A performance portable implementation of the semi-Lagrangian algorithm in six dimensions

In this paper, we describe our approach to develop a simulation software application for the fully kinetic Vlasov equation which will be used to explore physics beyond the gyrokinetic model. Simulating the fully kinetic Vlasov equation requires efficient utilization of compute and storage capabilities due to the high dimensionality of the problem. In addition, the implementation needs to be extensibility regarding the physical model and flexible regarding the hardware for production runs. We start on the algorithmic background to simulate the 6-D Vlasov equation using a semi-Lagrangian algorithm. The performance portable software stack, which enables production runs on pure CPU as well as AMD or Nvidia GPU accelerated nodes, is presented. The extensibility of our implementation is guaranteed through the described software architecture of the main kernel, which achieves a memory bandwidth of almost 500 GB/s on a V100 Nvidia GPU and around 100 GB/s on an Intel Xeon Gold CPU using a single code base. We provide performance data on multiple node level architectures discussing utilized and further available hardware capabilities. Finally, the network communication bottleneck of 6-D grid based algorithms is quantified. A verification of physics beyond gyrokinetic theory for the example of ion Bernstein waves concludes the work

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints

Background: Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. Results: In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the analysis of the dataset for Raf/MEK/ERK signaling provides novel biological insights regarding the existence of feedback regulation. Conclusion: Many optimization problems considered in systems and computational biology are subject to steady-state constraints. While most optimization methods have convergence problems if these steady-state constraints are highly nonlinear, the methods presented recover the convergence properties of optimizers which can exploit an analytical expression for the parameter-dependent steady state. This renders them an excellent alternative to methods which are currently employed in systems and computational biology

The Thioredoxin-Thioredoxin Reductase System Can Function in Vivo as an Alternative System to Reduce Oxidized Glutathione in Saccharomyces cerevisiae*

Cellular mechanisms that maintain redox homeostasis are crucial, providing buffering against oxidative stress. Glutathione, the most abundant low molecular weight thiol, is considered the major cellular redox buffer in most cells. To better understand how cells maintain glutathione redox homeostasis, cells of Saccharomyces cerevisiae were treated with extracellular oxidized glutathione (GSSG), and the effect on intracellular reduced glutathione (GSH) and GSSG were monitored over time. Intriguingly cells lacking GLR1 encoding the GSSG reductase in S. cerevisiae accumulated increased levels of GSH via a mechanism independent of the GSH biosynthetic pathway. Furthermore, residual NADPH-dependent GSSG reductase activity was found in lysate derived from glr1 cell. The cytosolic thioredoxin-thioredoxin reductase system and not the glutaredoxins (Grx1p, Grx2p, Grx6p, and Grx7p) contributes to the reduction of GSSG. Overexpression of the thioredoxins TRX1 or TRX2 in glr1 cells reduced GSSG accumulation, increased GSH levels, and reduced cellular glutathione Eh′. Conversely, deletion of TRX1 or TRX2 in the glr1 strain led to increased accumulation of GSSG, reduced GSH levels, and increased cellular Eh′. Furthermore, it was found that purified thioredoxins can reduce GSSG to GSH in the presence of thioredoxin reductase and NADPH in a reconstituted in vitro system. Collectively, these data indicate that the thioredoxin-thioredoxin reductase system can function as an alternative system to reduce GSSG in S. cerevisiae in vivo

Additional file 1 of Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints

Supporting Information S1. This document provides a detailed description of the stability proof and the derivation of the different pathway models. Furthermore, the parameter and bounds are listed, the influence of the retraction factor on convergence and run time as well as the behavior of the proposed methods in the presence of multiple steady states and bifurcations is illustrated. (PDF 577 kb

A mouse bone marrow stromal cell line with skeletal stem cell characteristics to study osteogenesis in vitro and in vivo

Bone marrow stromal cells (BMSCs) are composed of progenitor and multipotent skeletal stem cells, which are able to differentiate in vitro into osteocytes, adipocytes, and chondrocytes. Mouse BMSCs (mBMSCs) are a versatile model system to investigate factors involved in BMSC differentiation in vitro and in vivo as a variety of transgenic mouse models are available. In this study, mBMSCs were isolated and osteogenic differentiation was investigated in tissue culture and in vivo. Three out of seven independent cell isolates showed the ability to differentiate into osteocytes, adipocytes, and chondrocytes in vitro. In vitro multipotency of an established mBMSC line was maintained over 45 passages. The osteogenic differentiation of this cell line was confirmed by quantitative polymerase chain reaction (qPCR) analysis of specific markers such as osteocalcin and shown to be Runx2 dependent. Notably, the cell line, when transplanted subcutaneously into mice, possesses full skeletal stem cell characteristics in vivo in early and late passages, evident from bone tissue formation, induction of vascularization, and hematopoiesis. This cell line provides, thus, a versatile tool to unravel the molecular mechanisms governing osteogenesis in vivo thereby aiding to improve current strategies in bone regenerative therapy

Cancer Cells Employ Nuclear Caspase-8 to Overcome the p53-Dependent G2/M Checkpoint through Cleavage of USP28.

Cytosolic caspase-8 is a mediator of death receptor signaling. While caspase-8 expression is lost in some tumors, it is increased in others, indicating a conditional pro-survival function of caspase-8 in cancer. Here, we show that tumor cells employ DNA-damage-induced nuclear caspase-8 to override the p53-dependent G2/M cell-cycle checkpoint. Caspase-8 is upregulated and localized to the nucleus in multiple human cancers, correlating with treatment resistance and poor clinical outcome. Depletion of caspase-8 causes G2/M arrest, stabilization of p53, and induction of p53-dependent intrinsic apoptosis in tumor cells. In the nucleus, caspase-8 cleaves and inactivates the ubiquitin-specific peptidase 28 (USP28), preventing USP28 from de-ubiquitinating and stabilizing wild-type p53. This results in de facto p53 protein loss, switching cell fate from apoptosis toward mitosis. In summary, our work identifies a non-canonical role of caspase-8 exploited by cancer cells to override the p53-dependent G2/M cell-cycle checkpoint

Cancer Cells Employ Nuclear Caspase-8 to Overcome the p53-Dependent G2/M Checkpoint through Cleavage of USP28.

Cytosolic caspase-8 is a mediator of death receptor signaling. While caspase-8 expression is lost in some tumors, it is increased in others, indicating a conditional pro-survival function of caspase-8 in cancer. Here, we show that tumor cells employ DNA-damage-induced nuclear caspase-8 to override the p53-dependent G2/M cell-cycle checkpoint. Caspase-8 is upregulated and localized to the nucleus in multiple human cancers, correlating with treatment resistance and poor clinical outcome. Depletion of caspase-8 causes G2/M arrest, stabilization of p53, and induction of p53-dependent intrinsic apoptosis in tumor cells. In the nucleus, caspase-8 cleaves and inactivates the ubiquitin-specific peptidase 28 (USP28), preventing USP28 from de-ubiquitinating and stabilizing wild-type p53. This results in de facto p53 protein loss, switching cell fate from apoptosis toward mitosis. In summary, our work identifies a non-canonical role of caspase-8 exploited by cancer cells to override the p53-dependent G2/M cell-cycle checkpoint