Computational modeling of cambium activity provides a regulatory framework for simulating radial plant growth.

Precise organization of growing structures is a fundamental process in developmental biology. In plants, radial growth is mediated by the cambium, a stem cell niche continuously producing wood (xylem) and bast (phloem) in a strictly bidirectional manner. While this process contributes large parts to terrestrial biomass, cambium dynamics eludes direct experimental access due to obstacles in live-cell imaging. Here, we present a cell-based computational model visualizing cambium activity and integrating the function of central cambium regulators. Performing iterative comparisons of plant and model anatomies, we conclude that the receptor-like kinase PXY and its ligand CLE41 are part of a minimal framework sufficient for instructing tissue organization. By integrating tissue-specific cell wall stiffness values, we moreover probe the influence of physical constraints on tissue geometry. Our model highlights the role of intercellular communication within the cambium and shows that a limited number of factors are sufficient to create radial growth by bidirectional tissue production.

Computational modeling and quantitative physiology reveal central parameters for brassinosteroid-regulated early cell physiological processes linked to elongation growth of the Arabidopsis root.

Brassinosteroids (BR) are key hormonal regulators of plant development. However, whereas the individual components of BR perception and signaling are well characterized experimentally, the question of how they can act and whether they are sufficient to carry out the critical function of cellular elongation remains open. Here, we combined computational modeling with quantitative cell physiology to understand the dynamics of the plasma membrane (PM)-localized BR response pathway during the initiation of cellular responses in the epidermis of the Arabidopsis root tip that are be linked to cell elongation. The model, consisting of ordinary differential equations, comprises the BR-induced hyperpolarization of the PM, the acidification of the apoplast and subsequent cell wall swelling. We demonstrate that the competence of the root epidermal cells for the BR response predominantly depends on the amount and activity of H+-ATPases in the PM. The model further predicts that an influx of cations is required to compensate for the shift of positive charges caused by the apoplastic acidification. A potassium channel was subsequently identified and experimentally characterized, fulfilling this function. Thus, we established the landscape of components and parameters for physiological processes potentially linked to cell elongation, a central process in plant development.

All Driven by Energy Demand? Integrative Comparison of Metabolism of Enterococcus faecalis Wildtype and a Glutamine Synthase Mutant.

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g., food industry and also in human health. Many LAB genera have developed a multidrug resistance in the past few years, causing a serious problem in controlling hospital germs worldwide. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important to promote the development of new therapeutic approaches. In this study, we investigated the effect of glutamine auxotrophy (ΔglnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of the organism's natural environment in the human body and play a role in the food industry. We compared the results with those of the wildtype. Using a genome-scale metabolic model constrained by metabolic and proteomic data, our integrative method allows us to understand the bigger picture of the adaptation strategies of this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that ΔglnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the nonspecificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models because it affects energy balance. IMPORTANCE The integration of new pH-dependent experimental data on metabolic uptake and release fluxes, as well as of proteome data with a genome-scale computational model of a glutamine synthetase mutant of E. faecalis is used and compared with those of the wildtype to understand why glutamine auxotrophy results in a less efficient metabolism and how-in comparison with the wildtype-the glutamine synthetase knockout impacts metabolic adjustments during acidification or simply exposure to lower pH. We show that forced glutamine auxotrophy causes more energy demand and that this is likely due to a disregulated glutamine uptake. Proteome changes during acidification observed for the mutant resemble those of the wildtype with the exception of glycolysis-related genes, as the mutant is already energetically stressed at a higher pH and the respective proteome changes were in effect.

Impact of explicit area scaling on kinetic models involving multiple compartments.

BACKGROUND: Computational modelling of cell biological processes is a frequently used technique to analyse the underlying mechanisms and to generally understand the behaviour of these processes in the context of a pathway, network or even the whole cell. The most common technique in this context is the usage of ordinary differential equations that describe the kinetics of the relevant processes in mechanistic detail. Here, it is usually assumed that the content of the cell is well-stirred and thus homogeneous - which is of course an over-simplification, but often worked in the past. However, many processes happen at membranes and thus not in 3D, but in 2D. The scaling of the rates of these processes poses a special problem, if volumes of compartments are changed. They will typically scale with an area, but not with the volume of the involved compartment. However, commonly, this is neglected when setting up models and/or volume scaling also sometimes automatically happens when using modelling software in the field. RESULTS: Here, we investigate generic as well as specific, realistic cases to find out, how strong the impact of the wrong scaling is for the outcome of simulations. We show that the importance of correct area scaling depends on the architecture of the reaction site and its changes upon volume alterations and it is hard to foresee, if it has a significant impact or not just by looking at the original model set-up. Moreover, scaled rates might exhibit more or less control over the behaviour of the system and therefore, accordingly, incorrect scaling will have more or less influence. CONCLUSIONS: Working with multi-compartment reactions requires a careful consideration of the correct scaling of the rates when changing the volumes of the involved compartments. The error following incorrect scaling - often done by scaling with the volume of the respective compartments can lead to significant aberrations of model behaviour.

Specifying the role of BAK1-interacting receptor-like kinase 3 in brassinosteroid signaling.

Brassinosteroids (BR) are involved in the control of several developmental processes ranging from root elongation to senescence and adaptation to environmental cues. Thus, BR perception and signaling have to be precisely regulated. One regulator is BRI1-associated kinase 1 (BAK1)-interacting receptor-like kinase 3 (BIR3). In the absence of BR, BIR3 forms complexes with BR insensitive 1 (BRI1) and BAK1. However, the biophysical and energetic requirements for complex formation in the absence of the ligand have yet to be determined. Using computational modeling, we simulated the potential complexes between the cytoplasmic domains of BAK1, BRI1 and BIR3. Our calculations and experimental data confirm the interaction of BIR3 with BAK1 and BRI1, with the BAK1 BIR3 interaction clearly favored. Furthermore, we demonstrate that BIR3 and BRI1 share the same interaction site with BAK1. This suggests a competition between BIR3 and BRI1 for binding to BAK1, which results in preferential binding of BIR3 to BAK1 in the absence of the ligand thereby preventing the active participation of BAK1 in BR signaling. Our model also suggests that BAK1 and BRI1 can interact even while BAK1 is in complex with BIR3 at an additional binding site of BAK1 that does not allow active BR signaling.

Robustness of frequency vs. amplitude coding of calcium oscillations during changing temperatures.

Intracellular calcium oscillations have been widely studied. It is assumed that information is conveyed in the frequency, amplitude and shape of these oscillations. In particular, calcium signalling in mammalian liver cells has repeatedly been reported to display frequency coding so that an increasing amount of stimulus is translated into an increasing frequency of the oscillations. However, recently, we have shown that calcium oscillations in fish liver cells rather exhibit amplitude coding with increasing stimuli being translated into increasing amplitudes. Practical consequences of this difference are unknown so far. Here we investigated advantages and disadvantages of frequency vs. amplitude coding, in particular in environments with substantially changing temperatures (e.g. 10-20 degrees). For this purpose, we use computational modelling and a new approach to generate a calcium model exactly displaying a specific frequency and/or amplitude. We conclude that despite the advantages in flexibility that frequencies might offer for the transmission of information in the cell, amplitude coding is obviously more robust with respect to changes in environmental temperatures. This potentially explains the observed differences between two classes of organisms, one operating at constant temperatures whereas the other is not.

Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis.

Genome-scale metabolic models represent the entirety of metabolic reactions of an organism based on the annotation of the respective genome. These models commonly allow all reactions to proceed concurrently, disregarding the fact that at no point all proteins will be present in a cell. The metabolic reaction space can be constrained to a more physiological state using experimentally obtained information on enzyme abundances. However, high-quality, genome-wide protein measurements have been challenging and typically transcript abundances have been used as a surrogate for protein measurements. With recent developments in mass spectrometry-based proteomics, exemplified by SWATH-MS, the acquisition of highly quantitative proteome-wide data at reasonable throughput has come within reach. Here we present methodology to integrate such proteome-wide data into genome-scale models. We applied this methodology to study cellular changes in Enterococcus faecalis during adaptation to low pH. Our results indicate reduced proton production in the central metabolism and decreased membrane permeability for protons due to different membrane composition. We conclude that proteomic data constrain genome-scale models to a physiological state and, in return, genome-scale models are useful tools to contextualize proteomic data.

Using a genome-scale metabolic model of Enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism.

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.