of reproductive failure, measured in terms of stillbirth or neonatal mortality, for primiparous females was estimated as 60% (Beaufort), 79% (Sarasota), and 78% (Matagorda Bay). Females of higher parity, which have previously off-loaded a majority of their PCB burden, exhibit a much lower risk.

Animals↗

PubMedDate not supplied

Models-of-data and models-of-processes in the post-genomic era.

As we are entering the post-genomic era, models-of-data, such as mining and filtering methods for gene sequences and microarrays and the clustering of co-expressed genes, must be complemented with models-of-processes that explain relationships between genomic information and phenomena at biochemical and physiological levels. Many of these models will have the structure of compartment models, whose conceptualization, identification and analysis will fundamentally benefit from the seminal work of John Jacquez. The article indicates with three vignettes that non-linear compartment models in the formulation of biochemical systems theory are viable candidates for post-genomic models-of-processes.

Animals↗