Influences of Excluded Volume of Molecules on Signaling Processes on Biomembrane

We investigate the influences of the excluded volume of molecules on biochemical reaction processes on 2-dimensional surfaces using a model of signal transduction processes on biomembranes. We perform simulations of the 2-dimensional cell-based model, which describes the reactions and diffusion of the receptors, signaling proteins, target proteins, and crowders on the cell membrane. The signaling proteins are activated by receptors, and these activated signaling proteins activate target proteins that bind autonomously from the cytoplasm to the membrane, and unbind from the membrane if activated. If the target proteins bind frequently, the volume fraction of molecules on the membrane becomes so large that the excluded volume of the molecules for the reaction and diffusion dynamics cannot be negligible. We find that such excluded volume effects of the molecules induce non-trivial variations of the signal flow, defined as the activation frequency of target proteins, as follows. With an increase in the binding rate of target proteins, the signal flow varies by i) monotonically increasing; ii) increasing then decreasing in a bell-shaped curve; or iii) increasing, decreasing, then increasing in an S-shaped curve. We further demonstrate that the excluded volume of molecules influences the hierarchical molecular distributions throughout the reaction processes. In particular, when the system exhibits a large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters, and the target proteins tend to become distributed around such clusters. To explain these phenomena, we analyze the stochastic model of the local motions of molecules around the receptor.Comment: 31 pages, 10 figure

Comparing the Epidermal Growth Factor Interaction with Four Different Cell Lines: Intriguing Effects Imply Strong Dependency of Cellular Context

The interaction of the epidermal growth factor (EGF) with its receptor (EGFR) is known to be complex, and the common over-expression of EGF receptor family members in a multitude of tumors makes it important to decipher this interaction and the following signaling pathways. We have investigated the affinity and kinetics of 125I-EGF binding to EGFR in four human tumor cell lines, each using four culturing conditions, in real time by use of LigandTracer®

Systematic evaluation of pembrolizumab dosing in patients with advanced non-small cell lung cancer

International audienceBACKGROUND: In the phase I KEYNOTE-001 study, pembrolizumab demonstrated durable antitumor activity in patients with advanced non-small cell lung cancer (NSCLC). We sought to characterize the relationship between pembrolizumab dose, exposure, and response to define an effective dose for these patients. METHODS: Patients received pembrolizumab 2 mg/kg every 3 weeks (Q3W) (n=55), 10 mg/kg Q3W (n=238), or 10 mg/kg Q2W (n=156). Response (RECIST v1.1) was assessed every 9 weeks. The relationship between the estimated pembrolizumab area under the concentration-time curve at steady-state over 6 weeks (AUCss-6weeks) and the longitudinal change in tumor size (sum of longest diameters) was analyzed by regression and nonlinear mixed effects modeling. This model was simultaneously fit to all tumor size data, then used to simulate response rates, normalizing the trial data across dose for prognostic covariates (tumor PD-L1 expression and EGFR mutation status). The exposure-safety relationship was assessed by logistic regression of pembrolizumab AUCss-6weeks versus occurrence of adverse events of interest based on their immune etiology. RESULTS: Overall response rates were 15% (95% confidence interval [CI] 7%-28%) at 2 Q3W, 25% (18%-33%) at 10 Q3W, and 21% (95% CI 14% to 30%) at 10 Q2W. Regression analyses of percentage change from baseline in tumor size versus AUCss-6week indicated a flat relationship (regression slope P\textgreater0.05). Simulations showed the exposure-response relationship to be similarly flat, thus indicating that the lowest evaluated dose of 2 mg/kg Q3W to likely be at or near the efficacy plateau. Exposure-safety analysis showed the adverse event incidence to be similar among the clinically tested doses. CONCLUSIONS: No significant exposure dependency on efficacy or safety was identified for pembrolizumab across doses of 2 mg/kg to 10 mg/kg. These results support the use of a 2-mg/kg Q3W dosage in patients with previously treated, advanced NSCLC.ClinicalTrials.gov registry: NCT0129582

Unified regression model of binding equilibria in crowded environments

Molecular crowding is a critical feature distinguishing intracellular environments from idealized solution-based environments and is essential to understanding numerous biochemical reactions, from protein folding to signal transduction. Many biochemical reactions are dramatically altered by crowding, yet it is extremely difficult to predict how crowding will quantitatively affect any particular reaction systems. We previously developed a novel stochastic off-lattice model to efficiently simulate binding reactions across wide parameter ranges in various crowded conditions. We now show that a polynomial regression model can incorporate several interrelated parameters influencing chemistry under crowded conditions. The unified model of binding equilibria accurately reproduces the results of particle simulations over a broad range of variation of six physical parameters that collectively yield a complicated, non-linear crowding effect. The work represents an important step toward the long-term goal of computationally tractable predictive models of reaction chemistry in the cellular environment

High- and Low-Affinity Epidermal Growth Factor Receptor-Ligand Interactions Activate Distinct Signaling Pathways

Signaling mediated by the Epidermal Growth Factor Receptor (EGFR) is crucial in normal development, and aberrant EGFR signaling has been implicated in a wide variety of cancers. Here we find that the high- and low-affinity interactions between EGFR and its ligands activate different signaling pathways. While high-affinity ligand binding is sufficient for activation of most canonical signaling pathways, low-affinity binding is required for the activation of the Signal transducers and activators of transcription (Stats) and Phospholipase C-gamma 1 (PLCγ1). As the Stat proteins are involved in many cellular responses including proliferation, migration and apoptosis, these results assign a function to low-affinity interactions that has been omitted from computational models of EGFR signaling. The existence of receptors with distinct signaling properties provides a way for EGFR to respond to different concentrations of the same ligand in qualitatively different ways

Monitoring the Size and Lateral Dynamics of ErbB1 Enriched Membrane Domains through Live Cell Plasmon Coupling Microscopy

To illuminate the role of the spatial organization of the epidermal growth factor receptor (ErbB1) in signal transduction quantitative information about the receptor topography on the cell surface, ideally on living cells and in real time, are required. We demonstrate that plasmon coupling microscopy (PCM) enables to detect, size, and track individual membrane domains enriched in ErbB1 with high temporal resolution. We used a dendrimer enhanced labeling strategy to label ErbB1 receptors on epidermoid carcinoma cells (A431) with 60 nm Au nanoparticle (NP) immunolabels under physiological conditions at 37°C. The statistical analysis of the spatial NP distribution on the cell surface in the scanning electron microscope (SEM) confirmed a clustering of the NP labels consistent with a heterogeneous distribution of ErbB1 in the plasma membrane. Spectral shifts in the scattering response of clustered NPs facilitated the detection and sizing of individual NP clusters on living cells in solution in an optical microscope. We tracked the lateral diffusion of individual clusters at a frame rate of 200 frames/s while simultaneously monitoring the configurational dynamics of the clusters. Structural information about the NP clusters in their membrane confinements were obtained through analysis of the electromagnetic coupling of the co-confined NP labels through polarization resolved PCM. Our studies show that the ErbB1 receptor is enriched in membrane domains with typical diameters in the range between 60–250 nm. These membrane domains exhibit a slow lateral diffusion with a diffusion coefficient of  = |0.0054±0.0064| µm2/s, which is almost an order of magnitude slower than the mean diffusion coefficient of individual NP tagged ErbB1 receptors under identical conditions

A comparison of approximation techniques for variance-based sensitivity analysis of biochemical reaction systems

<p>Abstract</p> <p>Background</p> <p>Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of biochemical reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large biochemical reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by biochemical reaction systems that consist of a large number of reactions and molecular species.</p> <p>Results</p> <p>We present four techniques, derivative approximation (DA), polynomial approximation (PA), Gauss-Hermite integration (GHI), and orthonormal Hermite approximation (OHA), for <it>analytically </it>approximating the variance-based sensitivity indices associated with a biochemical reaction system. By using a well-known model of the mitogen-activated protein kinase signaling cascade as a case study, we numerically compare the approximation quality of these techniques against traditional Monte Carlo sampling. Our results indicate that, although DA is computationally the most attractive technique, special care should be exercised when using it for sensitivity analysis, since it may only be accurate at low levels of uncertainty. On the other hand, PA, GHI, and OHA are computationally more demanding than DA but can work well at high levels of uncertainty. GHI results in a slightly better accuracy than PA, but it is more difficult to implement. OHA produces the most accurate approximation results and can be implemented in a straightforward manner. It turns out that the computational cost of the four approximation techniques considered in this paper is orders of magnitude smaller than traditional Monte Carlo estimation. Software, coded in MATLAB^®, which implements all sensitivity analysis techniques discussed in this paper, is available free of charge.</p> <p>Conclusions</p> <p>Estimating variance-based sensitivity indices of a large biochemical reaction system is a computationally challenging task that can only be addressed via approximations. Among the methods presented in this paper, a technique based on orthonormal Hermite polynomials seems to be an acceptable candidate for the job, producing very good approximation results for a wide range of uncertainty levels in a fraction of the time required by traditional Monte Carlo sampling.</p

Mathematical modeling of intracellular signaling pathways

Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems

Single-molecule analysis of epidermal growth factor binding on the surface of living cells

Global cellular responses induced by epidermal growth factor (EGF) receptor (EGFR) occur immediately with a less than 1% occupancy among tens of thousands of EGFR molecules on single cell surface. Activation of EGFR requires the formation of a signaling dimer of EGFR bound with a single ligand to each molecule. How sufficient numbers of signaling dimers are formed at such low occupancy rate is still not known. Here, we have analyzed the kinetics of EGF binding and the formation of the signaling dimer using single-molecule imaging and mathematical modeling. A small number of EGFR on the cell surface formed dimeric binding sites, which bound EGF two orders of magnitude faster than the monomeric binding sites. There was a positive cooperative binding of EGF to the dimeric binding sites through a newly discovered kinetic intermediate. These two mechanisms facilitate the formation of signaling dimers of EGF/EGFR complexes