Plasmodesmal receptor-like kinases identified through analysis of rice cell wall extracted proteins

In plants, plasmodesmata (PD) are intercellular channels that function in both metabolite exchange and the transport of proteins and RNAs. Currently, many of the PD structural and regulatory components remain to be elucidated. Receptor-like kinases (RLKs) belonging to a notably expanded protein family in plants compared to the animal kingdom have been shown to play important roles in plant growth, development, pathogen resistance, and cell death. In this study, cell biological approaches were used to identify potential PD-associated RLK proteins among proteins contained within cell walls isolated from rice callus cultured cells. A total of 15 rice RLKs were investigated to determine their subcellular localization, using an Agrobacterium-mediated transient expression system. Of these six PD-associated RLKs were identified based on their co-localization with a viral movement protein that served as a PD marker, plasmolysis experiments, and subcellular localization at points of wall contact between spongy mesophyll cells. These findings suggest potential PD functions in apoplasmic signaling in response to environmental stimuli and developmental inputs

Robust simplifications of multiscale biochemical networks

<p>Abstract</p> <p>Background</p> <p>Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.</p> <p>Results</p> <p>We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-<it>κ</it>B pathway.</p> <p>Conclusion</p> <p>Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.</p