Robustness in Regulatory Networks: A Multi-Disciplinary Approach

We give in this paper indications about the dynamical impact (as phenotypic changes) coming from the main sources of perturbation in biological regulatory networks. First, we define the boundary of the interaction graph expressing the regulations between the main elements of the network (genes, proteins, metabolites, ...). Then, we search what changes in the state values on the boundary could cause some changes of states in the core of the system (robustness to boundary conditions). After, we analyse the role of the mode of updating (sequential, block sequential or parallel) on the asymptotics of the network, essentially on the occurrence of limit cycles (robustness to updating methods). Finally, we show the influence of some topological changes (e.g. suppression or addition of interactions) on the dynamical behaviour of the system (robustness to topology perturbations).     

Comparative Evolutionary Analysis of Cell Cycle Proteins Networks in Fission and Budding Yeast

Fission yeast and budding yeast are the two distantly related species with common ancestors. Various studies have shown significant differences in metabolic networks and regulatory networks. Cell cycle regulatory proteins in both species have differences in structural as well as in functional organization. Orthologous proteins in cell cycle regulatory protein networks seem to play contemporary role in both species during the evolution but little is known about non-orthologous proteins. Here, we used system biology approach to compare topological parameters of orthologous and non-orthologous proteins to find their contributions during the evolution to make an efficient cell cycle regulation. Observed results have shown a significant role of non-orthologous proteins in fission yeast in maintaining the efficiency of cell cycle regulation with less number of proteins as compared to budding yeast.     

Timing Robustness in the Budding and Fission Yeast Cell Cycles

Robustness of biological models has emerged as an important principle in systems biology. Many past analyses of Boolean models update all pending changes in signals simultaneously (i.e., synchronously), making it impossible to consider robustness to variations in timing that result from noise and different environmental conditions. We checked previously published mathematical models of the cell cycles of budding and fission yeast for robustness to timing variations by constructing Boolean models and analyzing them using model-checking software for the property of speed independence. Surprisingly, the models are nearly, but not totally, speed-independent. In some cases, examination of timing problems discovered in the analysis exposes apparent inaccuracies in the model. Biologically justified revisions to the model eliminate the timing problems. Furthermore, in silico random mutations in the regulatory interactions of a speed-independent Boolean model are shown to be unlikely to preserve speed independence, even in models that are otherwise functional, providing evidence for selection pressure to maintain timing robustness. Multiple cell cycle models exhibit strong robustness to timing variation, apparently due to evolutionary pressure. Thus, timing robustness can be a basis for generating testable hypotheses and can focus attention on aspects of a model that may need refinement.     

Testing a Mathematical Model of the Yeast Cell Cycle

We derived novel, testable predictions from a mathematical model of the budding yeast cell cycle. A key qualitative prediction of bistability was confirmed in a strain simultaneously lacking cdc14 and G1 cyclins. The model correctly predicted quantitative dependence of cell size on gene dosage of the G1 cyclin CLN3, but it incorrectly predicted strong genetic interactions between G1 cyclins and the anaphase-promoting complex specificity factor Cdh1. To provide constraints on model generation, we determined accurate concentrations for the abundance of all nine cyclins as well as the inhibitor Sic1 and the catalytic subunit Cdc28. For many of these we determined abundance throughout the cell cycle by centrifugal elutriation, in the presence or absence of Cdh1. In addition, perturbations to the Clb-kinase oscillator were introduced, and the effects on cyclin and Sic1 levels were compared between model and experiment. Reasonable agreement was obtained in many of these experiments, but significant experimental discrepancies from the model predictions were also observed. Thus, the model is a strong but incomplete attempt at a realistic representation of cell cycle control. Constraints of the sort developed here will be important in development of a truly predictive model.     

Ergodic Sets as Cell Phenotype of Budding Yeast Cell Cycle

It has been suggested that irreducible sets of states in Probabilistic Boolean Networks correspond to cellular phenotype. In this study, we identify such sets of states for each phase of the budding yeast cell cycle. We find that these “ergodic sets” underly the cyclin activity levels during each phase of the cell cycle. Our results compare to the observations made in several laboratory experiments as well as the results of differential equation models. Dynamical studies of this model: (i) indicate that under stochastic external signals the continuous oscillating waves of cyclin activity and the opposing waves of CKIs emerge from the logic of a Boolean-based regulatory network without the need for specific biochemical/kinetic parameters; (ii) suggest that the yeast cell cycle network is robust to the varying behavior of cell size (e.g., cell division under nitrogen deprived conditions); (iii) suggest the irreversibility of the Start signal is a function of logic of the G1 regulon, and changing the structure of the regulatory network can render start reversible.     

芽殖酵母细胞周期的检查点调控

芽殖酵母是研究真核细胞的模式菌。细胞周期检查点是确保细胞周期正常运行的一种调控机制。就芽殖酵母细胞周期检查点调控加以介绍。     

Use of Yeast Populations Fractionated by Zonal Centrifugation to Study the Cell Cycle

Zonal centrifugation in a sucrose density gradient was used to separate yeast cells primarily by size and thus by age in the cell cycle. This approach provides an alternative to synchronous growth for examining the properties of cells at different stages in the cell cycle.     

Improved Flow Cytometric Analysis of the Budding Yeast Cell Cycle

The budding yeast, Saccharomyces cerevisiae has been a remarkably useful model system for the study of eukaryotic cell cycle regulation. Flow cytometric analysis of DNA content in budding yeast has become a standard tool for the analysis of cell cycle progression. However, popular protocols utilizing the DNA binding dye, propidium iodide, suffer from a number of drawbacks that confound accurate analysis by flow cytometry. Here we show the utility of the DNA binding dye, SYTOX Green, in the cell cycle analysis of yeast. Samples analyzed using SYTOX Green exhibited better coefficients of variation, improved linearity between DNA content and fluorescence, and decreased peak drift associated with changes in dye concentration, growth conditions or cell size.

Key Words:

Flow cytometry, Cell cycle, Saccharomyces cerevisiae, SYTOX Green, Propidium iodide     

Dynamical Modeling of the Cell Cycle and Cell Fate Emergence in Caulobacter crescentus

The division of Caulobacter crescentus, a model organism for studying cell cycle and differentiation in bacteria, generates two cell types: swarmer and stalked. To complete its cycle, C. crescentus must first differentiate from the swarmer to the stalked phenotype. An important regulator involved in this process is CtrA, which operates in a gene regulatory network and coordinates many of the interactions associated to the generation of cellular asymmetry. Gaining insight into how such a differentiation phenomenon arises and how network components interact to bring about cellular behavior and function demands mathematical models and simulations. In this work, we present a dynamical model based on a generalization of the Boolean abstraction of gene expression for a minimal network controlling the cell cycle and asymmetric cell division in C. crescentus. This network was constructed from data obtained from an exhaustive search in the literature. The results of the simulations based on our model show a cyclic attractor whose configurations can be made to correspond with the current knowledge of the activity of the regulators participating in the gene network during the cell cycle. Additionally, we found two point attractors that can be interpreted in terms of the network configurations directing the two cell types. The entire network is shown to be operating close to the critical regime, which means that it is robust enough to perturbations on dynamics of the network, but adaptable to environmental changes.     

Regulatory Roles of Metabolites in Cell Signaling Networks

Mounting evidence suggests that cellular metabolites, in addition to being sources of fuel and macromolecular substrates, are actively involved in signaling and epigenetic regulation. Many metabolites, such as cyclic AMP, which regulates phosphorylation/dephosphorylation, have been identified to modulate DNA and histone methylation and protein stability. Metabolite-driven cellular regulation occurs through two distinct mechanisms: proteins allosterically bind or serve as substrates for protein signaling pathways, and metabolites covalently modify proteins to regulate their functions. Such novel protein metabolites include fumarate, succinyl-CoA, propionyl-CoA, butyryl-CoA and crontonyl-CoA. Other metabolites, including α-ketoglutarate, succinate and fumarate, regulate epigenetic processes and cell signaling via protein binding. Here, we summarize recent progress in metabolite-derived post-translational protein modification and metabolite-binding associated signaling regulation. Uncovering metabolites upstream of cell signaling and epigenetic networks permits the linkage of metabolic disorders and human diseases, and suggests that metabolite modulation may be a strategy for innovative therapeutics and disease prevention techniques.     

Cell Type Specific Alterations in Interchromosomal Networks across the Cell Cycle

The interchromosomal organization of a subset of human chromosomes (#1, 4, 11, 12, 16, 17, and 18) was examined in G1 and S phase of human WI38 lung fibroblast and MCF10A breast epithelial cells. Radial positioning of the chromosome territories (CTs) was independent of gene density, but size dependent. While no changes in radial positioning during the cell cycle were detected, there were stage-specific differences between cell types. Each CT was in close proximity (interaction) with a similar number of other CT except the gene rich CT17 which had significantly more interactions. Furthermore, CT17 was a member of the highest pairwise CT combinations with multiple interactions. Major differences were detected in the pairwise interaction profiles of MCF10A versus WI38 including cell cycle alterations from G1 to S. These alterations in interaction profiles were subdivided into five types: overall increase, overall decrease, switching from 1 to ≥2 interactions, vice versa, or no change. A global data mining program termed the chromatic median determined the most probable overall association network for the entire subset of CT. This probabilistic interchromosomal network was nearly completely different between the two cell lines. It was also strikingly altered across the cell cycle in MCF10A, but only slightly in WI38. We conclude that CT undergo multiple and preferred interactions with other CT in the nucleus and form preferred -albeit probabilistic- interchromosomal networks. This network of interactions is altered across the cell cycle and between cell types. It is intriguing to consider the relationship of these alterations to the corresponding changes in the gene expression program across the cell cycle and in different cell types.