Exploring the formation of Alzheimer's disease senile plaques in silico

Microglial roles in Alzheimer's disease: An agent-based model to elucidate microglial spatiotemporal response to beta-amyloid

Alzheimer's disease (AD) is characterized by beta-amyloid (Aβ) plaques in the brain and widespread neuronal damage. Because of the high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aβ plaques. In response to AD pathology, microglia are innate phagocytotic immune cells that transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aβ dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent-based model to characterize the spatiotemporal interactions between microglia and Aβ. Our model qualitatively reproduces mouse data trends where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p < 0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. In addition, in silico behavioral knockout simulations show that phagocytosis knockouts have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knockouts had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis, and replication of activated microglia have complex impacts on plaque volume and coverage, whereas microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.

Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases

Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.

Age-dependent accumulation of tau aggregation in Caenorhabditis elegans

Aging is the primary risk factor for Alzheimer's disease (AD) and related disorders (ADRDs). Tau aggregation is a hallmark of AD and other tauopathies. Even in normal aging, tau aggregation is found in brains, but in disease states, significantly more aggregated tau is present in brain regions demonstrating synaptic degeneration and neuronal loss. It is unclear how tau aggregation and aging interact to give rise to the phenotypes observed in disease states. Most AD/ADRD animal models have focused on late stages, after significant tau aggregation has occurred. There are fewer where we can observe the early aggregation events and progression during aging. In an attempt to address this gap, we created C. elegans models expressing a GFP-tagged version of the human tau protein. Here we examined how tau-gfp behaved during aging, comparing wild-type tau (hTau40), a disease-associated mutation (P301S), and an aggregation-prone variant (3PO). We measured age-dependent changes in GFP intensity and correlated those changes to normal aging in the nematode. We found differences in tau stability and accumulation depending on the tau variant expressed. hTau40GFP and P301SGFP were localized to axons and cell bodies, while 3POGFP was more concentrated within cell bodies. Expression of 3POGFP resulted in decreased lifespan and variations in locomotor rate, consistent with a pathological effect. Finally, we found that the human tau interacted genetically with the C. elegans ortholog of human tau, ptl-1, where the loss of ptl-1 significantly accelerated the time to death in animals expressing 3PO.

SARS-CoV-2 rate of spread in and across tissue, groundwater and soil: A meshless algorithm for the fractional diffusion equation

The epidemiological aspects of the viral dynamic of the SARS-CoV-2 have become increasingly crucial due to major questions and uncertainties around the unaddressed issues of how corpse burial or the disposal of contaminated waste impacts nearby soil and groundwater. Here, a theoretical framework base on a meshless algorithm using the moving least squares (MLS) shape functions is adopted for solving the time-fractional model of the viral diffusion in and across three different environments including water, tissue, and soil. Our computations predict that by considering the α (order of fractional derivative) best fit to experimental data, the virus has a traveling distance of 1 m m in water after 22, regardless of the source of contamination (e.g., from tissue or soil). The outcomes and extrapolations of our study are fundamental for providing valuable benchmarks for future experimentation on this topic and ultimately for the accurate description of viral spread across different environments. In addition to COVID-19 relief efforts, our methodology can be adapted for a wide range of applications such as studying virus ecology and genomic reservoirs in freshwater and marine environments.

Mathematical Model Shows How Sleep May Affect Amyloid-β Fibrillization

Deposition of amyloid-β (Aβ) fibers in the extracellular matrix of the brain is a ubiquitous feature associated with several neurodegenerative disorders, especially Alzheimer's disease (AD). Although many of the biological aspects that contribute to the formation of Aβ plaques are well addressed at the intra- and intercellular levels in short timescales, an understanding of how Aβ fibrillization usually starts to dominate at a longer timescale despite the presence of mechanisms dedicated to Aβ clearance is still lacking. Furthermore, no existing mathematical model integrates the impact of diurnal neural activity as emanated from circadian regulation to predict disease progression due to a disruption in the sleep-wake cycle. In this study, we develop a minimal model of Aβ fibrillization to investigate the onset of AD over a long timescale. Our results suggest that the diseased state is a manifestation of a phase change of the system from soluble Aβ (sAβ) to fibrillar Aβ (fAβ) domination upon surpassing a threshold in the production rate of sAβ. By incorporating the circadian rhythm into our model, we reveal that fAβ accumulation is crucially dependent on the regulation of the sleep-wake cycle, thereby indicating the importance of good sleep hygiene in averting AD onset. We also discuss potential intervention schemes to reduce fAβ accumulation in the brain by modification of the critical sAβ production rate.

In silico disease model: from simple networks to complex diseases

In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Mathematical Models to Shed Light on Amyloid-Beta and Tau Protein Dependent Pathologies in Alzheimer's Disease

The number of patients suffering from dementia due to Alzheimer's disease (AD) is constantly rising worldwide. This has accordingly resulted in huge burdens on the health systems and involved families. Lack of profound understanding of neural networking in normal brain and their interruption in AD makes the treatment of this neurodegenerative multifaceted disease a challenging issue. In recent years, mathematical and computational methods have paved the way towards a better understanding of the brain functional connectivity. Thus, much attention has been paid to this matter from both basic science researchers and clinicians with an interdisciplinary approach to determine what is not functioning properly in AD patients and how this malfunctioning can be addressed. In this review, a number of AD-related articles and well-studied pathophysiologic topics (e.g., amyloid-beta, neurofibrillary tangles, Ca²⁺ dysregulation, and synaptic plasticity alterations) has been literally surveyed from a computational and systems biology point of view. The neural networks were discussed from biological and mathematical point of views and their alterations in recent findings were further highlighted. Application of the graph theoretical analysis in the brain imaging was reviewed, depicting the relations between brain structure and function, without diving into mathematical details. Moreover, differential rate equations were briefly articulated, emphasizing the potential use of these equations in simplifying complex processes in relevance to pathologies of AD. Comprehensive insights were given into the AD progression from neural networks perspective, which may lead us towards potential strategies for early diagnosis and effective treatment of AD.

Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.

Pattern formation in multiphase models of chemotactic cell aggregation

We develop a continuum model for the aggregation of cells cultured in a nutrient-rich medium in a culture well. We consider a 2D geometry, representing a vertical slice through the culture well, and assume that the cell layer depth is small compared with the typical lengthscale of the culture well. We adopt a continuum mechanics approach, treating the cells and culture medium as a two-phase mixture. Specifically, the cells and culture medium are treated as fluids. Additionally, the cell phase can generate forces in response to environmental cues, which include the concentration of a chemoattractant that is produced by the cells within the culture medium. The model leads to a system of coupled nonlinear partial differential equations for the volume fraction and velocity of the cell phase, the culture medium pressure and the chemoattractant concentration, which must be solved subject to appropriate boundary and initial conditions. To gain insight into the system, we consider two model reductions, appropriate when the cell layer depth is thin compared to the typical length scale of the culture well: a (simple) 1D and a (more involved) thin-film extensional flow reduction. By investigating the resulting systems of equations analytically and numerically, we identify conditions under which small amplitude perturbations to a homogeneous steady state (corresponding to a spatially uniform cell distribution) can lead to a spatially varying steady state (pattern formation). Our analysis reveals that the simpler 1D reduction has the same qualitative features as the thin-film extensional flow reduction in the linear and weakly nonlinear regimes, motivating the use of the simpler 1D modelling approach when a qualitative understanding of the system is required. However, the thin-film extensional flow reduction may be more appropriate when detailed quantitative agreement between modelling predictions and experimental data is desired. Furthermore, full numerical simulations of the two model reductions in regions of parameter space when the system is not close to marginal stability reveal significant differences in the evolution of the volume fraction and velocity of the cell phase, and chemoattractant concentration.

Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases

INTRODUCTION

The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike 'black box' approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.

Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview

Protein misfolding refers to a process where proteins become structurally abnormal and lose their specific 3-dimensional spatial configuration. The histopathological presence of misfolded protein (MP) aggregates has been associated as the primary evidence of multiple neurological diseases, including Prion diseases, Alzheimer's disease, Parkinson's disease, and Creutzfeldt-Jacob disease. However, the exact mechanisms of MP aggregation and propagation, as well as their impact in the long-term patient's clinical condition are still not well understood. With this aim, a variety of mathematical models has been proposed for a better insight into the kinetic rate laws that govern the microscopic processes of protein aggregation. Complementary, another class of large-scale models rely on modern molecular imaging techniques for describing the phenomenological effects of MP propagation over the whole brain. Unfortunately, those neuroimaging-based studies do not take full advantage of the tremendous capabilities offered by the chemical kinetics modeling approach. Actually, it has been barely acknowledged that the vast majority of large-scale models have foundations on previous mathematical approaches that describe the chemical kinetics of protein replication and propagation. The purpose of the current manuscript is to present a historical review about the development of mathematical models for describing both microscopic processes that occur during the MP aggregation and large-scale events that characterize the progression of neurodegenerative MP-mediated diseases.

Modelling the molecular mechanisms of aging

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field.

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions

Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive dysfunction and loss of neurons. Here, we distil and discuss the current state of modeling in the area of neurodegeneration, and objectively compare the gaps between existing clinical knowledge and the mechanistic understanding of the major pathological processes implicated in neurodegenerative disorders. We also discuss new directions in the field of neurodegeneration that hold potential for furthering therapeutic interventions and strategies.

Amyloid pore-channel hypothesis: effect of ethanol on aggregation state using frog oocytes for an Alzheimer's disease study

Alzheimer's disease severely compromises cognitive function. One of the mechanisms to explain the pathology of Alzheimer’s disease has been the hypotheses of amyloid-pore/channel formation by complex Aβ-aggregates. Clinical studies suggested the moderate alcohol consumption can reduces probability developing neurodegenerative pathologies. A recent report explored the ability of ethanol to disrupt the generation of complex Aβ in vitro and reduce the toxicity in two cell lines. Molecular dynamics simulations were applied to understand how ethanol blocks the aggregation of amyloid. On the other hand, the in silico modeling showed ethanol effect over the dynamics assembling for complex Aβ-aggregates mediated by break the hydrosaline bridges between Asp 23 and Lys 28, was are key element for amyloid dimerization. The amyloid pore/channel hypothesis has been explored only in neuronal models, however recently experiments suggested the frog oocytes such an excellent model to explore the mechanism of the amyloid pore/channel hypothesis. So, the used of frog oocytes to explored the mechanism of amyloid aggregates is new, mainly for amyloid/pore hypothesis. Therefore, this experimental model is a powerful tool to explore the mechanism implicates in the Alzheimer’s disease pathology and also suggests a model to prevent the Alzheimer’s disease pathology. [BMB Reports 2015; 48(1): 13-18]

Numerical and analytical study of an atherosclerosis inflammatory disease model

We study a reaction-diffusion mathematical model for the evolution of atherosclerosis as an inflammation process by combining analytical tools with computer-intensive numerical calculations. The computational work involved the calculation of more than sixty thousand solutions of the full reaction-diffusion system and lead to the complete characterisation of the ω-limit for every initial condition. Qualitative properties of the solution are rigorously proved, some of them hinted at by the numerical study.

Topological basis of epileptogenesis in a model of severe cortical trauma

Epileptic activity often arises after a latent period following traumatic brain injury. Several factors contribute to the emergence of post-traumatic epilepsy, including disturbances to ionic homeostasis, pathological action of intrinsic and synaptic homeostatic plasticity, and remodeling of anatomical network synaptic connectivity. We simulated a large-scale, biophysically realistic computational model of cortical tissue to study the mechanisms underlying the genesis of post-traumatic paroxysmal epileptic-like activity in the deafferentation model of a severely traumatized cortical network. Post-traumatic generation of paroxysmal events did not require changes of the structural connectivity. Rather, network bursts were induced following the action of homeostatic synaptic plasticity, which selectively influenced functionally dominant groups of intact neurons with preserved inputs. This effect critically depended on the spatial density of intact neurons. Thus in the deafferentation model of post-traumatic epilepsy, a trauma-induced change in functional (rather than anatomical) connectivity might be sufficient for epileptogenesis.

Mathematical modeling for the pathogenesis of Alzheimer's disease

Despite extensive research, the pathogenesis of neurodegenerative Alzheimer's disease (AD) still eludes our comprehension. This is largely due to complex and dynamic cross-talks that occur among multiple cell types throughout the aging process. We present a mathematical model that helps define critical components of AD pathogenesis based on differential rate equations that represent the known cross-talks involving microglia, astroglia, neurons, and amyloid-β (Aβ). We demonstrate that the inflammatory activation of microglia serves as a key node for progressive neurodegeneration. Our analysis reveals that targeting microglia may hold potential promise in the prevention and treatment of AD.

Exploration of beneficial and deleterious effects of inflammation in stroke: dynamics of inflammation cells

The inflammatory process during stroke consists of activation of resident brain microglia and recruitment of leucocytes, namely neutrophils and monocytes/macrophages. During inflammation, microglial cells, neutrophils and macrophages secrete inflammatory cytokines and chemokines, and phagocytize dead cells. The recruitment of blood cells (neutrophils and macrophages) is mediated by the leucocyte-endothelium interactions and more specifically by cell adhesion molecules. A mathematical model is proposed to represent the dynamics of various brain cells and of immune cells (neutrophils and macrophages). This model is based on a set of six ordinary differential equations and explores the beneficial and deleterious effects of inflammation, respectively phagocytosis by immune cells and the release of pro-inflammatory mediators and nitric oxide (NO). The results of our simulations are qualitatively consistent with those observed in experiments in vivo and would suggest that the increase of phagocytosis could contribute to the increase of the percentage of living cells. The inhibition of the production of cytokines and NO and the blocking of neutrophil and macrophage infiltration into the brain parenchyma led also to the improvement of brain cell survival. This approach may help to explore the respective contributions of the beneficial and deleterious roles of the inflammatory process in stroke, and to study various therapeutic strategies in order to reduce stroke damage.

Prevention of inflammation-mediated neurotoxicity by Rg3 and its role in microglial activation

Considering the importance of inflammation and apoptosis in neurodegenerative conditions, the potential suppressive effects of the Rg3, a by-product obtained during the steaming of red ginseng, may indicate that Rg3 could provide a beneficial therapeutic approach to treating or preventing neurodegenerative disease. We investigated the effect of Rg3 on Abeta42-mediated microglial activation and inflammation-mediated neurotoxicity in murine BV-2 microglial and Neuro-2a neuroblastoma cells, respectively. Rg3 effectively reduced inflammatory cytokine expression in Abeta42-treated BV-2, and inhibited the binding of NF-kappaB p65 to its DNA consensus sequences, and significantly reduced the expression of TNF-alpha in activated microglia. Pretreatment with Rg3 increased the survival rate of Neuro-2a exposed to TNF-alpha. These observations suggest that Rg3 reduced neurotoxicity by inhibiting chronic inflammation through the suppression of activated microglia. In addition, the expression of pro-inflammatory cytokines in BV-2 stimulated by Abeta42 was decreased but not eliminated by Rg3 when binding to the macrophage scavenger receptor type A (MSRA) was blocked with fucoidan. This implies that the inflammatory response may not be exclusively triggered via MSRA. More interestingly, iNOS was almost completely inhibited in the presence of Rg3 when MSRA binding was blocked with fucoidan. Moreover, Rg3 increased the expression of MSRA in BV-2 transfected with siRNA targeting MSRA mRNA, and this increased MSRA expression may play a role in the phagocytosis of Abeta42 peptides. Our results indicate that inhibition of the inflammatory repertoire of microglia, neuroprotection, and increased MSRA expression induced by Rg3 may at least partly explain its therapeutic effects in chronic neurodegenerative diseases.

3D computation modelling of the influence of cytokine secretion on Th‐cell development suggests that negative selection (inhibition of Th1 cells) is more effective than positive selection by IL‐4 for Th2 cell dominance

Th-cell development has been suggested to include selective mechanisms in which certain cytokines select either Th1 or Th2 cells to proliferate and grow. The selective theory is based on the observation that Th2 cells secrete IL-4, a cytokine that promotes Th2 development, whereas Th1 cells secrete interferon-gamma (IFN-gamma) that favours Th1 development, and both positive and negative selective influences have been suggested to operate. In this study, we investigate the role of autocrine secretion and utilization of IL-4 by Th2 cells and address the question of whether an activated Th2 cell can be positively selected by IL-4 secreted from other Th2 cells. We present a spatial three dimensional (3D) modelling approach to simulate the interaction between the IL-4 ligand and its IL-4 receptors expressed on discrete IL-4 secreting cells. The simulations, based on existing experimental data on the IL-4 receptor-ligand system, illustrate how Th-cell development is highly dependent on the distance between cells that are communicating. The model suggests that a single Th2 cell is likely to communicate with possible target cells within a range of approximately 100 microm and that an activated Th2 cell manages to fill most of its own IL-4 receptors, even at a low secretion rate. The predictions made by the model suggest that negative selection against Th1 cells is more effective than positive selection by IL-4 for promoting Th2 dominance.

Probing the effects of the well-mixed assumption on viral infection dynamics

Viral kinetics have been extensively studied in the past through the use of spatially well-mixed ordinary differential equations describing the time evolution of the diseased state. However, emerging spatial structures such as localized populations of dead cells might adversely affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In a previous publication [Beauchemin, C., Samuel, J., Tuszynski, J., 2005. A simple cellular automaton model for influenza A viral infections. J. Theor. Biol. 232(2), 223-234], a simple two-dimensional cellular automaton model was introduced and shown to be accurate enough to model an uncomplicated infection with influenza A. Here, this model is used to investigate the effects of relaxing the well-mixed assumption. Particularly, the effects of the initial distribution of infected cells, the regeneration rule for dead epithelial cells, and the proliferation rule for immune cells are explored and shown to have an important impact on the development and outcome of the viral infection in our model.

Potential effects of microglial activation induced by ginsenoside Rg3 in rat primary culture: Enhancement of type a macrophage scavenger receptor expression

Brain microglia are phagocytic cells that are the major inflammatory response cells of the central nervous system and widely held to play important pathophysiologic roles in Alzheimer's disease (AD) in both potentially neurotoxic responses and potentially beneficial phagocytic responses. In the study, we examined whether ginsonoside Rg3, a by-product of red ginseng, enhances the microglial phagocytosis of Abeta. We found that Rg3 promoted Abeta uptake, internalization, and digestion. Increased maximal Abeta uptake was observed at 4 and 8 h after Rg3 pre-treatment (25 microg/mL), and the internalized Abeta was almost completely digested from cells within 36 h when pretreated with Rg3 comparing with single non-Rg3-treated groups. The expression of MSRA (type A MSR) was also up-regulated by Rg3 treatment in a dose- and time-dependent manner which was coincidently identified in western blots for MSRA proteins in cytosol. These results indicate that microglial phagocytosis of Abeta may be enhanced by Rg3 and the effect of Rg3 on promoting clearance of Abeta may be related to the MSRA-associated action of Rg3. Thus, stimulation of the MSRA might contribute to the therapeutic potentials of Rg3 in microglial phagocytosis and digestion in the treatment of AD.

Pharmacologie de la maladie d’Alzheimer : vision du futur

Ten years after the introduction of the first drug for the treatment of Alzheimer's disease, tacrine, it seems appropriate to reappraise the pharmacological processes of innovation in the field of research in dementia. The aim of this review is to pinpoint concrete improvements achieved in this field, in terms of experimental methods and clinical evaluation of the compounds, as well as the neurochemistry of the disease and cellular targets deserving of initial consideration. * The article first considers the use of animal models of Alzheimer's disease, which are classified according to two categories: animals with lesions of some neuronal pathways specifically implicated in clinical symptoms (i.e. lesions of the nucleus basalis of Meynert, the origin of cholinergic projections to the cortex underlying memory processes); and transgenic models, which are intended to reproduce some of the neuropathological hallmarks of Alzheimer's disease. Drugs can be tested in animals with such alterations for their effect on neuropathology, neurochemistry and behavioural disturbances. More recently, in silico models have been developed, which offer the possibility of simulating the pharmacodynamic effects of drugs in specific areas of the brain. These experiments are helpful in distinguishing purely symptomatic effects from disease-modifying effects, the latter being the ultimate goal of the modern pharmacology of dementia. * The second breakthrough considered in this article is the codification and standardisation of clinical methods for obtaining a more accurate and earlier diagnosis (the recent introduction of the concept of "Mild Cognitive Impairment", which includes patients who will later develop a true clinical dementia syndrome). In that respect, the determination of the biological markers of Alzheimer's disease (apolipoprotein E, amyloid substance, protein-tau, isoprostane) as well as progress in neuroimaging (functional positron emission tomography [fPET]-scan, single photon emission-computed tomography [SPECT], functional nuclear magnetic resonance [fNMR]) are discussed in terms of their potential as new tools in the early stages of drug development (surrogate markers). The methods used during the comparative clinical trials (phase III) have been elaborated and internationally standardised during the assessment of the different acetylcholinesterase inhibitors (AChE-I), with the knowledge that, since 1994, four of these have been officially approved: tacrine, donepezil, rivastigmine and galantamine; the same methods have been used for developing memantine, a recently-launched modulator of glutamatergic neurotransmission. The validated scales now take into consideration not only the cognitive dimensions of Alzheimer's disease but also the behavioural symptoms, with the introduction of the concept of BPSD (behavioural psychological symptoms of dementia). Some proposals to improve this clinical assessment of anti-dementia drugs are presented here. * The section of this article dealing with prospective issues considers the main pathways of interest in drug innovation and the elucidation of new targets for the future compounds. As well as their symptomatic effects on the different components of cognition, drugs should be neuroprotective and limit the lesions documented in Alzheimer's disease, with the aim of progressing far beyond the amyloid hypothesis (immunisation, beta-sheet breakers, secretase inhibitors). The field of excitotoxicity (which is mainly glutamate dependent) appears fruitful, because of the possibility of pharmacological intervention at the different steps in the excitotoxic process. All the new directions presented in this article support the concept of true disease-modifying agents. In conclusion, this prospective review should be considered as a guide in fostering drug innovation in Alzheimer's disease and related disorders and should help to decrease the gap existing between neuroscience and therapeutics.

Wave optics of Liesegang rings

Liesegang rings refract and reflect at the interface between the regions of the same gel but of different thickness. The incident and the refracted rings obey a refraction law analogous to the Snell's law of classical optics, with a reverse of the spacing coefficient being a counterpart of the refraction index. The wavelike behavior of the rings at the interface is explained by geometrical arguments derived from the Jablczynski's spacing principle, and is reproduced in numerical simulations based on a three-dimensional minimalistic version of the nucleation-growth model.

Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model

Infection with Mycobacterium tuberculosis is a major world health problem. An estimated 2 billion people are presently infected and the disease causes approximately 3 million deaths per year. After bacteria are inhaled into the lung, a complex immune response is triggered leading to the formation of multicellular structures termed granulomas. It is believed that the collection of host granulomas either contain bacteria resulting in a latent infection or are unable to do so, leading to active disease. Thus, understanding granuloma formation and function is essential for improving both diagnosis and treatment of tuberculosis. Granuloma formation is a complex spatio-temporal system involving interactions of bacteria, specific immune cells, including macrophages, CD4+ and CD8+ T cells, as well as immune effectors such as chemokine and cytokines. To study this complex dynamical system we have developed an agent-based model of granuloma formation in the lung. This model combines continuous representations of chemokines with discrete agent representations of macrophages and T cells in a cellular automata-like environment. Our results indicate that key host elements involved in granuloma formation are chemokine diffusion, prevention of macrophage overcrowding within the granuloma, arrival time, location and number of T cells within the granuloma, and an overall host ability to activate macrophages. Interestingly, a key bacterial factor is its intracellular growth rate, whereby slow growth actually facilitates survival.

Speed of reaction-diffusion fronts in spatially heterogeneous media

The front speed problem for nonuniform reaction rate and diffusion coefficient is studied by using singular perturbation analysis, the geometric approach of Hamilton-Jacobi dynamics, and the local speed approach. Exact and perturbed expressions for the front speed are obtained in the limit of large times. For linear and fractal heterogeneities, the analytic results have been compared with numerical results exhibiting a good agreement. Finally we reach a general expression for the speed of the front in the case of smooth and weak heterogeneities.