Modeling and investigating malaria P. Falciparum and P. Vivax infections: Application to Djibouti data.

Malaria is an infectious and communicable disease, caused by one or more species of Plasmodium parasites. There are five species of parasites responsible for malaria in humans, of which two, Plasmodium Falciparum and Plasmodium Vivax, are the most dangerous. In Djibouti, the two species of Plasmodium are present in different proportions in the infected population: 77% of P.

Segregation of co-cultured multicellular systems: review and modeling consideration.

Cell segregation caused by collective cell migration (CCM) is crucial for morphogenesis, functional development of tissue parts, and is an important aspect in other diseases such as cancer and its metastasis process. Efficiency of the cell segregation depends on the interplay between: (1) biochemical processes such as cell signaling and gene expression and (2) physical interactions between cells. Despite extensive research devoted to study the segregation of various co-cultured systems, we still do not understand the role of physical interactions in cell segregation.

Simulating photodynamic therapy for the treatment of glioblastoma using Monte Carlo radiative transport.

Significance: Glioblastoma (GBM) is a rare but deadly form of brain tumor with a low median survival rate of 14.6 months, due to its resistance to treatment. An independent simulation of the INtraoperative photoDYnamic therapy for GliOblastoma (INDYGO) trial, a clinical trial aiming to treat the GBM resection cavity with photodynamic therapy (PDT) via a laser coupled balloon device, is demonstrated.

Multi-scale nature of the tissue surface tension: Theoretical consideration on tissue model systems.

Tissue surface tension is one of the key parameters that govern tissue rearrangement, shaping, and segregation within various compartments during organogenesis, wound healing, and cancer diseases. Deeper insight into the relationship between tissue surface tension and cell residual stress accumulation caused by collective cell migration can help us to understand the multi-scale nature of cell rearrangement with pronounced oscillatory trend. Oscillatory change of cell velocity that caused strain and generated cell residual stress were discussed in the context of mechanical waves.

The dynamics along the biointerface between the epithelial and cancer mesenchymal cells: Modeling consideration.

Epithelial cancer is the one of most lethal cancer type worldwide. Targeting the early stage of disease would allow dramatic improvements in the survival of cancer patients. The early stage of the disease is related to cancer cell spreading across surrounding healthy epithelium.

The rearrangement of co-cultured cellular model systems via collective cell migration.

Cancer invasion through the surrounding epithelium and extracellular matrix (ECM) is the one of the main characteristics of cancer progression. While significant effort has been made to predict cancer cells response under various drug therapies, much less attention has been paid to understand the physical interactions between cancer cells and their microenvironment, which are essential for cancer invasion. Considering these physical interactions on various co-cultured in vitro model systems by emphasizing the role of viscoelasticity, the tissue surface tension, solid stress, and their inter-relations is a prerequisite for establishing the main factors that influence cancer cell spread and develop an efficient strategy to suppress it.

Mathematical investigation into the role of macrophage heterogeneity on the temporal and spatio-temporal dynamics of non-small cell lung cancers.

Non Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer, and represents the leading cause of cancer-related deaths worldwide. Experimental studies have shown that these solid cancers are heavily infiltrated with macrophages: anti-tumour M1 macrophages, pro-tumour M2 macrophages, and macrophage subtypes sharing both M1 and M2 properties. In this study we aim to investigate qualitatively the role of macrophages with different functional phenotypes (especially those with mixed phenotypes) on cancer dynamics and the success of different immunotherapies for cancer.

A computational investigation of COVID-19 transmission inside hospital wards and associated costs.

The COVID-19 pandemic has placed a particular burden on hospitals: from intra-hospital transmission of the infections to reduced admissions of non-COVID-19 patients. There are also high costs associated with the treatment of hospitalised COVID-19 patients, as well as reductions in revenues due to delayed and cancelled treatments. In this study we investigate computationally the transmission of COVID-19 inside a hospital ward that contains multiple-bed bays (with 4 or 6 beds) and multiple single-bed side rooms (that can accommodate the contacts of COVID-19-positive patients).

Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix.

In this study we investigate computationally tumour-oncolytic virus (OV) interactions that take place within a heterogeneous extracellular matrix (ECM). The ECM is viewed as a mixture of two constitutive phases, namely a fibre phase and a non-fibre phase. The multiscale mathematical model presented here focuses on the nonlocal cell-cell and cell-ECM interactions, and how these interactions might be impacted by the infection of cancer cells with the OV.

A multi-strain epidemic model for COVID-19 with infected and asymptomatic cases: Application to French data.

Many SARS-CoV-2 variants have appeared over the last months, and many more will continue to appear. Understanding the competition between these different variants could help make future predictions on the evolution of epidemics. In this study we use a mathematical model to investigate the impact of three different SARS-CoV-2 variants on the spread of COVID-19 across France, between January-May 2021 (before vaccination was extended to the full population).

Inverse problem approaches for mutation laws in heterogeneous tumours with local and nonlocal dynamics.

Cancer cell mutations occur when cells undergo multiple cell divisions, and these mutations can be spontaneous or environmentally-induced. The mechanisms that promote and sustain these mutations are still not fully understood. This study deals with the identification (or reconstruction) of the usually unknown cancer cell mutation law, which lead to the transformation of a primary tumour cell population into a secondary, more aggressive cell population.

Quantitative predictive approaches for Dupuytren disease: a brief review and future perspectives.

In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer.

Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions.

We propose and study computationally a novel non-local multiscale moving boundary mathematical model for tumour and oncolytic virus (OV) interactions when we consider the hypothesis for cancer dynamics. This spatio-temporal model focuses on two cancer cell phenotypes that can be infected with the OV or remain uninfected, and which can either move in response to the extracellular-matrix (ECM) density or proliferate. The interactions between cancer cells, those among cancer cells and ECM, and those among cells and OV occur at the macroscale.

Collective Cell Migration in a Fibrous Environment: A Hybrid Multiscale Modelling Approach.

The specific structure of the extracellular matrix (ECM), and in particular the density and orientation of collagen fibres, plays an important role in the evolution of solid cancers. While many experimental studies discussed the role of ECM in individual and collective cell migration, there are still unanswered questions about the impact of nonlocal cell sensing of other cells on the overall shape of tumour aggregation and its migration type. There are also unanswered questions about the migration and spread of tumour that arises at the boundary between different tissues with different collagen fibre orientations.

Leadership Through Influence: What Mechanisms Allow Leaders to Steer a Swarm?

Collective migration of cells and animals often relies on a specialised set of "leaders", whose role is to steer a population of naive followers towards some target. We formulate a continuous model to understand the dynamics and structure of such groups, splitting a population into separate follower and leader types with distinct orientation responses. We incorporate leader influence via three principal mechanisms: a bias in the orientation of leaders towards the destination (orientation-bias), a faster movement of leaders when moving towards the target (speed-bias), and leaders making themselves more clear to followers when moving towards the target (conspicuousness-bias).

Mathematical investigation of innate immune responses to lung cancer: The role of macrophages with mixed phenotypes.

Macrophages' role in the evolution of solid tumours is a well accepted fact, with the M1-like macrophages having an anti-tumour role and the M2-like macrophages having a pro-tumour role. Despite the fact that some clinical studies on lung tumours have emphasised also the presence of macrophages with mixed M1 and M2 phenotypes in addition to macrophages with distinct phenotypes, the majority of studies still use the distinct M1-M2 classification to predict the evolution of tumours and patient survival. In this theoretical study we use a mathematical modelling and computational approach to investigate the role of macrophages with mixed phenotype on growth/control/elimination of lung tumours.

Oncolytic viral therapies and the delicate balance between virus-macrophage-tumour interactions: A mathematical approach.

The success of oncolytic virotherapies depends on the tumour microenvironment, which contains a large number of infiltrating immune cells. In this theoretical study, we derive an ODE model to investigate the interactions between breast cancer tumour cells, an oncolytic virus (Vesicular Stomatitis Virus), and tumour-infiltrating macrophages with different phenotypes which can impact the dynamics of oncolytic viruses. The complexity of the model requires a combined analytical-numerical approach to understand the transient and asymptotic dynamics of this model.

Modelling the transmission of infectious diseases inside hospital bays: implications for COVID-19.

Healthcare associated transmission of viral infections is a major problem that has significant economic costs and can lead to loss of life. Infections with the highly contagious SARS-CoV-2 virus have been shown to have a high prevalence in hospitals around the world. The spread of this virus might be impacted by the density of patients inside hospital bays.

Directionality of Macrophages Movement in Tumour Invasion: A Multiscale Moving-Boundary Approach.

Invasion of the surrounding tissue is one of the recognised hallmarks of cancer (Hanahan and Weinberg in Cell 100: 57-70, 2000. https://doi.org/10.

Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle.

Progress in shortening the duration of tuberculosis (TB) treatment is hampered by the lack of a predictive model that accurately reflects the diverse environment within the lung. This is important as TB has been shown to produce distinct localisations to different areas of the lung during different disease stages, with the environmental heterogeneity within the lung of factors such as air ventilation, blood perfusion and oxygen tension believed to contribute to the apical localisation witnessed during the post-primary form of the disease. Building upon our previous model of environmental lung heterogeneity, we present a networked metapopulation model that simulates TB across the whole lung, incorporating these notions of environmental heterogeneity across the whole TB life-cycle to show how different stages of the disease are influenced by different environmental and immunological factors.

Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review.

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches.

Multiscale moving boundary modelling of cancer interactions with a fusogenic oncolytic virus: The impact of syncytia dynamics.

Oncolytic viral therapies is one of the new promising strategies against cancer, due to the ability of oncolytic viruses to specifically replicate inside cancer cells and kill them. There is increasing evidence that a sub-class of viruses that contain fusion proteins (triggering the formation of syncytia) can lead to better oncolytic results. Since the details of the tumour dynamics following syncytia formation are not fully understood, in this study we consider a modelling and computational approach to describe the effect of a fusogenic oncolytic virus on the multiscale dynamics of a spreading tumour.

Mathematical models of transmission dynamics and vaccine strategies in Hong Kong during the 2017-2018 winter influenza season.

Two mathematical models described by simple ordinary differential equations are developed to investigate the Hong Kong influenza epidemic during 2017-2018 winter, based on overall epidemic dynamics and different influenza subtypes. The first model, describing the overall epidemic dynamics, provides the starting data for the second model which different influenza subtypes, and whose dynamics is further investigated. Weekly data from December 2017 to May 2018 are obtained from the data base of the Centre of Health Protection in Hong Kong, and used to parametrise the models.

Multiscale modelling of cancer response to oncolytic viral therapy.

Oncolytic viruses (OV) are viruses that can replicate selectively within cancer cells and destroy them. While the past few decades have seen significant progress related to the use of these viruses in clinical contexts, the success of oncolytic therapies is dampened by the complex spatial tumour-OV interactions. In this work, we present a novel multiscale moving boundary modelling for the tumour-OV interactions, which is based on coupled systems of partial differential equations both at macro-scale (tissue-scale) and at micro-scale (cell-scale) that are connected through a double feedback link.

Non-local Parabolic and Hyperbolic Models for Cell Polarisation in Heterogeneous Cancer Cell Populations.

Tumours consist of heterogeneous populations of cells. The sub-populations can have different features, including cell motility, proliferation and metastatic potential. The interactions between clonal sub-populations are complex, from stable coexistence to dominant behaviours.