Modeling steric effects in antibody aggregation using rule-based methods

The allergic response is produced by the release of immune mediators by mast cells and basophils. This process, in turn, is initiated by the aggregation of antigens and IgE-FcεRI antibody-receptor complexes. Computational modeling of antibody- antigen aggregate formation as well as the size and structure of these aggregates is an important tool for greater understanding of the allergic response. In addition, the incorporation of molecular geometry into aggregation models can more accurately capture details of the aggregation process, and may lead to insights into how geom- etry affects aggregate formation. However, it is challenging to simulate aggregation due to the computational cost of simulating large molecules. Methods to geometri- cally model antibody aggregation inspired by rigid body robotic motion simulations have previously been developed; however, high computational cost mandates that the resolution of the 3D molecular models be reduced, which affects the results of the simulation. Rule-based modeling can be used to model aggregation with low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. In this work, we propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and the binding rate constant associated with each rule. We demonstrate how the set of rules is constructed accord- ing to the curvature of the molecule. We then perform a study of antigen-antibody aggregation using our proposed method combined with a previously developed 3D rigid-body Monte Carlo simulation. We first simulate the binding of IgE antibodies bound to cell surface receptors FcεRI to various binding regions of the allergen Pen a 1 using the aforementioned Monte Carlo simulation, and we analyze the distribution of the sizes of the aggregates that form during the simulation. Then, using our novel rule-based approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. In particular, we use the distances between the binding regions of the Pen a 1 molecule to optimize the rules and associated binding rate constants. The optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that IgE-FcεRI receptor complexes will bind to these regions. In ad- dition, the optimized rule-based models provide a means of quantifying the variation in aggregate size distribution that results from differences in molecular geometry. We perform this procedure for seven resolutions and three molecular conforma- tions of Pen a 1. We then analyze the impact of resolution and conformation on the aggregate size distribution and on the optimal rule-based model. In addition, we develop a predictive model by first fixing the rule set and varying only the binding rate constant for each resolution, and then fitting the resulting data to a function. This model is intended to enable the prediction of the aggregate size distribution for higher resolutions while requiring only data for lower resolution Monte Carlo models, thus enhancing computational efficiency. Finally, we use a simple rule-based model to fit experimental cell degranulation data for various concentrations of the shrimp allergen Pen a 1 and the IgE antibody

Interspecies differences in protein expression do not impact the spatiotemporal regulation of glycoprotein VI mediated activation

Background Accurate protein quantification is a vital prerequisite for generating meaningful predictions when using systems biology approaches, a method that is increasingly being used to unravel the complexities of sub cellular interactions and as part of the drug discovery process. Quantitative proteomics, flow cytometry and western blotting have been extensively used to define human platelet protein copy numbers, yet for mouse platelets, a model widely used for platelet research, evidence is largely limited to a single proteomic dataset in which the total amount of proteins were generally comparatively higher than those found in human platelets. Objectives To investigate the functional implications of discrepancies between levels of mouse and human proteins in the GPVI signalling pathway using a systems pharmacology model of GPVI Methods The protein copy number of mouse platelet receptors was determined using flow cytometry. The Virtual Platelet, a mathematical model of Glycoprotein VI (GPVI) signalling, was used to determine the consequences of protein copy number differences observed between human and mouse platelets. Results and conclusion Despite the small size of mouse platelets compared to human platelets they possessed a greater density of surface receptors alongside a higher concentration of intracellular signalling proteins. Surprisingly the predicted temporal profile of Syk activity was similar in both species with predictions supported experimentally. Super resolution microscopy demonstrates that the spatial distribution of Syk is similar between species, suggesting that the spatial distribution of receptors and signalling molecules in activated platelets, rather than their copy number, is important for signalling pathway regulation

Development of a novel scale-down method to study pH and dissolved oxygen heterogeneities in mammalian cell cultures

In recent years, there has been a growing interest in studying the effects of spatial heterogeneities that are present in large-scale cell culture bioreactors. When scaling up from bench-top to production bioreactors mixing times often increase, which make heterogeneities within large-scale vessels more likely

Modelling and quantifying the effect of heterogeneity in soil physical conditions on fungal growth

Despite the importance of fungi in soil ecosystem services, a theoretical framework that links soil management strategies with fungal ecology is still lacking. One of the key challenges is to understand how the complex geometrical shape of pores in soil affects fungal spread and species interaction. Progress in this area has long been hampered by a lack of experimental techniques for quantification. In this paper we use X-ray computed tomography to quantify and characterize the pore geometry at microscopic scales (30 μm) that are relevant for fungal spread in soil. We analysed the pore geometry for replicated samples with bulk-densities ranging from 1.2–1.6 g/cm3. The bulk-density of soils significantly affected the total volume, mean pore diameter and connectivity of the pore volume. A previously described fungal growth model comprising a minimal set of physiological processes required to produce a range of phenotypic responses was used to analyse the effect of these geometric descriptors on fungal invasion, and we showed that the degree and rate of fungal invasion was affected mainly by pore volume and pore connectivity. The presented experimental and theoretical framework is a significant first step towards understanding how environmental change and soil management impact on fungal diversity in soils

Design and Functionalization of Alumina Monoliths for Protein Purification by Chromatography

This thesis is about the development of multimodal porous cellular alumina structures (monoliths) by an emulsion-gel casting technique using eco-friendly and inexpensive lipids such as corn oil, castor oil, margarine and their mixtures as the dispersed phase. The monoliths obtained showed good mechanical stability (in terms of compressive strength) despite being porous (up to 60%). The formation of the porous networks was interpreted based on combined droplet coalescence, flocculation and Ostwald ripening effects. The presence of such effects along the emulsion storage time led to changes in their viscoelastic and morphological properties, which were found to correlate with structural descriptors of the monoliths after sintering (e.g. average pore sizes and porosity). Furthermore, the fact that these monoliths had hierarchically distributed pores supposes that there would be paths or channels for fluids to flow through them. Experimental and computational studies were performed to understand the behaviour of fluid flow through the monolith. As per literature, several modelling approaches have been applied to describe experimentally observed flow behaviour in such materials. Morphology plays a key role in determining their hydrodynamic and mass transfer properties. Therefore, a direct computational fluid dynamics (CFD) modelling approach was applied to simulate flow behaviour in these columns. The morphological structure of a fabricated alumina monolith was first reconstructed using 3D X-ray tomography data and, subsequently, OpenFOAM, an open-source CFD tool, was used to simulate the essential parameters for monoliths’ performance characterisation and optimisation, i.e. velocity and pressure fields, fluid streamlines, shear stress and residence time distribution (RTD). Moreover, the tortuosity of the monolith was estimated by a novel method, using the computed streamlines, and its value (~1.1) was found to be in the same range of the results obtained by known experimental, analytical and numerical equations. Besides, it was observed that fluid transport was dominated by flow heterogeneities and advection, while the shear stress at pore mouths was significantly higher than in other regions. The proposed modelling approach, with expected high potential for designing target materials, was successfully validated by an experimentally obtained residence time distribution (RTD). However, alumina itself is a relatively non-reactive material. Therefore, to explore a potential application for the produced monolith a simple, single stage sol-gel synthesis method was used to functionalize the monoliths with (3-Aminopropyl)triethoxysilane (APTES) in an aqueous environment. The nature of the attachment of APTES to the alumina and its distribution through the monolith column was evaluated using characterization methods involving FTIR-ATR, SEM-EDS and XPS measurements. Furthermore, the reaction conditions in terms of catalyst used (acid or base) and temperature were adjusted and, separately, a factorial experimental design was applied to elicit the interdependent influence of humidity, number of APTES coating layers and precursor concentration on the silanization of alumina. The reaction was found to be optimum at basic pH and a temperature of 80˚C. Optimally functionalized monoliths with highest amine density of 166 μmol/g of the column were obtained with a single coat using 2M APTES solution, and at 100% humidity. Finally, experiments were carried out to understand the protein interactions with the produced amine functionalized alumina monolithic columns. Bovine Serum Albumin (BSA) was used as the model protein. Studies were carried out at varied BSA concentrations (0.5 to 10 mg.mL-1) to understand the interaction behaviour between the protein and the column. It was found that at lower concentrations there appeared to be stronger binding. At higher BSA concentrations, due to the formation of aggregates, the interaction appears to be a multi-layered physical adsorption. Dynamic light scattering measurements further confirmed the presence of protein aggregation phenomena at higher protein concentrations due to the contact of the protein solution with the column. From these inferences, appropriate strategies were used to bind Protein G to the column - a maximum of 1.43 mg Protein G/g of monolith (29% by mass of column) was bound. Finally, a binding-elution experiment using bovine immunoglobulin G was conducted and it was found that 73.4% (IgG/Protein G) could bind to the column and 86% of the bound IgG could be eluted using an appropriate buffer. This proved the potential of the amine functionalized monolith for further application as an Affinity Chromatography medium