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At least 199 records · Page 11Linked to original sources
arXiv2013

On Systemic Destruction of Human Locomotor System

Locomotor system disorders affect a vast majority of people at some time in their life bringing pain, functional limitations, social and economic implications. Modern medicine cannot offer prevention and effective treatment for most chronic musculoskeletal conditions, because their etiology and pathogenesis are unknown. This is due to the lack of systemic understanding of the locomotor system functioning in both healthy and unhealthy states. Here we apply systems sciences to analyze the human locomotor system and develop a general theory that reveals the systemic destructive process in the locomotor system, linking together all its disorders. The systemic destruction involves adaptation and self-organization processes in the locomotor system, whose side effects introduce a positive feedback loop with nervous and vascular disturbances. Most chronic musculoskeletal conditions are just manifestations and consequences of this process. On the basis of our theoretical findings, we developed the world's first technology that effectively counteracts the systemic destruction and improves the locomotor system state at any age, also preventing problems in nervous and cardiovascular systems.

q-bio.TO↗
arXiv2013

Penetration Deep into Tissues of Reactive Oxygen Species Generated in Floating-Electrode Dielectric Barrier Discharge (FE-DBD): in Vitro Agarose Gel Model Mimicking an Open Wound

In this manuscript we present an in vitro model based on agarose gel that can be used to simulate a dirty, oily, bloody, and morphologically complex surface of, for example, an open wound. We show this models effectiveness in simulating depth of penetration of reactive species generated in plasma deep into tissue of a rat and confirm the penetration depths with agarose gel model. We envision that in the future such a model could be used to study plasma discharges (and other modalities) and minimize the use of live animals: plasma can be optimized on the agarose gel wound model and then finally verified using an actual wound.

physics.bio-ph↗
arXiv2013

Senescent fibroblasts can drive melanoma initiation and progression

Skin is one of the largest human organ systems whose primary purpose is the protection of deeper tissues. As such, the skin must maintain a homeostatic balance in the face of many microenvironmental and genetic perturbations. At its simplest, skin homeostasis is maintained by the balance between skin cell growth and death such that skin architecture is preserved. This study presents a hybrid multiscale mathematical model of normal skin (vSkin). The model focuses on key cellular and microenvironmental variables that regulate homeostatic interactions among keratinocytes, melanocytes and fibroblasts, key components of the skin. The model recapitulates normal skin structure, and is robust enough to withstand physical as well as biochemical perturbations. Furthermore, the vSkin model revealed the important role of the skin microenvironment in melanoma initiation and progression. Our experiments showed that dermal fibroblasts, which are an important source of growth factors in the skin, adopt a phenotype that facilitates cancer cell growth and invasion when they become senescent. Based on these experimental results, we incorporated senescent fibroblasts into vSkin model and showed that senescent fibroblasts transform the skin microenvironment and enhance the growth and invasion of normal melanocytes as well as early stage melanoma cells. These predictions are consistent with our experimental results as well as clinical observations. Our co-culture experiments show that the senescent fibroblasts promote the growth and invasion of non-tumorigenic melanoma cells. We also observed increased proteolytic activity in stromal fields adjacent to melanoma lesions in human histology. Collectively, senescent fibroblasts create a pro-oncogenic environment that synergizes with mutations to drive melanoma initiation and progression and should therefore be considered as a potential future therapeutic target.

q-bio.TO↗
arXiv2013

WARN: an R package for quantitative reconstruction of weaning ages in archaeological populations using bone collagen nitrogen isotope ratios

Nitrogen isotope analysis of bone collagen has been used to reconstruct the breastfeeding practices of archaeological human populations. However, weaning ages have been estimated subjectively because of a lack of both information on subadult bone collagen turnover rates and appropriate analytical models. Here, we present a model for analyzing cross-sectional delta-15N data of subadult bone collagen, which incorporates newly estimated bone collagen turnover rates and a framework of approximate Bayesian computation. Temporal changes in human subadult bone collagen turnover rates were estimated anew from data on tissue-level bone metabolism reported in previous studies. A model for reconstructing precise weaning ages was then developed and incorporating the estimated turnover rates. The model is presented as a new open source R package, WARN (Weaning Age Reconstruction with Nitrogen isotope analysis), which computes the age at the start and end of weaning, 15N-enrichment through maternal to infant tissue, and delta-15N value of collagen synthesized entirely from weaning foods with their posterior probabilities. A precise reconstruction of past breastfeeding and weaning practices over a wide range of time periods and geographic regions could make it possible to understand this unique feature of human life history and cultural diversity in infant feeding practices.

q-bio.TO↗
arXiv2013

Tumor can originate from not only rare cancer stem cells

Tumors are believed to consist of a heterogeneous population of tumor cells originating from rare cancer stem cells (CSCs). However, emerging evidences show that tumor may also originate from non-CSCs. Here, we give evidences supporting that the number of tumorigenic tumor cells is higher than the number of CSCs and tumor can also derive from non-CSCs. First, we applied an idealized mathematical model and theoretically calculated that non-CSCs could initiate tumor if their proliferation potential was adequate. Next, we demonstrated by experimental studies that 17.7%, 38.6% and 5.2% of tumor cells in murine B16 solid melanoma, H22 hepatoma and Lewis lung carcinoma, respectively, were potentially tumorigenic. We propose that the rare CSCs, if exist, are not the only origination of a tumor.

q-bio.CB↗
arXiv2013

A Systems Model of the Eco-physiological Response of Plants to Environmental Heavy Metal Concentrations

The ecophysiological response of plants to environmental heavy metal stress is indicated by the profile of its tissue HM concentrations (Cp) versus the concentration of the HM in the substrate (Cs). We report a systems biology approach to the modelling of the Cp- Cs profile using as loose analogy, the Verhulst model of population dynamics but formulated in the concentration domain. The HM is conceptualized as an ecological organism that `colonizes' the resource zone of the plant cells driven by the potential supplied by the higher HM concentration in the substrate. The infinite occupation by the HM is limited by the eventual saturation of the cellular binding sites. The solution of the differential equation results in the logistic equation, the r-K model. The model is tested for 3 metalophillic plants T.erecta, S. vulgaris and E. splendens growing in different types of substrates, contaminated to varying extents by different copper compounds. The model fitted the experimental Cp- Cs profiles well. The r, K parameter values and secondary quantities derived from them, allowed a quantification of the number of Cu binding sites per cell at saturation, the sensitivities (affinities) of these sites for Cu in the 3 experimental systems as well as the extraction of information related to the substrate phyto-availability of the Cu. Thus even though the model operates at the systems level it permits useful insights into underlying processes that ultimately derive from the cumulative molecular processes of HM homeostasis. The chief advantages of the model are its simplicity, fewer arbitrary parameters, and the non-specification of constraints on substrate and plant type.

q-bio.TO↗
arXiv2013

Exploiting ecological principles to better understand cancer progression and treatment

A small but growing number of people are finding interesting parallels between ecosystems as studied by ecologists (think of a Savanna or the Amazon rain forest or a Coral reef) and tumours1-3. The idea of viewing cancer from an ecological perspective has many implications but fundamentally, it means that we should not see cancer just as a group of mutated cells. A more useful definition of cancer is to consider it a disruption in the complex balance of many interacting cellular and microenvironmental elements in a specific organ. This perspective means that organs undergoing carcinogenesis should be seen as sophisticated ecosystems in homeostasis that cancer cells can disrupt. It also makes cancer seem even more complex but may ultimately provides isights that make it more treatable. Here we discuss how ecological principles can be used to better understand cancer progression and treatment, using several mathematical and computational models to illustrate our argument.

q-bio.TO↗
arXiv2013

Modeling the Dichotomy of the Immune Response to Cancer: Cytotoxic Effects and Tumor-Promoting Inflammation

Although the immune response is often regarded as acting to suppress tumor growth, it is now clear that it can be both stimulatory and inhibitory. The interplay between these competing influences has complex implications for tumor development and cancer dormancy. To study this biological phenomenon theoretically we construct a minimally parameterized framework that incorporates all aspects of the immune response. We combine the effects of all immune cell types, general principles of self-limited logistic growth, and the physical process of inflammation into one quantitative setting. Simulations suggest that while there are pro-tumor or antitumor immunogenic responses characterized by larger or smaller final tumor volumes, respectively, each response involves an initial period where tumor growth is stimulated beyond that of growth without an immune response. The mathematical description is non-identifiable which allows us to capture inherent biological variability in tumor growth that can significantly alter tumor-immune dynamics and thus treatment success rates. The ability of this model to predict immunomodulation of tumor growth may offer a template for the design of novel treatment approaches that exploit immune response to improve tumor suppression, including the potential attainment of an immune-induced dormant state.

q-bio.CB↗
arXiv2013

Mathematical modeling of the metastatic process

Mathematical modeling in cancer has been growing in popularity and impact since its inception in 1932. The first theoretical mathematical modeling in cancer research was focused on understanding tumor growth laws and has grown to include the competition between healthy and normal tissue, carcinogenesis, therapy and metastasis. It is the latter topic, metastasis, on which we will focus this short review, specifically discussing various computational and mathematical models of different portions of the metastatic process, including: the emergence of the metastatic phenotype, the timing and size distribution of metastases, the factors that influence the dormancy of micrometastases and patterns of spread from a given primary tumor.

q-bio.TO↗
arXiv2017

Travelling wave analysis of a mathematical model of glioblastoma growth

In this paper we analyse a previously proposed cell-based model of glioblastoma (brain tumour) growth, which is based on the assumption that the cancer cells switch phenotypes between a proliferative and motile state (Gerlee and Nelander, PLoS Comp. Bio., 8(6) 2012). The dynamics of this model can be described by a system of partial differential equations, which exhibits travelling wave solutions whose wave speed depends crucially on the rates of phenotypic switching. We show that under certain conditions on the model parameters, a closed form expression of the wave speed can be obtained, and using singular perturbation methods we also derive an approximate expression of the wave front shape. These new analytical results agree with simulations of the cell-based model, and importantly show that the inverse relationship between wave front steepness and speed observed for the Fisher equation no longer holds when phenotypic switching is considered.

q-bio.TO↗
arXiv2013

Simulating Tissue Morphogenesis and Signaling

During embryonic development tissue morphogenesis and signaling are tightly coupled. It is therefore important to simulate both tissue morphogenesis and signaling simultaneously in in silico models of developmental processes. The resolution of the processes depends on the questions of interest. As part of this chapter we will introduce different descriptions of tissue morphogenesis. In the most simple approximation tissue is a continuous domain and tissue expansion is described according to a pre-defined function of time (and possibly space). In a slightly more advanced version the expansion speed and direction of the tissue may depend on a signaling variable that evolves on the domain. Both versions will be referred to as 'prescribed growth'. Alternatively tissue can be regarded as incompressible fluid and can be described with Navier-Stokes equations. Local cell expansion, proliferation, and death are then incorporated by a source term. In other applications the cell boundaries may be important and cell-based models must be introduced. Finally, cells may move within the tissue, a process best described by agent-based models.

q-bio.TO↗
arXiv2013

Homogenization approach to water transport in plant tissues with periodic microstructures

Water flow in plant tissues takes place in two different physical domains separated by semipermeable membranes: cell insides and cell walls. The assembly of all cell insides and cell walls are termed symplast and apoplast, respectively. Water transport is pressure driven in both, where osmosis plays an essential role in membrane crossing. In this paper, a microscopic model of water flow and transport of an osmotically active solute in a plant tissue is considered. The model is posed on the scale of a single cell and the tissue is assumed to be composed of periodically distributed cells. The flow in the symplast can be regarded as a viscous Stokes flow, while Darcy's law applies in the porous apoplast. Transmission conditions at the interface (semipermeable membrane) are obtained by balancing the mass fluxes through the interface and by describing the protein mediated transport as a surface reaction. Applying homogenization techniques, macroscopic equations for water and solute transport in a plant tissue are derived. The macroscopic problem is given by a Darcy law with a force term proportional to the difference in concentrations of the osmotically active solute in the symplast and apoplast; i.e. the flow is also driven by the local concentration difference and its direction can be different than the one prescribed by the pressure gradient.

math.AP↗
arXiv2013

Compartmental analysis of nuclear imaging data for the quantification of FDG liver metabolism

This paper utilizes compartmental analysis and a statistical optimization technique in order to reduce a compartmental model describing the metabolism of labelled glucose in liver. Specifically, we first design a compartmental model for the gut providing as output the tracer concentration in the portal vein. This quantity is then used as one of the two input functions in a compartmental model for the liver. This model, in turn, provides as output the tracer coefficients quantitatively describing the effectiveness with which the labelled glucose is transported between the different compartments. For both models, the computation of the solutions for the inverse problems is performed by means of an Ant Colony Optimization algorithm. The validation of the whole process is realized by means of synthetic data simulated by solving the forward problem of the compartmental system.

q-bio.TO↗
arXiv2013

Defining a Simulation Strategy for Cancer Immunocompetence

Although there are various types of cancer treatments, none of these currently take into account the effect of ageing of the immune system and hence altered responses to cancer. Recent studies have shown that in vitro stimulation of T cells can help in the treatment of patients. There are many factors that have to be considered when simulating an organism's immunocompetence. Our particular interest lies in the study of loss of immunocompetence with age. We are trying to answer questions such as: Given a certain age of a patient, how fit is their immune system to fight cancer? Would an immune boost improve the effectiveness of a cancer treatment given the patient's immune phenotype and age? We believe that understanding the processes of immune system ageing and degradation through computer simulation may help in answering these questions. Specifically, we have decided to look at the change in numbers of naive T cells with age, as they play a important role in responses to cancer and anti-tumour vaccination. In this work we present an agent-based simulation model to understand the interactions which influence the naive T cell populations over time. Our agent model is based on existing mathematical system dynamic model, but in comparisons offers better scope for customisation and detailed analysis. We believe that the results obtained can in future help with the modelling of T cell populations inside tumours.

cs.CE↗
arXiv2013

Quantitative Analysis of the Tumor/Metastasis System and its Optimal Therapeutic Control

A mathematical model for time development of metastases and their distribution in size and carrying capacity is presented. The model is used to theoretically investigate anti-cancer therapies such as surgery and chemical treatments (cytotoxic or anti-angiogenic), in monotherapy or in combination. Quantification of the effect of surgery on the size distribution of metastatic colonies is derived. For systemic therapies, emphasis is placed on the differences between the treatment of an isolated lesion and a population of metastases. Combination therapy is addressed, in particular the problem of the drugs administration sequence. Theoretical optimal schedules are derived that show the superiority of a metronomic administration scheme (defined as a continuous administration of a given amount of drug spread during the whole therapeutic cycle) on a classical Maximum Tolerated Dose scheme (where the dose is given as a few concentrated administrations at the beginning of the cycle), for the total metastatic burden in the organism.

q-bio.TO↗
arXiv2013

Maximum Tolerated Dose Versus Metronomic Scheduling in the Treatment of Metastatic Cancers

Although optimal control theory has been used for the theoretical study of anti-cancerous drugs scheduling optimization, with the aim of reducing the primary tumor volume, the effect on metastases is often ignored. Here, we use a previously published model for metastatic development to define an optimal control problem at the scale of the entire organism of the patient. In silico study of the impact of different scheduling strategies for anti-angiogenic and cytotoxic agents (either in monotherapy or in combination) is performed to compare a low-dose, continuous, metronomic administration scheme with a more classical maximum tolerated dose schedule. Simulation results reveal differences between primary tumor reduction and control of metastases but overall suggest use of the metronomic protocol.

q-bio.TO↗
arXiv2013

Dynamic Image-Based Modelling of Kidney Branching Morphogenesis

Kidney branching morphogenesis has been studied extensively, but the mechanism that defines the branch points is still elusive. Here we obtained a 2D movie of kidney branching morphogenesis in culture to test different models of branching morphogenesis with physiological growth dynamics. We carried out image segmentation and calculated the displacement fields between the frames. The models were subsequently solved on the 2D domain, that was extracted from the movie. We find that Turing patterns are sensitive to the initial conditions when solved on the epithelial shapes. A previously proposed diffusion-dependent geometry effect allowed us to reproduce the growth fields reasonably well, both for an inhibitor of branching that was produced in the epithelium, and for an inducer of branching that was produced in the mesenchyme. The latter could be represented by Glial-derived neurotrophic factor (GDNF), which is expressed in the mesenchyme and induces outgrowth of ureteric branches. Considering that the Turing model represents the interaction between the GDNF and its receptor RET very well and that the model reproduces the relevant expression patterns in developing wildtype and mutant kidneys, it is well possible that a combination of the Turing mechanism and the geometry effect control branching morphogenesis.

q-bio.TO↗