pwc-archive/papers-with-abstracts huggingface dataset:
The architecture of co-culture spheroids regulates tumor invasion within a 3D extracellular matrix
Authors: Yu Ling Huang, Carina Shiau, Cindy Wu, Jeffrey E. Segall, Mingming Wu
Date: 2020-02-09 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Tumor invasion, the process by which tumor cells break away from their primary tumor and gain access to vascular systems, is an important step in cancer metastasis. Most current 3D tumor invasion assays consisted of single tumor cells embedded within an extracellular matrix (ECM). These assays taught us much of what we know today on how key biophysical (e.g. ECM stiffness) and biochemical (e.g. cytokine gradients) parameters within the tumor microenvironment guided and regulated tumor invasion. One limitation of the single tumor cell invasion assay was that it did not account for cell-cell adhesion within the tumor. In this article, we developed a micrometer scale 3D co-culture spheroid invasion assay that was compatible with microscopic imaging. Micrometer scale co-culture spheroids (1:1 ratio of metastatic breast cancer MDA-MB-231 and non-tumorigenic epithelial MCF-10A cells) were made using an array of microwells, and then were embedded within a collagen matrix in a microfluidic platform. Real time imaging of tumor spheroid invasion revealed that the spatial distribution of the two cell types within the tumor spheroid critically regulated tumor invasion. This work linked tumor architecture with tumor invasion and highlighted the importance of the biophysical cues within the bulk of the tumor in tumor invasion.
The weakest link bridging germinal center B cells and follicular dendritic cells limits antibody affinity maturation
Authors: Rajat Desikan, Rustom Antia, Narendra M. Dixit
Date: 2020-02-07 00:00:00

The affinity of antibodies (Abs) produced in vivo for their target antigens (Ags) is typically well below the maximum affinity possible. Nearly 25 years ago, Foote and Eisen explained how an 'affinity ceiling' could arise from constraints associated with the acquisition of soluble antigen by B cells. However, recent studies have shown that B cells in germinal centers (where Ab affinity maturation occurs) acquire Ag not in soluble form but presented as receptor-bound immune complexes on follicular dendritic cells (FDCs). How the affinity ceiling arises in such a scenario is unclear. Here, we argue that the ceiling arises from the weakest link of the chain of protein complexes that bridges B cells and FDCs and is broken during Ag acquisition. This hypothesis explains the affinity ceiling realized in vivo and suggests that strengthening the weakest link could raise the ceiling and improve Ab responses.
The double dealing of cyclin D1
Authors: Guergana Tchakarska, Brigitte Sola
Date: 2020-01-03 00:00:00

The cell cycle is tightly regulated by cyclins and their catalytic moieties, the cyclin-dependent kinases (CDKs). Cyclin D1, in association with CDK4/6, acts as a mitogenic sensor and integrates extracellular mitogenic signals and cell cycle progression. When deregulated (overexpressed, accumulated, inappropriately located), cyclin D1 becomes an oncogene and is recognized as a driver of solid tumors and hemopathies. Recent studies on the oncogenic roles of cyclin D1 reported non-canonical functions dependent on the partners of cyclin D1 and its location within tumor cells or tissues. Support for these new functions was provided by various mouse models of oncogenesis. Finally, proteomic and transcriptomic data identified complex cyclin D1 networks. This review focuses on these aspects of cyclin D1 pathophysiology, which may be crucial for targeted therapy.
Chromatin Structure Changes in Human Disease: A Mini Review
Authors: Yuriy Shckorbatov
Date: 2019-12-06 00:00:00

There are many experimental data indicating the correlations of the changes in high level of organization of chromatin in human cells and changes in the state of the whole organism related to disease, state of tiredness or aging. In our previous work: arXiv.org-2018 (1812.00186) we analyzed the publications on the topic up to 2017. In this work we focused on works upon the problem of connection of the state of chromatin with human diseases published in 2018-2019. In the modern literature the most attention is paid to problem of chromatin transformations in different forms of cancer, Alzheimer'r disease, and hereditary diseases. Summing up, the tendency of scientific research of noncommunicable diseases is shifting towards investigation of aspects of nuclear regulation of disease origin, connected with conformation of chromatin.
A statistical-inference approach to reconstruct inter-cellular interactions in cell-migration experiments
Authors: Elena Agliari, Pablo J. Sáez, Adriano Barra, Matthieu Piel, Pablo Vargas, Michele Castellana
Date: 2019-12-04 00:00:00

Migration of cells can be characterized by two, prototypical types of motion: individual and collective migration. We propose a statistical-inference approach designed to detect the presence of cell-cell interactions that give rise to collective behaviors in cell-motility experiments. Such inference method has been first successfully tested on synthetic motional data, and then applied to two experiments. In the first experiment, cell migrate in a wound-healing model: when applied to this experiment, the inference method predicts the existence of cell-cell interactions, correctly mirroring the strong intercellular contacts which are present in the experiment. In the second experiment, dendritic cells migrate in a chemokine gradient. Our inference analysis does not provide evidence for interactions, indicating that cells migrate by sensing independently the chemokine source. According to this prediction, we speculate that mature dendritic cells disregard inter-cellular signals that could otherwise delay their arrival to lymph vessels.
A RBA model for the chemostat modeling
Authors: Marc Dinh, Vincent Fromion
Date: 2019-11-15 00:00:00
Tasks: model

The purpose of this paper is to show that it is possible to replace Monod's type model of a chemostat by a constraint based model of bacteria at the genome scale. This new model is an extension of the RBA model of bacteria developed in a batch mode to the chemostat. This new model, and the associated framework, leads to a dramatic improvement in the prediction capacities of the chemostat behaviour. Indeed, for example, the internal states of the bacteria are now part of the prediction outputs and the chemostat behaviour can now be predicted for any limiting source. Finally, the first interests of this new predictive method are illustrated on a set of classic situations where predictions are already close of the well-known biological observations about chemostat. This paper is an extended version of [8] that includes a discussion on the modeling assumptions.
Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow
Authors: Geir Halnes, Klas H. Pettersen, Leiv Øyehaug, Marie E. Rognes, Gaute T. Einevoll
Date: 2016-06-09 00:00:00

We review modeling of astrocyte ion dynamics with a specific focus on the implications of so-called spatial potassium buffering, where excess potassium in the extracellular space (ECS) is transported away to prevent pathological neural spiking. The recently introduced Kirchoff-Nernst-Planck (KNP) scheme for modeling ion dynamics in astrocytes (and brain tissue in general) is outlined and used to study such spatial buffering. We next describe how the ion dynamics of astrocytes may regulate microscopic liquid flow by osmotic effects and how such microscopic flow can be linked to whole-brain macroscopic flow. We thus include the key elements in a putative multiscale theory with astrocytes linking neural activity on a microscopic scale to macroscopic fluid flow.
Modeling stripe formation on growing zebrafish tailfins
Authors: Alexandria Volkening, Madeline R Abbott, Dorothy Catey, Neil Chandra, Bethany Dubois, Francesca Lim, Bjorn Sandstede
Date: 2019-11-09 00:00:00

As zebrafish develop, black and gold stripes form across their skin due to the interactions of brightly colored pigment cells. These characteristic patterns emerge on the growing fish body, as well as on the anal and caudal fins. While wild-type stripes form parallel to a horizontal marker on the body, patterns on the tailfin gradually extend distally outward. Interestingly, several mutations lead to altered body patterns without affecting fin stripes. Through an exploratory modeling approach, our goal is to help better understand these differences between body and fin patterns. By adapting a prior agent-based model of cell interactions on the fish body, we present an in silico study of stripe development on tailfins. Our main result is a demonstration that two cell types can produce stripes on the caudal fin. We highlight several ways that bone rays, growth, and the body-fin interface may be involved in patterning, and we raise questions for future work related to pattern robustness.
Triangulation sensing: how cells recover a source from diffusing particles in three dimensions
Authors: Ulrich Dobramysl, David Holcman
Date: 2019-11-07 00:00:00
Tasks: Position

How can cells embedded into a gradient concentration triangulate the position of the source and migrate toward their final destination? The source triangulation requires to recover the three dimensional coordinates of the source from the fluxes of diffusing cues at narrow windows (receptors) located on the surface of a cell. We develop here a method to address this question and we show in the limit of fast binding rate to the receptors, that at least three receptors are necessary. We solve the steady-state diffusion equation using an asymptotic approach, which agrees with hybrid stochastic-analytical simulations. Interestingly, with an accuracy of few percent, the source cannot be located if it is located at a distance tens of time the size of the cell. Finally, the precision of the source recovery increases with the number of receptors.
Asymptotic expansion approximation for spatial structure arising from directionally biased movement
Authors: Michael J Plank
Date: 2019-10-22 00:00:00

Spatial structure can arise in spatial point process models via a range of mechanisms, including neighbour-dependent directionally biased movement. This spatial structure is neglected by mean-field models, but can have important effects on population dynamics. Spatial moment dynamics are one way to obtain a deterministic approximation of a dynamic spatial point process that retains some information about spatial structure. However, the applicability of this approach is limited by the computational cost of numerically solving spatial moment dynamic equations at a sufficient resolution. We present an asymptotic expansion for the equilibrium solution to the spatial moment dynamics equations in the presence of neighbour-dependent directional bias. We show that the asymptotic expansion provides a highly efficient scheme for obtaining approximate equilibrium solutions to the spatial moment dynamics equations when bias is weak. This scheme will be particularly useful for performing parameter inference on spatial moment models.
Maximal information transmission is compatible with ultrasensitive biological pathways
Authors: Gabriele Micali, Robert G. Endres
Date: 2019-11-01 00:00:00

Cells are often considered input-output devices that maximize the transmission of information by converting extracellular stimuli (input) via signaling pathways (communication channel) to cell behavior (output). However, in biological systems outputs might feed back into inputs due to cell motility, and the biological channel can change by mutations during evolution. Here, we show that the conventional channel capacity obtained by optimizing the input distribution for a fixed channel may not reflect the global optimum. In a new approach we analytically identify both input distributions and input-output curves that optimally transmit information, given constraints from noise and the dynamic range of the channel. We find a universal optimal input distribution only depending on the input noise, and we generalize our formalism to multiple outputs (or inputs). Applying our formalism to Escherichia coli chemotaxis, we find that its pathway is compatible with optimal information transmission despite the ultrasensitive rotary motors.
Quantitative analysis of the dynamics of maternal gradients of the early Drosophila embryo
Authors: Victoria Yu. Samuta, Alexander V. Spirov
Date: 2019-09-01 00:00:00

The predetermination, formation and maintenance of the primary morphogenetic gradient (bicoid gradient) of the early Drosophila embryo involves many interrelated processes. Here we focus on a system-biological analysis of the processes of redistribution of bicoid mRNA in an early embryo. The results of a quantitative analysis of experimental data, together with the results of their dynamic modeling, substantiate the role of active transport in the redistribution of bicoid mRNA.
A Multiphase Model of Growth Factor-Regulated Atherosclerotic Cap Formation
Authors: Michael G. Watson, Helen M. Byrne, Charlie Macaskill, Mary R. Myerscough
Date: 2019-08-08 00:00:00

Atherosclerosis is characterised by the growth of fatty plaques in the inner (intimal) layer of the artery wall. In mature plaques, vascular smooth muscle cells (SMCs) are recruited from the adjacent medial layer to deposit a cap of fibrous collagen over the fatty plaque core. The fibrous cap isolates the thrombogenic content of the plaque from the bloodstream and prevents the formation of blood clots that cause myocardial infarction or stroke. Despite the important protective role of the cap, the mechanisms that regulate cap formation and maintenance are not well understood. It remains unclear why certain caps become stable, while others become vulnerable to rupture. We develop a multiphase PDE model with non-standard boundary conditions to investigate collagen cap formation by SMCs in response to growth factor signals from the endothelium. Diffusible platelet-derived growth factor (PDGF) stimulates SMC migration, proliferation and collagen degradation, while diffusible transforming growth factor (TGF)-$\beta$ stimulates SMC collagen synthesis and inhibits collagen degradation. The model SMCs respond haptotactically to gradients in the collagen phase and have reduced rates of migration and proliferation in dense collagenous tissue. The model, which is parameterised using a range of in vivo and in vitro experimental data, reproduces several observations from studies of plaque growth in atherosclerosis-prone mice. Numerical simulations and model analysis demonstrate that a stable cap can be formed by a relatively small SMC population and emphasise the critical role of TGF-$\beta$ in effective cap formation and maintenance. These findings provide unique insight into the cellular and biochemical mechanisms that may lead to plaque destabilisation and rupture. This work represents an important step towards the development of a comprehensive in silico plaque.
Kinetic models with non-local sensing determining cell polarization and speed according to independent cues
Authors: Nadia Loy, Luigi Preziosi
Date: 2019-06-17 00:00:00

Cells move by run and tumble, a kind of dynamics in which the cell alternates runs over straight lines and re-orientations. This erratic motion may be influenced by external factors, like chemicals, nutrients, the extra-cellular matrix, in the sense that the cell measures the external field and elaborates the signal eventually adapting its dynamics. We propose a kinetic transport equation implementing a velocity-jump process in which the transition probability takes into account a double bias, which acts, respectively, on the choice of the direction of motion and of the speed. The double bias depends on two different non-local sensing cues coming from the external environment. We analyze how the size of the cell and the way of sensing the environment with respect to the variation of the external fields affect the cell population dynamics by recovering an appropriate macroscopic limit and directly integrating the kinetic transport equation. A comparison between the solutions of the transport equation and of the proper macroscopic limit is also performed.
Reassessment of the basis of cell size control based on analysis of cell-to-cell variability
Authors: Giuseppe Facchetti, Benjamin Knapp, Fred Chang, Martin Howard
Date: 2019-02-13 00:00:00

Fundamental mechanisms governing cell size control and homeostasis are still poorly understood. The relationship between sizes at division and birth in single cells is used as a metric to categorize the basis of size homeostasis [1-3]. Cells dividing at a fixed size regardless of birth size (sizer) are expected to show a division-birth slope of 0, whereas cells dividing after growing for a fixed size increment (adder) have an expected slope of +1 [4]. These two theoretical values are, however, rarely experimentally observed. For example, rod-shaped fission yeast $\it{Schizosaccharomyces}$ $\it{pombe}$ cells, which divide at a fixed surface area [5, 6], exhibit a division-birth slope for cell lengths of 0.25$\pm$0.02, significantly different from the expected sizer value of zero. Here we investigate possible reasons for this discrepancy by developing a mathematical model of sizer control including the relevant sources of variation. Our results support $\it{pure}$ sizer control and show that deviation from zero slope is exaggerated by measurement of an inappropriate geometrical quantity (e.g., length instead of area), combined with cell-to-cell radius variability. The model predicts that mutants with greater errors in size sensing or septum positioning paradoxically appear to behave as better sizers. Furthermore, accounting for cell width variability, we show that pure sizer control can in some circumstances reproduce the apparent adder behaviour observed in $\it{E. coli}$. These findings demonstrate that analysis of geometric variation can lead to new insights into cell size control.
grenepipe: A flexible, scalable, and reproducible pipeline to automate variant and frequency calling from sequence reads
Authors: Lucas Czech, Moises Exposito-Alonso
Date: 2021-03-28 00:00:00

Processing high-throughput DNA sequencing data of individuals or populations requires stringing together independent software tools with many parameters, often leading to non-reproducible pipelines and datasets. We developed grenepipe to streamline this data processing, an all-in-one Snakemake workflow from raw sequencing data to the end product of a table of individuals' genotypes or population frequencies. Our pipeline allows users to select among a range of popular software tools within a single configuration file, automatically downloads and installs software and dependencies, and runs with two command calls: to prepare and to run. It is highly optimized for scalability in cluster environments and parallel computing, splitting data tasks into manageable genomic sections and automatically consolidating the outputs. grenepipe is published under the GPL-3 license, and freely available at https://github.com/moiexpositoalonsolab/grenepipe.
COSINE: A Web Server for Clonal and Subclonal Structure Inference and Evolution in Cancer Genomics
Authors: Xiguo Yuan, Yuan Zhao, Yang Guo, Linmei Ge, Wei Liu, Shiyu Wen, Qi Li, Zhangbo Wan, Peina Zheng, Tao Guo, Zhida Li, Martin Peifer, Yupeng Cun
Date: 2021-03-28 00:00:00

Cancers evolve from mutation of a single cell with sequential clonal and subclonal expansion of somatic mutation acquisition. Inferring clonal and subclonal structures from bulk or single cell tumor genomic sequencing data has a huge impact on cancer evolution studies. Clonal state and mutational order can provide detailed insight into tumor origin and its future development. In the past decade, a variety of methods have been developed for subclonal reconstruction using bulk tumor sequencing data. As these methods have been developed in different programming languages and using different input data formats, their use and comparison can be problematic. Therefore, we established a web server for clonal and subclonal structure inference and evolution of cancer genomic data (COSINE), which included 12 popular subclonal reconstruction methods. We decomposed each method via a detailed workflow of single processing steps with a user-friendly interface. To the best of our knowledge, this is the first web server providing online subclonal inference, including the most popular subclonal reconstruction methods. COSINE is freely accessible at www.clab-cosine.net or http://bio.rj.run:48996/cun-web.
Sequencing by Emergence: Modeling and Estimation
Authors: Nicholas Boyd, Samuel Woodhouse, Kalim Mir
Date: 2021-03-18 00:00:00

Sequencing by Emergence (SEQE) is a new single-molecule nucleic acid (DNA/RNA) sequencing technology that estimates sequence as an emergent property of the binding and localization of a repertoire of short oligonucleotide probes. SEQE promises to deliver accurate, ultra-long, haplotype-phased reads at the whole genome-scale for very low cost within 10 minutes. The data SEQE generates requires entirely new inference techniques. In this paper we introduce a probabilistic model of the SEQE measurement process and an algorithm that estimates sequence by solving a convex relaxation of the corresponding maximum likelihood problem. We demonstrate the effectiveness of our algorithm on a variety of simulated datasets.
Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data
Authors: Lingsong Meng, Dorina Avram, George Tseng, Zhiguang Huo
Date: 2021-03-18 00:00:00
Tasks: Clustering, Epidemiology

The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subtypes obtained from existing approaches are not necessarily associated with clinical outcomes. With the rich clinical data along with the omics data in modern epidemiology cohorts, it is urgent to develop an outcome-guided clustering algorithm to fully integrate the phenotypic data with the high-dimensional omics data. Hence, we extended a sparse K-means method to an outcome-guided sparse K-means (GuidedSparseKmeans) method. An unified objective function was proposed, which was comprised of (i) weighted K-means to perform sample clusterings; (ii) lasso regularizations to perform gene selection from the high-dimensional omics data; (iii) incorporation of a phenotypic variable from the clinical dataset to facilitate biologically meaningful clustering results. By iteratively optimizing the objective function, we will simultaneously obtain a phenotype-related sample clustering results and gene selection results. We demonstrated the superior performance of the GuidedSparseKmeans by comparing with existing clustering methods in simulations and applications of high-dimensional transcriptomic data of breast cancer and Alzheimer's disease. Our algorithm has been implemented into an R package, which is publicly available on GitHub (https://github.com/LingsongMeng/GuidedSparseKmeans).