Models of sequestration and receptor cross-talk for explaining multiple mutants in plant stem cell regulation

<p>Abstract</p> <p>Background</p> <p>Stem cells reside in a plant's shoot meristem throughout its life and are main regulators of above-ground plant development. The stem cell maintenance depends on a feedback network between the <it>CLAVATA </it>and <it>WUSCHEL </it>genes. The CLAVATA3 peptide binds to the CLAVATA1 receptor leading to WUSCHEL inhibition. WUSCHEL, on the other hand, activates CLAVATA3 expression. Recent experiments suggest a second pathway where CLAVATA3 inhibits WUSCHEL via the CORYNE receptor pathway. An interesting question, central for understanding the receptor signaling, is why the <it>clavata1-11 </it>null mutant has a weaker phenotype compared with the <it>clavata1-1 </it>non-null mutant. It has been suggested that this relies on interference from the mutated CLAVATA1 acting on the CORYNE pathway.</p> <p>Results</p> <p>We present two models for the CLAVATA-WUSCHEL feedback network including two receptor pathways for WUSCHEL repression and differing only by the hypothesized mechanisms for the <it>clavata1-1 </it>non-null mutant. The first model is an implementation of the previously suggested interference mechanism. The other model assumes an unaltered binding between CLAVATA3 and the mutated CLAVATA1 but with a loss of propagated signal into the cell. We optimize the models using data from wild type and four single receptor mutant experiments and use data from two receptor double mutant experiments in a validation step. Both models are able to explain all seven phenotypes and in addition qualitatively predict CLAVATA3 perturbations. The two models for the <it>clavata1-1 </it>mutant differ in the direct mechanism of the mutant, but they also predict other differences in the dynamics of the stem cell regulating network. We show that the interference hypothesis leads to an abundance of receptors, while the loss-of-signal hypothesis leads to sequestration of CLAVATA3 and relies on degradation or internalization of the bound CLAVATA1 receptor.</p> <p>Conclusions</p> <p>Using computational modeling, we show that an interference hypothesis and a more parsimonious loss-of-signal hypothesis for a <it>clavata1 </it>non-null mutant both lead to behaviors predicting wild type and six receptor mutant experiments. Although the two models have identical implementations of the unperturbed feedback network for stem cell regulation, we can point out model-predicted differences that may be resolved in future experiments.</p

Self-Organization in High-Density Bacterial Colonies: Efficient Crowd Control

Colonies of bacterial cells can display complex collective dynamics, frequently culminating in the formation of biofilms and other ordered super-structures. Recent studies suggest that to cope with local environmental challenges, bacterial cells can actively seek out small chambers or cavities and assemble there, engaging in quorum sensing behavior. By using a novel microfluidic device, we showed that within chambers of distinct shapes and sizes allowing continuous cell escape, bacterial colonies can gradually self-organize. The directions of orientation of cells, their growth, and collective motion are mutually correlated and dictated by the chamber walls and locations of chamber exits. The ultimate highly organized steady state is conducive to a more-organized escape of cells from the chambers and increased access of nutrients into and evacuation of waste out of the colonies. Using a computational model, we suggest that the lengths of the cells might be optimized to maximize self-organization while minimizing the potential for stampede-like exit blockage. The self-organization described here may be crucial for the early stage of the organization of high-density bacterial colonies populating small, physically confined growth niches. It suggests that this phenomenon can play a critical role in bacterial biofilm initiation and development of other complex multicellular bacterial super-structures, including those implicated in infectious diseases

A Cell-Based Model for Quorum Sensing in Heterogeneous Bacterial Colonies

Although bacteria are unicellular organisms, they have the ability to act in concert by synthesizing and detecting small diffusing autoinducer molecules. The phenomenon, known as quorum sensing, has mainly been proposed to serve as a means for cell-density measurement. Here, we use a cell-based model of growing bacterial microcolonies to investigate a quorum-sensing mechanism at a single cell level. We show that the model indeed predicts a density-dependent behavior, highly dependent on local cell-clustering and the geometry of the space where the colony is evolving. We analyze the molecular network with two positive feedback loops to find the multistability regions and show how the quorum-sensing mechanism depends on different model parameters. Specifically, we show that the switching capability of the network leads to more constraints on parameters in a natural environment where the bacteria themselves produce autoinducer than compared to situations where autoinducer is introduced externally. The cell-based model also allows us to investigate mixed populations, where non-producing cheater cells are shown to have a fitness advantage, but still cannot completely outcompete producer cells. Simulations, therefore, are able to predict the relative fitness of cheater cells from experiments and can also display and account for the paradoxical phenomenon seen in experiments; even though the cheater cells have a fitness advantage in each of the investigated groups, the overall effect is an increase in the fraction of producer cells. The cell-based type of model presented here together with high-resolution experiments will play an integral role in a more explicit and precise comparison of models and experiments, addressing quorum sensing at a cellular resolution

Cell signaling: a systems approach

All higher functions of the cell are dependent on cell signaling. It is the way of the cell to obtain information about the world surrounding it. By getting information on e.g. temperature, glucose concentrations, or the density of neighboring cells, the cell can make decisions on how to optimally act given the supplied information. Even though cells are very different, some of the basic mechanisms governing the signaling are the same everywhere – from the most simple single-cellular bacteria to the cells we find in our bodies. In this thesis we will study cell signaling in three different types of cells: in Paper I we study the TGF-β pathway in endothelial mammal cells, in Paper II and Paper III the biofilm formation and quorum sensing of bacterial cells, and in Paper IV plant stem cells. We use a combination of rate-equation models, mechanical cell-based models, and statistical tools to study the dynamics of these networks. By this approach we can find and validate hypotheses in cases where mere biological intuition is not enough. We can also indentify key components and modules of the systems, and predict quantities not yet measured. This provides a work flow where the model is set up to test hypotheses against available data, the model suggests new experiments which later can be used to further improve the model. The approach of combining experimental data with mathematical modeling has proven to be very fruitful for the understanding of many biological systems

A Rate Equation Approach to Elucidate the Kinetics and Robustness of the TGF-β Pathway

We present a rate equation model for the TGF-β pathway in endothelial cells together with novel measurements. This pathway plays a prominent role in inter- and intracellular communication and subversion can lead to cancer, fibrosis vascular disorders, and immune diseases. The model successfully describes the kinetics of experimental data and also correctly predicts the behavior in experiments where the system is perturbed. A novel method in this context, simulated tempering, is used to fit the model parameters to the data. It provides an ensemble of high quality solutions, which are analyzed with clustering methods and display a hierarchical structure highlighting distinct parameter subspaces with biological interpretations. This analysis discriminates between different biological mechanisms to achieve a transient signal from a sustained TGF-β input, where one mechanism is to use a negative feedback to turn the signal off. Further analysis in terms of parameter sensitivity reveals that this negative feedback loop in TGF-β signaling renders the system global robustness. This sheds light upon the role of the Smad7 protein in this system