Mathematical Models of Tumor-Immune System Dynamics

"This collection of papers offers a broad synopsis of state-of-the-art mathematical methods used in modeling the interaction between tumors and the immune system. These papers were presented at the four-day workshop on Mathematical Models of Tumor-Immune System Dynamics held in Sydney, Australia from January 7th to January 10th, 2013. The workshop brought together applied mathematicians, biologists, and clinicians actively working in the field of cancer immunology to share their current research and to increase awareness of the innovative mathematical tools that are applicable to the growing field of cancer immunology. Recent progress in cancer immunology and advances in immunotherapy suggest that the immune system plays a fundamental role in host defense against tumors and could be utilized to prevent or cure cancer. Although theoretical and experimental studies of tumor-immune system dynamics have a long history, there are still many unanswered questions about the mechanisms that govern the interaction between the immune system and a growing tumor. The multidimensional nature of these complex interactions requires a cross-disciplinary approach to capture more realistic dynamics of the essential biology. The papers presented in this volume explore these issues and the results will be of interest to graduate students and researchers in a variety of fields within mathematical and biological sciences."--Publisher website

One-step macroscopic alignment of conjugated polymer systems by epitaxial crystallization during spin-coating

The one-step preparation of highly anisotropic polymer semiconductor thin films directly from solution is demonstrated. The conjugated polymer poly(3-hexylthiophene) (P3HT) as well as P3HT:fullerene bulk-heterojunction blends can be spin-coated from a mixture of the crystallizable solvent 1,3,5-trichlorobenzene (TCB) and a second carrier solvent such as chlorobenzene. Solidification is initiated by growth of macroscopic TCB spherulites followed by epitaxial crystallization of P3HT on TCB crystals. Subsequent sublimation of TCB leaves behind a replica of the original TCB spherulites. Thus, highly ordered thin films are obtained, which feature square-centimeter-sized domains that are composed of one spherulite-like structure each. A combination of optical microscopy and polarized photoluminescence spectroscopy reveals radial alignment of the polymer backbone in case of P3HT, whereas P3HT:fullerene blends display a tangential orientation with respect to the center of spherulite-like structures. Moreover, grazing-incidence wide-angle X-ray scattering reveals an increased relative degree of crystallinity and predominantly flat-on conformation of P3HT crystallites in the blend. The use of other processing methods such as dip-coating is also feasible and offers uniaxial orientation of the macromolecule. Finally, the applicability of this method to a variety of other semi-crystalline conjugated polymer systems is established. Those include other poly(3-alkylthiophene)s, two polyfluorenes, the low band-gap polymer PCPDTBT, a diketopyrrolopyrrole (DPP) small molecule as well as a number of polymer:fullerene and polymer:polymer blends. Macroscopic spherulite-like structures of the conjugated polymer poly(3-hexylthiophene) (P3HT) grow directly during spin-coating. This is achieved by processing P3HT or P3HT:fullerene bulk heterojunction blends from a mixture of the crystallizable solvent 1,3,5-trichlorobenzene and a second carrier solvent such as chlorobenzene. Epitaxial growth of the polymer on solidified solvent crystals gives rise to circular-symmetric, spherulite-like structures that feature a high degree of anisotropy.

Iron(II) spin transition complexes with dendritic ligands, Part I

The ligands G1- and G2-oligo (benzyl ether) (PBE) dendrons and their iron(II) complexes [Fe(Gn-PBE)3]A2·xH2O (with n = 1, 2 and A = triflate, tosylate) were prepared. The magnetic properties of the complexes were investigated by a SQUID magnetometer. All complexes exhibit gradual spin transition below room temperature. At very low temperatures the magnetic behaviour reflects zero-field splitting (ZFS) effects. 57Fe-Mössbauer spectroscopy was performed to distinguish between ZFS of high spin species and spin state conversion into the low spin state. Further characterisation was carried out by thermogravimetric analysis (TGA) and FT-IR spectroscopy. Structural features have been determined by powder XRD measurements.

Iron(II) spin-transition complexes with dendritic ligands, part II

The dendritic triazole-based complexes \[Fe(G1-BOC)3](triflate) 2·xH2O (1; G1-BOC = tert-butyl {3-\[3-(3-tert- butoxycarbonylaminopropyl)-5-(\[1,2,4]triazol-4-ylcarbamoyl)-phenyl]propyl} carbamate, triflate = CF3SO3-), \[Fe(G1-BOC) 3]-(tosylate)2·xH2O(2;tosylate = p-CH3PhSO3-),\[Fe(G1-DPBE)3]-(triflate) 2·xH2O {3; G1-DPBE = 3,5-bis(3,5- didodecaoxybenzyloxy)-N-\[1,2,4]triazol-4-ylbenzamide}, \[Fe(G1-DPBE) 3]-(tosylate)2·xH2O (4) and \[Fe(G1-DPBE)3](BF4)2·xH2O (5) were designed and synthesized. Magnetic and thermal properties of these novel complexes were characterized by magnetic susceptibility measurements, 57Fe Mössbauer spectroscopy and thermogravimetric analysis or differential scanning calorimetry, respectively. All dendritic complexes under study show different spin-transition behaviour with respect to the nature of different dendritic ligands and counteranions. Complexes 1 and 2 have pronounced effects of a spin-state change during the first heating process and gradual spintransition properties for further temperature treatments, whereas 3 and 4 exhibited a very sharp spin-state change in the first heating procedures. Complex 5 showed a gradual spin-transition curve. In this paper, we report how the magnetic properties of these complexes are correlated with noncoordinated water molecules and their effects on spin states.

Integrating the impact of climate into event based failure models for water pipes

This paper presents an event-based failure model to predict the number of failures that occur in water distribution assets. Often, such models have been based on analysis of historical failure data combined with pipe characteristics and environmental conditions. In this paper weather data have been added to the model to take into account the commonly observed seasonal variation of the failure rate. The theoretical basis of existing logistic regression models is briefly described in this paper, along with the refinements made to the model for inclusion of seasonal variation of weather. The performance of these refinements is tested using data from two Australian water authorities.

Conceptualising business models : definitions, frameworks and classifications

The business model concept is gaining traction in different disciplines but is still criticized for being fuzzy and vague and lacking consensus on its definition and compositional elements. In this paper we set out to advance our understanding of the business model concept by addressing three areas of foundational research: business model definitions, business model elements, and business model archetypes. We define a business model as a representation of the value logic of an organization in terms of how it creates and captures customer value. This abstract and generic definition is made more specific and operational by the compositional elements that need to address the customer, value proposition, organizational architecture (firm and network level) and economics dimensions. Business model archetypes complement the definition and elements by providing a more concrete and empirical understanding of the business model concept. The main contributions of this paper are (1) explicitly including the customer value concept in the business model definition and focussing on value creation, (2) presenting four core dimensions that business model elements need to cover, (3) arguing for flexibility by adapting and extending business model elements to cater for different purposes and contexts (e.g. technology, innovation, strategy),(4) stressing a more systematic approach to business model archetypes by using business model elements for their description, and (5) suggesting to use business model archetype research for the empirical exploration and testing of business model elements and their relationships.

Building macroscale models from microscale probabilistic models : a general probabilistic approach for nonlinear diffusion and multispecies phenomena

A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.

Cost efficiency of Japanese steam power generation companies : a Bayesian comparison of random and fixed frontier models

This study analyses and compares the cost efficiency of Japanese steam power generation companies using the fixed and random Bayesian frontier models. We show that it is essential to account for heterogeneity in modelling the performance of energy companies. Results from the model estimation also indicate that restricting CO2 emissions can lead to a decrease in total cost. The study finally discusses the efficiency variations between the energy companies under analysis, and elaborates on the managerial and policy implications of the results.

Bayesian models for spatio-temporal assessment of disease

This thesis has contributed to the advancement of knowledge in disease modelling by addressing interesting and crucial issues relevant to modelling health data over space and time. The research has led to the increased understanding of spatial scales, temporal scales, and spatial smoothing for modelling diseases, in terms of their methodology and applications. This research is of particular significance to researchers seeking to employ statistical modelling techniques over space and time in various disciplines. A broad class of statistical models are employed to assess what impact of spatial and temporal scales have on simulated and real data.

A case study of the Iranian national railway and its absolute capacity expansion using analytical models

Identifying railway capacity is an important task that can identify "in principal" whether the network can handle an intended traffic flow, and whether there is any free capacity left for additional train services. Capacity determination techniques can also be used to identify how best to improve an existing network, and at least cost. In this article an optimization approach has been applied to a case study of the Iran national railway, in order to identify its current capacity and to optimally expand it given a variety of technical conditions. This railway is very important in Iran and will be upgraded extensively in the coming years. Hence the conclusions in this article may help in that endeavor. A sensitivity analysis is recommended to evaluate a wider range of possible scenarios. Hence more useful lower and upper bounds can be provided for the performance of the system

Integrin-linked kinase as a target for ERG-mediated invasive properties in prostate cancer models

Approximately half of prostate cancers (PCa) carry TMPRSS2-ERG translocations; however, the clinical impact of this genomic alteration remains enigmatic. Expression of v-ets erythroblastosis virus E26 oncogene like (avian) gene (ERG) promotes prostatic epithelial dysplasia in transgenic mice and acquisition of epithelial-to-mesenchymal transition (EMT) characteristics in human prostatic epithelial cells (PrECs). To explore whether ERG-induced EMT in PrECs was associated with therapeutically targetable transformation characteristics, we established stable populations of BPH-1, PNT1B and RWPE-1 immortalized human PrEC lines that constitutively express flag-tagged ERG3 (fERG). All fERG-expressing populations exhibited characteristics of in vitro and in vivo transformation. Microarray analysis revealed >2000 commonly dysregulated genes in the fERG-PrEC lines. Functional analysis revealed evidence that fERG cells underwent EMT and acquired invasive characteristics. The fERG-induced EMT transcript signature was exemplified by suppressed expression of E-cadherin and keratins 5, 8, 14 and 18; elevated expression of N-cadherin, N-cadherin 2 and vimentin, and of the EMT transcriptional regulators Snail, Zeb1 and Zeb2, and lymphoid enhancer-binding factor-1 (LEF-1). In BPH-1 and RWPE-1-fERG cells, fERG expression is correlated with increased expression of integrin-linked kinase (ILK) and its downstream effectors Snail and LEF-1. Interfering RNA suppression of ERG decreased expression of ILK, Snail and LEF-1, whereas small interfering RNA suppression of ILK did not alter fERG expression. Interfering RNA suppression of ERG or ILK impaired fERG-PrEC Matrigel invasion. Treating fERG-BPH-1 cells with the small molecule ILK inhibitor, QLT-0267, resulted in dose-dependent suppression of Snail and LEF-1 expression, Matrigel invasion and reversion of anchorage-independent growth. These results suggest that ILK is a therapeutically targetable mediator of ERG-induced EMT and transformation in PCa.

Bridging experiments, models and simulations : an integrative approach to validation in computational cardiac electrophysiology

Computational models in physiology often integrate functional and structural information from a large range of spatio-temporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and scepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace and refine animal experiments. A fundamental requirement to fulfil these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated. The present study aims at informing strategies for validation by elucidating the complex interrelations between experiments, models and simulations in cardiac electrophysiology. We describe the processes, data and knowledge involved in the construction of whole ventricular multiscale models of cardiac electrophysiology. Our analysis reveals that models, simulations, and experiments are intertwined, in an assemblage that is a system itself, namely the model-simulation-experiment (MSE) system. Validation must therefore take into account the complex interplay between models, simulations and experiments. Key points for developing strategies for validation are: 1) understanding sources of bio-variability is crucial to the comparison between simulation and experimental results; 2) robustness of techniques and tools is a pre-requisite to conducting physiological investigations using the MSE system; 3) definition and adoption of standards facilitates interoperability of experiments, models and simulations; 4) physiological validation must be understood as an iterative process that defines the specific aspects of electrophysiology the MSE system targets, and is driven by advancements in experimental and computational methods and the combination of both.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression