pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic model of HIV infection with immune system response of T-lymphocytes, B-cells and dendritic cells: a review
Authors: Miguel Ramos Pascual
Date: 2020-08-05 00:00:00

A dynamic model of non-lineal time-dependent ordinary differential equations (ODE) has been applied to the interactions of a HIV infection with the immune system cells. This model has been simplified into two compartments: lymph node and peripheral blood. The model includes CD4 T-lymphocytes in several states (quiescent Q, naive N and activated T), cytotoxic CD8 T-cells, B-cells and dendritic cells. Cytokines and immunoglobulins specific for each antigen (i.e. gp41 or p24) have been also included in the model, modelling the atraction effect of CD4 T-cells to the infected area and the reduction of virus concentration by immunoglobulins. HIV virus infection of CD4 T-lymphocytes is modelled in several stages: before fusion as HIV-attached (H) and after fusion as non-permissive / abortively infected (M), and permissive / latently infected (L) and permissive / actively infected (I). These equations have been implemented in a C++/Python interface application, called Immune System app, which runs Open Modelica software to solve the ODE system through a 4th order Runge-Kutta numerical approximation. Results of the simulation show that although HIV virus concentration in both compartments is lower than $10^{-10}$ virus/$\mu L$ after t=2 years, quiescent lymphocytes reach an equilibrium with a concentration lower than the initial conditions, due to the latency state, which serves as a reservoir in time of virus production. As a conclusion, this model can provide reliable results in other conditions, such as antiviral therapies.
A deep reinforcement learning model based on deterministic policy gradient for collective neural crest cell migration
Authors: Yihao Zhang, Zhaojie Chai, Yubing Sun, George Lykotrafitis
Date: 2020-07-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Modeling cell interactions such as co-attraction and contact-inhibition of locomotion is essential for understanding collective cell migration. Here, we propose a novel deep reinforcement learning model for collective neural crest cell migration. We apply the deep deterministic policy gradient algorithm in association with a particle dynamics simulation environment to train agents to determine the migration path. Because of the different migration mechanisms of leader and follower neural crest cells, we train two types of agents (leaders and followers) to learn the collective cell migration behavior. For a leader agent, we consider a linear combination of a global task, resulting in the shortest path to the target source, and a local task, resulting in a coordinated motion along the local chemoattractant gradient. For a follower agent, we consider only the local task. First, we show that the self-driven forces learned by the leader cell point approximately to the placode, which means that the agent is able to learn to follow the shortest path to the target. To validate our method, we compare the total time elapsed for agents to reach the placode computed using the proposed method and the time computed using an agent-based model. The distributions of the migration time intervals calculated using the two methods are shown to not differ significantly. We then study the effect of co-attraction and contact-inhibition of locomotion to the collective leader cell migration. We show that the overall leader cell migration for the case with co-attraction is slower because the co-attraction mitigates the source-driven effect. In addition, we find that the leader and follower agents learn to follow a similar migration behavior as in experimental observations. Overall, our proposed method provides useful insight on how to apply reinforcement learning techniques to simulate collective cell migration.
Anisotropic Diffusion and Traveling Waves of Toxic Proteins in Neurodegenerative Diseases
Authors: P. G. Kevrekidis, Travis Thompson, Alain Goriely
Date: 2020-07-05 00:00:00

Neurodegenerative diseases are closely associated with the amplification and invasion of toxic proteins. In particular Alzheimer's disease is characterized by the systematic progression of amyloid-$\beta$ and $\tau$-proteins in the brain. These two protein families are coupled and it is believed that their joint presence greatly enhances the resulting damage. Here, we examine a class of coupled chemical kinetics models of healthy and toxic proteins in two spatial dimensions. The anisotropic diffusion expected to take place within the brain along axonal pathways is factored in the models and produces a filamentary, predominantly one-dimensional transmission. Nevertheless, the potential of the anisotropic models towards generating interactions taking advantage of the two-dimensional landscape is showcased. Finally, a reduction of the models into a simpler family of generalized Fisher-Kolmogorov-Petrovskii-Piskunov (FKPP) type systems is examined. It is seen that the latter captures well the qualitative propagation features, although it may somewhat underestimate the concentrations of the toxic proteins.
Mechanism underlying dynamic scaling properties observed in the contour of spreading epithelial monolayer
Authors: Toshiki Oguma, Hisako Takigawa-Imamura, Takashi Miura
Date: 2020-07-03 00:00:00
Tasks: parameter estimation

We found evidence of dynamic scaling in the spreading of MDCK monolayer, which can be characterized by the Hurst exponent ${\alpha} = 0.86$ and the growth exponent ${\beta} = 0.73$, and theoretically and experimentally clarified the mechanism that governs the contour shape dynamics. During the spreading of the monolayer, it is known that so-called "leader cells" generate the driving force and lead the other cells. Our time-lapse observations of cell behavior showed that these leader cells appeared at the early stage of the spreading, and formed the monolayer protrusion. Informed by these observations, we developed a simple mathematical model that included differences in cell motility, cell-cell adhesion, and random cell movement. The model reproduced the quantitative characteristics obtained from the experiment, such as the spreading speed, the distribution of the increment, and the dynamic scaling law. Analysis of the model equation revealed that the model could reproduce the different scaling law from ${\alpha} = 0.5, {\beta} = 0.25$ to ${\alpha} = 0.9, {\beta} = 0.75$, and the exponents ${\alpha}, {\beta}$ were determined by the two indices: $\rho t$ and $c$. Based on the analytical result, parameter estimation from the experimental results was achieved. The monolayer on the collagen-coated dishes showed a different scaling law ${\alpha} = 0.74, {\beta} = 0.68$, suggesting that cell motility increased by 9 folds. This result was consistent with the assay of the single-cell motility. Our study demonstrated that the dynamics of the contour of the monolayer were explained by the simple model, and proposed a new mechanism that exhibits the dynamic scaling property.
Predicting trajectories and mechanisms of antibiotic resistance evolution
Authors: Fernanda Pinheiro, Omar Warsi, Dan I. Andersson, Michael Lässig
Date: 2020-07-02 00:00:00

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. We map metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of dosage-dependent growth rates and resistance levels, as well as the prevalent resistance mechanism depending on drug and nutrient levels. These predictions are confirmed by empirical growth curves and genomic data of E. coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.
Inferring differentiation order in adaptive immune responses from population level data
Authors: Alexander S. Miles, Philip D. Hodgkin, Ken R. Duffy
Date: 2019-08-09 00:00:00

A hallmark of the adaptive immune response is the proliferation of pathogen-specific lymphocytes that leave in their wake a long lived population of cells that provide lasting immunity. A subject of ongoing investigation is when during an adaptive immune response those memory cells are produced. In two ground-breaking studies, Buchholz et al. (Science, 2013) and Gerlach et al. (Science, 2013) employed experimental methods that allowed identification of offspring from individual lymphocytes in vivo, which we call clonal data, at a single time point. Through the development, application and fitting of a mathematical model, Buchholz et al. (Science, 2013) concluded that, if memory is produced during the expansion phase, memory cell precursors are made before the effector cells that clear the original pathogen. We sought to determine the general validity and power of the modeling approach introduced in Buchholz et al. (Science, 2013) for quickly evaluating differentiation networks by adapting it to make it suitable for drawing inferences from more readily available non-clonal phenotypic proportion time-courses. We first established the method drew consistent deductions when fit to the non-clonal data in Buchholz et al. (Science, 2013) itself. We fit a variant of the model to data reported in Badovinac et al. (J. Immun., 2007), Schlub et al. (Immun. & Cell Bio., 2010), and Kinjo et al. (Nature Commun., 2015) with necessary simplifications to match different reported data in these papers. The deduction from the model was consistent with that in Buchholz et al. (Science, 2013), albeit with questionable parameterizations. An alternative possibility, supported by the data in Kinjo et al. (Nature Commun., 2015), is that memory precursors are created after the expansion phase, which is a deduction not possible from the mathematical methods provided in Buchholz et al. (Science, 2013).
A Mathematical Description of Bacterial Chemotaxis in Response to Two Stimuli
Authors: Jeungeun Park, Zahra Aminzare
Date: 2020-06-01 00:00:00
Tasks: TAR, Vocal Bursts Valence Prediction

Bacteria are often exposed to multiple stimuli in complex environments, and their efficient chemotactic decisions are critical to survive and grow in their native environments. Bacterial responses to the environmental stimuli depend on the ratio of their corresponding chemoreceptors. By incorporating the signaling machinery of individual cells, we analyze the collective motion of a population of Escherichia coli bacteria in response to two stimuli, mainly serine and methyl-aspartate (MeAsp), in a one-dimensional and a two-dimensional environment, which is inspired by experimental results in Y. Kalinin et al., J. Bacteriol. 192(7):1796-1800, 2010. Under suitable conditions, we show that if the ratio of the main chemoreceptors of individual cells, namely Tar/Tsr is less than a specific threshold, the bacteria move to the gradient of serine, and if the ratio is greater than the threshold, the group of bacteria move toward the gradient of MeAsp. Finally, we examine the theory with Monte-Carlo agent-based simulations, and verify that our results qualitatively agree well with the experimental results in Y. Kalinin et al. (2010).
Transcriptional profiling reveals fundamental differences in iPS-derived progenitors of endothelial cells (PECs) versus adult circulating EPCs
Authors: Elmira Jalilian, William Raimes
Date: 2020-05-04 00:00:00
Tasks: Diagnostic, Translation

There are a number of different stem cell sources that have the potential to be used as therapeutics in vascular degenerative diseases. On the one hand, there are so called endothelial progenitor cells (EPCs), which are typically derived from adult blood. They carry the marker CD34, but the true nature and definition of EPCs is still controversial. On the other hand, there are embryonic precursors of endothelial cells (PECs), which also express CD34, and can be differentiated from embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) in vitro. In this study, it was aimed to compare these two different CD34 positive cell populations by full genome transcriptional profiling (RNAseq). To this end, we firstly optimised a PEC differentiation protocol and found that vascular endothelial growth factor (VEGF) is critical for the transition of cells from mesodermal precursors to PECs. Additionally, principal component analysis (PCA) of RNAseq data showed that blood-derived EPCs clustered far from iPS-derived PECs which illustrates these populations are fundamentally different. This data will be useful to better define these cell populations and facilitating the translation of regenerative approaches in this field as well as providing potentially novel diagnostic tools.
A Bayesian traction force microscopy method with automated denoising in a user-friendly software package
Authors: Yunfei Huang, Gerhard Gompper, Benedikt Sabass
Date: 2020-05-04 00:00:00
Tasks: Denoising, L2 Regularization

Adherent biological cells generate traction forces on a substrate that play a central role for migration, mechanosensing, differentiation, and collective behavior. The established method for quantifying this cell-substrate interaction is traction force microscopy (TFM). In spite of recent advancements, inference of the traction forces from measurements remains very sensitive to noise. However, suppression of the noise reduces the measurement accuracy and the spatial resolution, which makes it crucial to select an optimal level of noise reduction. Here, we present a fully automated method for noise reduction and robust, standardized traction-force reconstruction. The method, termed Bayesian Fourier transform traction cytometry, combines the robustness of Bayesian L2 regularization with the computation speed of Fourier transform traction cytometry. We validate the performance of the method with synthetic and real data. The method is made freely available as a software package with a graphical user-interface for intuitive usage.
Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies
Authors: Paolo Fagone, Rosella Ciurleo, Salvo Danilo Lombardo, Carmelo Iacobello, Concetta Ilenia Palermo, Yehuda Shoenfeld, Klaus Bendtzen, Placido Bramanti, Ferdinando Nicoletti
Date: 2020-05-03 00:00:00
Tasks: Management

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) has posed a serious threat to global health. As no specific therapeutics are yet available to control disease evolution, more in-depth understanding of the pathogenic mechanisms induced by SARS-CoV-2 will help to characterize new targets for the management of COVID-19. The present study identified a specific set of biological pathways altered in primary human lung epithelium upon SARS-CoV-2 infection, and a comparison with SARS-CoV from the 2003 pandemic was studied. The transcriptomic profiles were also exploited as possible novel therapeutic targets, and anti-signature perturbation analysis predicted potential drugs to control disease progression. Among them, Mitogen-activated protein kinase kinase (MEK), serine-threonine kinase (AKT), mammalian target of rapamycin (mTOR) and I kappa B Kinase (IKK) inhibitors emerged as candidate drugs. Finally, sex-specific differences that may underlie the higher COVID-19 mortality in men are proposed.
Stochastic and nonstochastic descriptions of the 2019-2020 measles outbreak worldwide with an emphasis in Mexico
Authors: A. Vivanco-Lira, R. Luna-Banenelli
Date: 2020-04-04 00:00:00

Measles is an infectious disease caused by the Morbilivirus Measles Virus which has accompanied the human race since the 4th millennium BC, it is a disease usually concerning the paediatric population and in the past, before the advent of vaccination, almost all the population suffered from it, and in some cases the complications derived from this disease, such as central nervous involvement. Vaccination changed the course of the disease worldwide and diminished the associated comorbidities and mortality; in Mexico the vaccination program commenced in the decade of the 1970s and was successful in preventing peaks of infection. Nevertheless, due to various factors, has the world seen measles outbreaks once more, this commencing in the year 2019 and extending towards the year 2020. Here we make account of the biology and the pathophysiology of the viral infection, and present three models: one concerning the dynamics of the cases by means of a continuous method and a discrete stochastic model; one concerning the cellular compartmentalization behaviour of the virus, that is the viral tropism towards certain cell types in the host and the tendencies in extended or complicated infection; the last one concerning geographic behaviour of the virus, regarding in particular the tendencies in Mexico City, those involved at a global scale, and finally a model providing a prediction of the viral genotypes' distribution worldwide.
Prediction of cellular burden with host-circuit models
Authors: Evangelos-Marios Nikolados, Andrea Y. Weiße, Diego A. Oyarzún
Date: 2020-04-02 00:00:00
Tasks: Prediction, Robust Design

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognised bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modelling paradigm can facilitate fast and robust design cycles in synthetic biology.
Second-order Phase Transition in Phytoplankton Trait Dynamics
Authors: Jenny Held, Tom Lorimer, Francesco Pomati, Ruedi Stoop, Carlo Albert
Date: 2020-04-01 00:00:00

Key traits of unicellular species, like cell size, often follow scale-free or self-similar distributions, hinting at the possibility of an underlying critical process. However, linking such empirical scaling laws to the critical regime of realistic individual-based model classes is difficult. Here we reveal new empirical scaling evidence associated with a transition in the population and chlorophyll dynamics of phytoplankton. We offer a possible explanation for these observations by deriving scaling laws in the vicinity of the critical point of a new universality class of non-local cell growth and division models. This `criticality hypothesis' can be tested through new scaling predictions derived for our model class, for the response of chlorophyll distributions to perturbations. The derived scaling laws may also be generalized to other cellular traits and environmental drivers relevant to phytoplankton ecology.
A Mathematical Model of Platelet Aggregation in an Extravascular Injury Under Flow
Authors: Kathryn G. Link, Matthew G. Sorrells, Nicholas A. Danes, Keith B. Neeves, Karin Leiderman, Aaron L. Fogelson
Date: 2020-03-04 00:00:00

We present the first mathematical model of flow-mediated primary hemostasis in an extravascular injury, which can track the process from initial deposition to occlusion. The model consists of a system of ordinary differential equations (ODE) that describe platelet aggregation (adhesion and cohesion), soluble-agonist-dependent platelet activation, and the flow of blood through the injury. The formation of platelet aggregates increases resistance to flow through the injury, which is modeled using the Stokes-Brinkman equations. Data from analogous experimental (microfluidic flow) and partial differential equation models informed parameter values used in the ODE model description of platelet adhesion, cohesion, and activation. This model predicts injury occlusion under a range of flow and platelet activation conditions. Simulations testing the effects of shear and activation rates resulted in delayed occlusion and aggregate heterogeneity. These results validate our hypothesis that flow-mediated dilution of activating chemical ADP hinders aggregate development. This novel modeling framework can be extended to include more mechanisms of platelet activation as well as the addition of the biochemical reactions of coagulation, resulting in a computationally efficient high throughput screening tool.
Adhesion-driven patterns in a calcium-dependent model of cancer cell movement
Authors: Katerina Kaouri, Vasiliki Bitsouni, Andreas Buttenschön, Rüdiger Thul
Date: 2020-03-01 00:00:00

Cancer cells exhibit increased motility and proliferation, which are instrumental in the formation of tumours and metastases. These pathological changes can be traced back to malfunctions of cellular signalling pathways, and calcium signalling plays a prominent role in these. We formulate a new model for cancer cell movement which for the first time explicitly accounts for the dependence of cell proliferation and cell-cell adhesion on calcium. At the heart of our work is a non-linear, integro-differential (non-local) equation for cancer cell movement, accounting for cell diffusion, advection and proliferation. We also employ an established model of cellular calcium signalling with a rich dynamical repertoire that includes experimentally observed periodic wave trains and solitary pulses. The cancer cell density exhibits travelling fronts and complex spatial patterns arising from an adhesion-driven instability (ADI). We show how the different calcium signals and variations in the strengths of cell-cell attraction and repulsion shape the emergent cellular aggregation patterns, which are a key component of the metastatic process. Performing a linear stability analysis, we identify parameter regions corresponding to ADI. These regions are confirmed by numerical simulations, which also reveal different types of aggregation patterns and these patterns are significantly affected by \ca. Our study demonstrates that the maximal cell density decreases with calcium concentration, while the frequencies of the calcium oscillations and the cell density oscillations are approximately equal in many cases. Furthermore, as the calcium levels increase the speed of the travelling fronts increases, which is related to a higher cancer invasion potential. These novel insights provide a step forward in the design of new cancer treatments that may rely on controlling the dynamics of cellular calcium.