Mouse phenogenomics: the fast track to "systems metabolism"

With the completion of the many genomes, genetics is positioned to meet physiology. In this review, we summarize the coming of "systems metabolism" in mammals through the use of the mouse, as a model system to study metabolism. Building on mouse genetics with increasingly sophisticated clinical and molecular phenotyping strategies has enabled scientists to now tackle complex biomedical questions, such as those related to the pathogenesis of the common metabolic disorders. The ultimate goal of such strategies will be to mimic human metabolism with the click of a mouse.     

Induced pluripotent stem cells for modelling human diseases

Research into the pathophysiological mechanisms of human disease and the development of targeted therapies have been hindered by a lack of predictive disease models that can be experimentally manipulated in vitro. This review describes the current state of modelling human diseases with the use of human induced pluripotent stem (iPS) cell lines. To date, a variety of neurodegenerative diseases, haematopoietic disorders, metabolic conditions and cardiovascular pathologies have been captured in a Petri dish through reprogramming of patient cells into iPS cells followed by directed differentiation of disease-relevant cells and tissues. However, realizing the true promise of iPS cells for advancing our basic understanding of disease and ultimately providing novel cell-based therapies will require more refined protocols for generating the highly specialized cells affected by disease, coupled with strategies for drug discovery and cell transplantation.     

Human pluripotent stem cells for disease modelling and drug screening

Considerable hope surrounds the use of disease-specific pluripotent stem cells to generate models of human disease allowing exploration of pathological mechanisms and search for new treatments. Disease-specific human embryonic stem cells were the first to provide a useful source for studying certain disease states. The recent demonstration that human somatic cells, derived from readily accessible tissue such as skin or blood, can be converted to embryonic-like induced pluripotent stem cells (hiPSCs) has opened new perspectives for modelling and understanding a larger number of human pathologies. In this review, we examine the opportunities and challenges for the use of disease-specific pluripotent stem cells in disease modelling and drug screening. Progress in these areas will substantially accelerate effective application of disease-specific human pluripotent stem cells for drug screening.     

Alzheimer's disease genetics: lessons to improve disease modelling

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.     

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease

BackgroundChagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues.Methodology/principal findingsWe compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty.Conclusions/significanceThe choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.     

Computational simulation modelling of bioreactor configurations for regenerating human bladder

The objective of this study was to investigate a bioreactor suitable for human bladder regeneration. Simulations were performed using the computational fluid dynamic tools. The thickness of the bladder scaffold was 3 mm, similar to the human bladder, and overall hold-up volume within the spherical shape scaffold was 755 ml. All simulations were performed using (i) Brinkman equation on porous regions using the properties of 1% chitosan–1% gelatin structures, (ii) Michaelis–Menten type rate law nutrient consumption for smooth muscle cells (SMCs) and (iii) Mackie–Meares relationship for determining effective diffusivities. Steady state simulations were performed using flow rates from 0.5 to 5 ml/min. Two different inlet shapes: (i) straight entry at the centre (Design 1) and (ii) entry with an expansion (Design 2) were simulated to evaluate shear stress distribution. Also, mimicking bladder shape of two inlets (Design 3) was tested. Design 2 provided the uniform shear stress at the inlet and nutrient distribution, which was further investigated for the effect of scaffold locations within the reactor: (i) attached with a 3-mm open channel (Design 2-A), (ii) flow through with no open channel (Design 2-B) and (iii) porous structure suspended in the middle with 1.5-mm open channel on either side (Design 2-C). In Design 2-A and 2-C, fluid flow occurred by diffusion dominant mechanisms. Furthermore, the designed bioreactor is suitable for increased cell density of SMCs. These results showed that increasing the flow rate is necessary due to the decreased permeability at cell densities similar to the human bladder.     

Landscape modelling spatial bottlenecks: implications for raccoon rabies disease spread

A landscape genetic simulation modelling approach is used to understand factors affecting raccoon rabies disease spread in southern Ontario, Canada. Using the Ontario Rabies Model, we test the hypothesis that landscape configuration (shape of available habitat) affects dispersal, as indicated by genetic structuring. We simulated range expansions of raccoons from New York into vacant landscapes in Ontario, in two areas that differed by the presence or absence of a landscape constriction. Our results provide theoretical evidence that landscape constriction acts as a vicariant bottleneck. We discuss implications for raccoon rabies spread.     

Introduction to the modelling of venereal disease

Summary The continually rising trend in the incidence of venereal diseases, especially gonorrhoea, in a large number of countries, both developed and developing is causing considerable public health concern. There is a disquieting volume of human suffering involved, as well as large economic losses in treatment and hospitalization. The present paper reviews the existing state of development in the mathematical modelling of the relevant disease dynamics. The criss-cross nature of the infections, which in heterosexual contacts switch between the male and female populations, together with the nonlinear form of the rate of spread normally occurring in infectious diseases, leads to special types of simultaneous nonlinear differential equations.The simplest deterministic models available entail threshold phenomena connecting the maintenance of endemic states to the contact-rates, the personto-person infection-rates, and the removal-rates. A few stochastic results are also available.Special attention is given to the aspects of nonhomogeneous mixing, analysis of contact-rates, infection without immunity, allowance for asymptomatic infection, the recognition of many different classes of infected individuals, and the problems of public health forecasting and control. In some cases transient solutions of the equations can be used to forecast future trends in disease incidence, depending on appropriate assumptions about alternative public health interventions.It is concluded that further mathematical work should be concentrated on relatively simple models comprising no more than three or four district epidemiological groups for each sex. There should be both (i) more intense mathematical investigations, and (ii) new attempts to assimilate the models directly to public health venereal disease control.     

Two-dimensional dynamic modelling of human knee joint

A mathematical dynamic model of the two-dimensional representation of the knee joint is presented. The profiles of the joint surfaces are determined from X-ray films and they are represented by polynomials. The joint ligaments are modelled as nonlinear elastic springs of realistic stiffness properties. Nonlinear equations of motion coupled with nonlinear constraint conditions are solved numerically. Time derivatives are approximated by Newmark difference formulae and the resulting nonlinear algebraic equations are solved employing the Newton-Raphson iteration scheme. Several dynamic loads are applied to the center of mass of the tibia and the ensuing motion is investigated. Numerical results on ligament forces, contact point locations between femur and tibia, and the orientation of tibia relative to femur are presented. The results are shown to be consistent with the anatomy of the knee joint.     

A spatial mechanism with higher pairs for modelling the human knee joint

By generalizing a previous model proposed in the literature, a new spatial kinematic model of the knee joint passive motion is presented. The model is based on an equivalent spatial parallel mechanism which relies upon the assumption that fibers within the anterior cruciate ligament (ACL), the medial collateral ligament (MCL) and the posterior cruciate ligament (PCL) can be considered as isometric during the knee flexion in passive motion (virtually unloaded motion). The articular surfaces of femoral and tibial condyles are modelled as 3-D surfaces of general shapes. In particular, the paper presents the closure equations of the new mechanism both for surfaces represented by means of scalar equations that have the Cartesian coordinates of the points of the surface as variables and for surfaces represented in parametric form. An example of simulation is presented in the case both femoral condyles are modelled as ellipsoidal surfaces and tibial condyles as spherical surfaces. The results of the simulation are compared to those of the previous models and to measurements. The comparison confirms the expectation that a better approximation of the tibiofemoral condyle surfaces leads to a more accurate model of the knee passive motion.     

Risk factors for human disease emergence

A comprehensive literature review identifies 1415 species of infectious organism known to be pathogenic to humans, including 217 viruses and prions, 538 bacteria and rickettsia, 307 fungi, 66 protozoa and 287 helminths. Out of these, 868 (61%) are zoonotic, that is, they can be transmitted between humans and animals, and 175 pathogenic species are associated with diseases considered to be 'emerging'. We test the hypothesis that zoonotic pathogens are more likely to be associated with emerging diseases than non-emerging ones. Out of the emerging pathogens, 132 (75%) are zoonotic, and overall, zoonotic pathogens are twice as likely to be associated with emerging diseases than non-zoonotic pathogens. However, the result varies among taxa, with protozoa and viruses particularly likely to emerge, and helminths particularly unlikely to do so, irrespective of their zoonotic status. No association between transmission route and emergence was found. This study represents the first quantitative analysis identifying risk factors for human disease emergence.     

Splice modulating therapies for human disease

Dysregulation of splicing and alternative splicing underlies many genetic and acquired diseases. We present an overview of recent strategies and successes in modulating splicing therapeutically in clinical and preclinical contexts. Effective approaches include restoring open reading frames, influencing alternative splicing, and inducing exon inclusion to generate beneficial proteins and remove deleterious ones.