Effective wound repair treatments rely on a clear picture of how cell proliferation and migration are coordinated during tissue restoration. Fibroblasts are key contributors to tissue restoration in the dermis, and modern imaging tools allow their cell-cycle progression to be observed directly, enabling comparison between experiments and computational models. Here we investigate how different stages of the cell cycle influence fibroblast-driven wound closure using the Discrete Laplacian Cell Mechanics (DLCM) framework driven by time-lapse microscopy data. \textit{In vitro} assays provide cell positions, migration behaviour, and cycle-stage information, and we show that incorporating proliferation, migration, and cell cycle arrest allows the computational model to reproduce the essential experimental trends. The results reveal that arrest in the G1 phase notably impacts the cell cycle dynamics and that the initial spatial arrangement of cycle states significantly affects wound closure. By linking single-cell cycle dynamics with emergent tissue behaviour this work establishes a quantitative approach for exploring how intracellular processes shape repair processes. More broadly, it demonstrates the value of integrating high-resolution data with cell-based mechanical models and provides a foundation for systematic \textit{in silico} evaluation of therapeutic interventions.
A sensor-fused wearable assistance prototype for upper-limb function (triceps brachii and extensor pollicis brevis) is presented. The device integrates surface electromyography (sEMG), an inertial measurement unit (IMU), and flex/force sensors on an M5StickC plus an ESP32-S3 compute hub. Signals are band-pass and notch filtered; features (RMS, MAV, zero-crossings, and 4-12 Hz tremor-band power) are computed in 250 ms windows and fed to an INT8 TensorFlow Lite Micro model. Control commands are bounded by a control-barrier-function safety envelope and delivered within game-based tasks with lightweight personalization. In a pilot technical feasibility evaluation with healthy volunteers (n = 12) performing three ADL-oriented tasks, tremor prominence decreased (Delta TI = -0.092, 95% CI [-0.102, -0.079]), range of motion increased (+12.65%, 95% CI [+8.43, +13.89]), repetitions rose (+2.99 min^-1, 95% CI [+2.61, +3.35]), and the EMG median-frequency slope became less negative (Delta = +0.100 Hz/min, 95% CI [+0.083, +0.127]). The sensing-to-assist loop ran at 100 Hz with 8.7 ms median on-device latency, 100% session completion, and 0 device-related adverse events. These results demonstrate technical feasibility of embedded, sensor-fused assistance for upper-limb function; formal patient studies under IRB oversight are planned.
Charilaos Akasiadis, Miguel Ponce-de-Leon, Arnau Montagud
et al.
Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a Genetic Algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.
Xianghao Zhan, Yuzhe Liu, Samuel J. Raymond
et al.
Objective: Many recent studies have suggested that brain deformation resulting from a head impact is linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even though several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the process of brain deformation calculation and thus improve the potential for clinical applications. Methods: We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 1803 total head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. Results: The proposed deep learning head model can calculate the maximum principal strain for every element in the entire brain in less than 0.001s (with an average root mean squared error of 0.025, and with a standard deviation of 0.002 over twenty repeats with random data partition and model initialization). The contributions of various features to the predictive power of the model were investigated, and it was noted that the features based on angular acceleration were found to be more predictive than the features based on angular velocity. Conclusion: Trained using the dataset of 1803 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts. Significance: In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.
Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz
et al.
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease. The gold standard in drug development remains microscopic imaging of liver stage parasites in in vitro cell culture models. Image analysis presents a major bottleneck in this pipeline since the parasite has significant variability in size, shape, and density in these models. As with other highly variable datasets, traditional segmentation models have poor generalizability as they rely on hand-crafted features; thus, manual annotation of liver stage malaria images remains standard. To address this need, we develop a convolutional neural network architecture that utilizes spatial dropout sampling for parasite segmentation and epistemic uncertainty estimation in images of liver stage malaria. Our pipeline produces high-precision segmentations nearly identical to expert annotations, generalizes well on a diverse dataset of liver stage malaria parasites, and promotes independence between learned feature maps to model the uncertainty of generated predictions.
Helen M Byrne, Heather A Harrington, Ruth Muschel
et al.
Understanding how the spatial structure of blood vessel networks relates to their function in healthy and abnormal biological tissues could improve diagnosis and treatment for diseases such as cancer. New imaging techniques can generate multiple, high-resolution images of the same tissue region, and show how vessel networks evolve during disease onset and treatment. Such experimental advances have created an exciting opportunity for discovering new links between vessel structure and disease through the development of mathematical tools that can analyse these rich datasets. Here we explain how topological data analysis (TDA) can be used to study vessel network structures. TDA is a growing field in the mathematical and computational sciences, that consists of algorithmic methods for identifying global and multi-scale structures in high-dimensional data sets that may be noisy and incomplete. TDA has identified the effect of ageing on vessel networks in the brain and more recently proposed to study blood flow and stenosis. Here we present preliminary work which shows how TDA of spatial network structure can be used to characterise tumour vasculature.
Adrianne L. Jenner, Chae-Ok Yun, Peter S. Kim
et al.
Oncolytic virotherapy is an experimental cancer treatment that uses genetically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. While PEG modification appeared to improve plasma retention and initial infectivity it also increased the virus particle arrival time. We derive a mathematical model that describes the interaction between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy is explored.
Acute lymphoblastic leukemia (ALL) and chronic lymphocytic leukemia (CLL) are two major forms of leukemia that arise from lymphoid cells (LCs). ALL occurs mostly in children and CLL occurs mainly in old people. However, the Philadelphia-chromosome-positive ALL (Ph+-ALL) and the Ph-like ALL occur in both children and adults. To understand childhood leukemia/lymphoma, we have recently proposed two hypotheses on the causes and the mechanism of cell transformation of a LC. Hypothesis A is: repeated bone-remodeling during bone-growth and bone-repair may be a source of cell injuries of marrow cells including hematopoietic stem cells (HSCs), myeloid cells, and LCs. Hypothesis B is: a LC may have three pathways on transformation: a slow, a rapid, and an accelerated. We discuss in the present paper the developing mechanisms of ALL and CLL by these hypotheses. Having a peak incidence in young children, ALL may develop mainly as a result of rapid cell transformation of a lymphoblast (or pro-lymphocyte). Differently, Ph+-ALL and Ph-like ALL may develop as results of transformation of a lymphoblast via accelerated pathway. Occurring mainly in adults, CLL may be a result of transformation of a memory B-cell via slow pathway. By causing cell injuries of HSCs and LCs, repeated bone-remodeling during bone-growth and bone-repair may be related to the cell transformation of a LC. In conclusion, ALL may develop as a result of cell transformation of a lymphoblast via rapid or accelerated pathway; and repeated bone-remodeling during bone-growth may be a trigger for the cell transformation of a lymphoblast in a child.
Giulia Pinton, Angelo Ferraro, Massimo Balma
et al.
In this study, we investigated the effects of specific low frequency electromagnetic fields sequences on U937 cells, an in vitro model of human monocyte/macrophage differentiation. U937 cells were exposed to electromagnetic stimulation by means of the SyntheXer system using two similar sequences, XR-BC31 and XR-BC31/F. Each sequence was a time series of twenty-nine wave segments. Here, we report that exposure (4 days, once a day) of U937 cells to the XR-BC31 setting, but not to the XR-BC31/F, resulted in increased expression of the histone demethylase KDM6B along with a global reduction in histone H3 lysine 27 (H3K27) tri-methylation. Furthermore, exposure to the XR-BC31 sequence induced differentiation of U937 cells towards a macrophage-like phenotype displaying a KDM6B dependent increase in expression and secretion of the anti-inflammatory interleukins (ILs), IL-10 and IL-4. Importantly, all the observed changes were highly dependent on the sequence's nature. Our results open a new way of interpretation for the effects of low frequency electromagnetic fields observed in vivo. Indeed, it is conceivable that a specific low frequency electromagnetic fields treatment may cause changes in chromatin accessibility and consequently in the expression of anti-inflammatory mediators and in cell differentiation.
Lymphomas are a large group of neoplasms developed from lymphoid cells (LCs) in lymph nodes (LNs) or lymphoid tissues (LTs). Some forms of lymphomas, including Burkitt lymphoma (BL), ALK+ anaplastic large cell lymphoma (ALK+-ALCL), and T-cell lymphoblastic lymphoma/leukemia (T-LBL), occur mainly in children and teenagers. Hodgkin's lymphoma (HL) has a peak incidence at age 20s. To understand pediatric lymphoma, we have recently proposed two hypotheses on the causes and the mechanism of cell transformation of a LC. Hypothesis A is: repeated bone-remodeling during bone-growth and bone-repair may be a source of cell injuries of marrow cells including hematopoietic stem cells (HSCs), myeloid cells, and LCs, and thymic involution may be a source of damage to the developing T-cells in thymus. Hypothesis B is: a LC may have three pathways on transformation: a slow, a rapid, and an accelerated. In this paper, we discuss pediatric lymphomas by this hypothesis. Having a peak incidence at young age, BL, T-LBL, ALK+-ALCL, and HL develop more likely as a result of rapid transformation of a LC. In BL, ALK+-ALCL, and HL, the cell transformations may be triggered by severe viral infections. In T-LBL, the cell transformation may be related to thymic involution. Occurring in both adults and children, diffuse large B-cell lymphoma (DLBCL) may develop via slow or accelerated pathway. In conclusion, pediatric lymphoma may develop as a result of "one-step" cell transformation of a LC, and severe viral infections may be the main trigger for the rapid transformation of a LC in a LN/LT.
Cancer is a disease of cellular regulation, often initiated by genetic mutation within cells, and leading to a heterogeneous cell population within tissues. In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success. Selection in this process is imposed by both the microenvironment (neighboring cells, extracellular matrix, and diffusing substances), and the whole of the organism through for example the blood supply. In this view, the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change, the more their environment will change. Furthermore, instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels. This coupling between cell, microenvironment, and organism results in behavior that is hard to predict. Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue. We demonstrate that stages of tumor development emerge naturally, without the need for sequential mutation of specific genes. Secondly, we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype, showing a clear distinction between the fitness at the cell level and survival of the population. This provides new insights into potential side effects of recent tumor vasculature renormalization approaches.
Tibor Bakacs, Katalin Kristof, Jitendra Mehrishi
et al.
The US and Hungarian statistical records of the years 1900 and 1896, respectively, before the dramatic medical advances, show 32% and 27% deaths attributable to infections, whereas only 5% and 2% due to cancer. These data can be interpreted to mean that (i) the immune system evolved for purging nascent selfish cells, which establish natural chimerism littering the soma and the germline by conspecific alien cells and (ii) defense against pathogens that represent xenogeneic aliens appeared later in evolution. `Liberating' T cells from the semantic trap of immunity and the shackles of the `two-signal' model of T cell activation, we point out theoretical grounds that the immune response to cancer is conceptually different from the immune response to infection. We argue for a one-signal model (with stochastic influences) as the explanation for T cell activation in preference to the widely accepted two-signal model of co-stimulation. Convincing evidence for our one-signal model emerged from the widespread autoimmune adverse events in 64.2% of advanced melanoma patients treated with the anti-CTLA-4 antibody (ipilimumab) that blocks an immune checkpoint. Harnessing the unleashed autoimmune power of T cells could be rewarding to defeat cancer. Assuming that immunization against isogeneic tumors also would be effective is a fallacy.
Cyanobacteria forming one-dimensional filaments are paradigmatic model organisms of the transition between unicellular and multicellular living forms. Under nitrogen limiting conditions, in filaments of the genus Anabaena, some cells differentiate into heterocysts, which lose the possibility to divide but are able to fix environmental nitrogen for the colony. These heterocysts form a quasi-regular pattern in the filament, representing a prototype of patterning and morphogenesis in prokaryotes. Recent years have seen advances in the identification of the molecular mechanism regulating this pattern. We use this data to build a theory on heterocyst pattern formation, for which both genetic regulation and the effects of cell division and filament growth are key components. The theory is based on the interplay of three generic mechanisms: local autoactivation, early long range inhibition, and late long range inhibition. These mechanisms can be identified with the dynamics of hetR, patS and hetN expression. Our theory reproduces quantitatively the experimental dynamics of pattern formation and maintenance for wild type and mutants. We find that hetN alone is not enough to play the role as the late inhibitory mechanism: a second mechanism, hypothetically the products of nitrogen fixation supplied by heterocysts, must also play a role in late long range inhibition. The preponderance of even intervals between heterocysts arises naturally as a result of the interplay between the timescales of genetic regulation and cell division. We also find that a purely stochastic initiation of the pattern, without a two-stage process, is enough to reproduce experimental observations.
A central question in developmental biology is how size and position are determined. The genetic code carries instructions on how to control these properties in order to regulate the pattern and morphology of structures in the developing organism. Transcription and protein translation mechanisms implement these instructions. However, this cannot happen without some manner of sampling of epigenetic information on the current patterns and morphological forms of structures in the organism. Any rigorous description of space- and time-varying patterns and morphological forms reduces to one among various classes of spatio-temporal partial differential equations. Reaction-transport equations represent one such class. Starting from simple Fickian diffusion, the incorporation of reaction, phase segregation and advection terms can represent many of the patterns seen in the animal and plant kingdoms. Morphological form, requiring the development of three-dimensional structure, also can be represented by these equations of mass transport, albeit to a limited degree. The recognition that physical forces play controlling roles in shaping tissues leads to the conclusion that (nonlinear) elasticity governs the development of morphological form. In this setting, inhomogeneous growth drives the elasticity problem. The combination of reaction-transport equations with those of elasto-growth makes accessible a potentially unlimited spectrum of patterning and morphogenetic phenomena in developmental biology. This perspective communication is a survey of the partial differential equations of mathematical physics that have been proposed to govern patterning and morphogenesis in developmental biology. Several numerical examples are included to illustrate these equations and the corresponding physics, with the intention of providing physical insight wherever possible.
Bartlomiej Waclaw, Ivana Bozic, Meredith E. Pittman
et al.
Most cancers in humans are large, measuring centimeters in diameter, composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is their homogeneity - virtually every neoplastic cell within a large cancer contains the same core set of genetic alterations, with heterogeneity confined to mutations that have emerged after the last clonal expansions. How such clones expand within the spatially-constrained three dimensional architecture of a tumor, and come to dominate a large, pre-existing lesion, has never been explained. We here describe a model for tumor evolution that shows how short-range migration and cell turnover can account for rapid cell mixing inside the tumor. With it, we show that even a small selective advantage of a single cell within a large tumor allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides novel insights into spatial and temporal aspects of tumor growth but also suggests that targeting short range cellular migratory activity could have dramatic effects on tumor growth rates.
Jana L. Gevertz, Zahra Aminzare, Kerri-Ann Norton
et al.
Practically, all chemotherapeutic agents lead to drug resistance. Clinically, it is a challenge to determine whether resistance arises prior to, or as a result of, cancer therapy. Further, a number of different intracellular and microenvironmental factors have been correlated with the emergence of drug resistance. With the goal of better understanding drug resistance and its connection with the tumor microenvironment, we have developed a hybrid discrete-continuous mathematical model. In this model, cancer cells described through a particle-spring approach respond to dynamically changing oxygen and DNA damaging drug concentrations described through partial differential equations. We thoroughly explored the behavior of our self-calibrated model under the following common conditions: a fixed layout of the vasculature, an identical initial configuration of cancer cells, the same mechanism of drug action, and one mechanism of cellular response to the drug. We considered one set of simulations in which drug resistance existed prior to the start of treatment, and another set in which drug resistance is acquired in response to treatment. This allows us to compare how both kinds of resistance influence the spatial and temporal dynamics of the developing tumor, and its clonal diversity. We show that both pre-existing and acquired resistance can give rise to three biologically distinct parameter regimes: successful tumor eradication, reduced effectiveness of drug during the course of treatment (resistance), and complete treatment failure.
Turing mechanisms can yield a large variety of patterns from noisy, homogenous initial conditions and have been proposed as patterning mechanism for many developmental processes. However, the molecular components that give rise to Turing patterns have remained elusive, and the small size of the parameter space that permits Turing patterns to emerge makes it difficult to explain how Turing patterns could evolve. We have recently shown that Turing patterns can be obtained with a single ligand if the ligand-receptor interaction is taken into account. Here we show that the general properties of ligand-receptor systems result in very large Turing spaces. Thus, the restriction of receptors to single cells, negative feedbacks, regulatory interactions between different ligand-receptor systems, and the clustering of receptors on the cell surface all greatly enlarge the Turing space. We further show that the feedbacks that occur in the FGF10/SHH network that controls lung branching morphogenesis are sufficient to result in large Turing spaces. We conclude that the cellular restriction of receptors provides a mechanism to sufficiently increase the size of the Turing space to make the evolution of Turing patterns likely. Additional feedbacks may then have further enlarged the Turing space. Given their robustness and flexibility, we propose that receptor-ligand based Turing mechanisms present a general mechanism for patterning in biology.