Ursula Kummer

Drug effects are difficult to investigate in detail in vivo. However, a mechanistic understanding of drug action is clearly beneficial for both pharmaceutical development as well as for optimization of treatment designs. We here established a quantitative systems pharmacology (QSP) mouse model which simultaneously describes whole-body pharmacokinetics of murine IFN-α as well as the cellular pharmacodynamic effect through the antiviral response biomarker Mx2. To this end, a dynamic model of intracellular IFN-α signalling in the JAK/STAT pathway was combined with a whole-body physiologically-based pharmacokinetic model of IFN-α in mice. The pharmacodynamic behaviour of the resulting mouse IFN-α QSP model was first compared to a cellular model of the JAK/STAT pathway to compare in vitro and in vivo drug effects and to identify functional differences. It was found that the in vitro drug effect in the cellular model overestimates the in vivo response in mice at least by a factor of two which is partly due to the missing drug clearance in vitro. Also, the drug responses in the in vitro model were time delayed. Interspecies analyses in murine and a previously published human QSP model of IFN-α next show a similar dynamic behavior. However, our models demonstrate eight to 16-fold stronger response levels in mice than in humans due to more efficient interferon binding. Our analysis supports a mechanistic analysis of both upstream pharmacokinetic as well as downstream pharmacodynamic drug effects through the combination of physiological knowledge and quantitative computational models. The study hence shows potential applications for QSP modelling in terms of study planning, for example by choosing physiologically relevant in vitro concentrations. Also, the QSP model allows inter-species comparisons of the effect strength in specific functional readouts, which in humans are otherwise not possible due to the limited sampling possibilities. We expect QSP modelling to play an increasingly important role in drug development and research in the future.

SUMMARY: The decoding of calcium signals by plant calcium‐dependent kinases (CPKs) is not fully understood yet. Based on kinetic in vitro measurements of the activity of several CPK proteins, their individual activity profile was modeled and coupled to cytosolic calcium concentration changes from in vivo measurements of guard cells and epidermal leaf cells. In addition, computationally produced surrogate data were used. It was analyzed how individual activity profiles of CPKs will change in response to the different calcium time courses. In addition, the impact of modeling explicitly individual elementary reaction steps for binding and unbinding in comparison to using a heuristic Hill equation for the binding process was investigated.

Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

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.

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g. food industry, but also in human health. Many LAB genera have developed a multidrug resistance in the past few years, becoming a serious problem in controlling hospital germs all around the world. 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. In this study, we investigated the effect of glutamine auxotrophy (Δ glnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of its natural environment in the human body, but also play a role in food industry. We compared the results to those of the wildtype. Our integrative method, using a genome-scale metabolic model, constrained by metabolic and proteomic data allows us to understand the bigger picture of adaptation strategies in 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 Δ glnA 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 non-specificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models since it affects energy balance.

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.

SUMMARY: The acidification of plant vacuoles is of great importance for various physiological processes, as a multitude of secondary active transporters utilize the proton gradient established across the vacuolar membrane. Vacuolar‐type H⁺‐translocating ATPases and a pyrophosphatase are thought to enable vacuoles to accumulate protons against their electrochemical potential. However, recent studies pointed to the ATPase located at the trans‐Golgi network/early endosome (TGN/EE) to contribute to vacuolar acidification in a manner not understood as of now. Here, we combined experimental data and computational modeling to test different hypotheses for vacuolar acidification mechanisms. For this, we analyzed different models with respect to their ability to describe existing experimental data. To better differentiate between alternative acidification mechanisms, new experimental data have been generated. By fitting the models to the experimental data, we were able to prioritize the hypothesis in which vesicular trafficking of Ca²⁺/H⁺‐antiporters from the TGN/EE to the vacuolar membrane and the activity of ATP‐dependent Ca²⁺‐pumps at the tonoplast might explain the residual acidification observed in Arabidopsis mutants defective in vacuolar proton pump activity. The presented modeling approach provides an integrative perspective on vacuolar pH regulation in Arabidopsis and holds potential to guide further experimental work.

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.