Systems Level Modeling of the Cell Cycle Using Budding Yeast

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism

Lipopolysaccharide enhances FcγR-dependent functions in vivo through CD11b/CD18 up-regulation

Fc receptors for immunoglobulin G (IgG) (FcγR) mediate several defence mechanisms in the course of inflammatory and infectious diseases. In Gram-negative infections, cellular wall lipopolysaccharides (LPS) modulate different immune responses. We have recently demonstrated that murine LPS in vivo treatment significantly increases FcγR-dependent clearance of immune complexes (IC). In addition, we and others have reported the induction of adhesion molecules on macrophages and neutrophils by LPS in vivo and by tumour necrosis factor-α (TNF-α) in vitro. The aim of this paper was to investigate CD11b/CD18 participation in LPS enhancing effects on Fcγ-dependent functionality of tissue macrophages. Our results have demonstrated that LPS can enhance antibody-dependent cellular cytotoxicity (ADCC) and IC-triggered cytotoxicity (IC-Ctx), two reactions which involve the Fcγ-receptor but different lytic mechanisms. In vitro incubation of splenocytes from LPS-treated mice with anti-CD11b/CD18 abrogated ADCC and IC-Ctx enhancement, without affecting FcγR expression. Similar results were obtained with physiological concentrations of fibrinogen. In this way cytotoxic values of LPS-splenocytes decreased to the basal levels of control mice. Time and temperature requirements for such inhibition strongly suggested that anti-CD11b/CD18 could modulate intracellular signals leading to downregulation of FcγR functionality. Data presented herein support the hypothesis that functional and/or physical associations between integrins and FcγR could be critical for the modulation of effector functions during an inflammatory response

Approaches to Biosimulation of Cellular Processes

Modelling and simulation are at the heart of the rapidly developing field of systems biology. This paper reviews various types of models, simulation methods, and theoretical approaches that are presently being used in the quantitative description of cellular processes. We first describe how molecular interaction networks can be represented by means of stoichiometric, topological and kinetic models. We briefly discuss the formulation of kinetic models using mesoscopic (stochastic) or macroscopic (continuous) approaches, and we go on to describe how detailed models of molecular interaction networks (silicon cells) can be constructed on the basis of experimentally determined kinetic parameters for cellular processes. We show how theory can help in analyzing models by applying control analysis to a recently published silicon cell model. Finally, we review some of the theoretical approaches available to analyse kinetic models and experimental data, respectively