Computational and Mathematical Modelling of the EGF Receptor System

This chapter gives an overview of computational and mathematical modelling of the EGF receptor system. It begins with a survey of motivations for producing such models, then describes the main approaches that are taken to carrying out such modelling, viz. differential equations and individual-based modelling. Finally, a number of projects that applying modelling and simulation techniques to various aspects of the EGF receptor system are described

Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes

<p>Abstract</p> <p>Background</p> <p>Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes.</p> <p>Results</p> <p><it>Meredys </it>allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time.</p> <p>Conclusions</p> <p><it>Meredys </it>provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. <it>Meredys </it>is a Free Software and the source code is available at <url>http://meredys.sourceforge.net/</url>.</p

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally

Glycomics Analysis of Mammalian Heparan Sulfates Modified by the Human Extracellular Sulfatase HSulf2

The Sulfs are a family of endosulfatases that selectively modify the 6O-sulfation state of cell-surface heparan sulfate (HS) molecules. Sulfs serve as modulators of cell-signaling events because the changes they induce alter the cell surface co-receptor functions of HS chains. A variety of studies have been aimed at understanding how Sulfs modify HS structure, and many of these studies utilize Sulf knockout cell lines as the source for the HS used in the experiments. However, genetic manipulation of Sulfs has been shown to alter the expression levels of HS biosynthetic enzymes, and in these cases an assessment of the fine structural changes induced solely by Sulf enzymatic activity is not possible. Therefore, the present work aims to extend the understanding of substrate specificities of HSulf2 using in vitro experiments to compare HSulf2 activities on HS from different organ tissues.To further the understanding of Sulf enzymatic activity, we conducted in vitro experiments where a variety of mammalian HS substrates were modified by recombinant human Sulf2 (HSulf2). Subsequent to treatment with HSulf2, the HS samples were exhaustively depolymerized and analyzed using size-exclusion liquid chromatography-mass spectrometry (SEC-LC/MS). We found that HSulf2 activity was highly dependent on the structural features of the HS substrate. Additionally, we characterized, for the first time, the activity of HSulf2 on the non-reducing end (NRE) of HS chains. The results indicate that the action pattern of HSulf2 at the NRE is different compared to internally within the HS chain.The results of the present study indicate that the activity of Sulfs is dependent on the unique structural features of the HS populations that they edit. The activity of HSulf2 at HS NREs implicates the Sulfs as key regulators of this region of the chains, and concomitantly, the protein-binding events that occur there

Using a logical model to predict the growth of yeast

<p>Abstract</p> <p>Background</p> <p>A logical model of the known metabolic processes in <it>S. cerevisiae </it>was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement.</p> <p>Results</p> <p>Compared to the FBA model, the logical model has information on an additional 263 putative genes and 247 additional reactions. The correctness of this model was evaluated by comparison with iND750 (an updated FBA model closely related to iFF708) by evaluating the performance of both models on predicting empirical minimal medium growth data/essential gene listings.</p> <p>Conclusion</p> <p>ROC analysis and other statistical studies revealed that use of the simpler logical form and larger coverage results in no significant degradation of performance compared to iND750.</p

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process

Mast cells and eosinophils in invasive breast carcinoma

<p>Abstract</p> <p>Background</p> <p>Inflammatory cells in the tumour stroma has gained increasing interest recently. Thus, we aimed to study the frequency and prognostic impact of stromal mast cells and tumour infiltrating eosinophils in invasive breast carcinomas.</p> <p>Methods</p> <p>Tissue microarrays containing 234 cases of invasive breast cancer were prepared and analysed for the presence of stromal mast cells and eosinophils. Tumour infiltrating eosinophils were counted on hematoxylin-eosin slides. Immunostaining for tryptase was done and the total number of mast cells were counted and correlated to the proliferation marker Ki 67, positivity for estrogen and progesterone receptors, clinical parameters and clinical outcome.</p> <p>Results</p> <p>Stromal mast cells were found to correlate to low grade tumours and estrogen receptor positivity. There was a total lack of eosinophils in breast cancer tumours.</p> <p>Conclusion</p> <p>A high number of mast cells in the tumours correlated to low-grade tumours and estrogen receptor positivity. Eosinophils are not tumour infiltrating in breast cancers.</p

Amyloid Precursor Protein Is Trafficked and Secreted via Synaptic Vesicles

A large body of evidence has implicated amyloid precursor protein (APP) and its proteolytic derivatives as key players in the physiological context of neuronal synaptogenesis and synapse maintenance, as well as in the pathology of Alzheimer's Disease (AD). Although APP processing and release are known to occur in response to neuronal stimulation, the exact mechanism by which APP reaches the neuronal surface is unclear. We now demonstrate that a small but relevant number of synaptic vesicles contain APP, which can be released during neuronal activity, and most likely represent the major exocytic pathway of APP. This novel finding leads us to propose a revised model of presynaptic APP trafficking that reconciles existing knowledge on APP with our present understanding of vesicular release and recycling

A Review of Dietary Prevention of Human Papillomavirus-Related Infection of the Cervix and Cervical Intraepithelial Neoplasia

The natural history of cervical cancer suggests that prevention can be achieved by modification of the host's immune system through a nutrient-mediated program. This study reviews the preventive role of dietary intake on cervical intraepithelial neoplasia (CIN) induced by human papillomavirus (HPV). Electronic databases were searched using relevant keywords such as, but not limited to, human papillomavirus infection, cervical intraepithelial neoplasia, lifestyle factors, nutrients intake, and diet. High consumption of fruit and vegetables appears to be protective against CIN. The findings also highlight the possibility of consuming high levels of specific nutrients, vitamins, and minerals, and retaining sufficient level of these elements in the body, especially those with high antioxidants and antiviral properties, to prevent progression of transient and persistent HPV infections to high-grade CIN 2 and 3 (including in situ cervical cancer). The protective effect is not significant for high-risk HPV persistent infections and invasive cervical cancer. Although it appears that intake of specific nutrients, vitamins, and minerals may be good in CIN prevention, there is lack of evidence from controlled trial to confirm this. Health professionals shall focus on implementation of a balanced-diet prevention strategy at an early stage for cervical cancer prevention