Cell monolayers have serious limitations for cell biological investigations and for cell-based assays in drug screening and toxicity studies. However, the establishment of three-dimensional cultures as a mainstream approach requires the development of reliable protocols, new cell lines and suitable imaging techniques.

The third dimension bridges the gap between cell culture and live tissue

Many types of mammalian cells can aggregate and differentiate into 3-D multicellular spheroids when cultured in suspension or a nonadhesive environment. Compared to conventional monolayer cultures, multicellular spheroids resemble real tissues better in terms of structural and functional properties. Multicellular spheroids formed by transformed cells are widely used as avascular tumor models for metastasis and invasion research and for therapeutic screening. Many primary or progenitor cells on the other hand, show significantly enhanced viability and functional performance when grown as spheroids. Multicellular spheroids in this aspect are ideal building units for tissue reconstruction. Here we review the current understanding of multicellular spheroid formation mechanisms, their biomedical applications, and recent advances in spheroid culture, manipulation, and analysis techniques.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/biot.1285

Recent advances in three-dimensional multicellular spheroid culture for biomedical research

The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.

https://www.embopress.org/doi/pdf/10.1038/msb.2009.77

Applications of genome-scale metabolic reconstructions

Over the past few years, establishment and adaptation of cell-based assays for drug development and testing has become an important topic in high-throughput screening (HTS). Most new assays are designed to rapidly detect specific cellular effects reflecting action at various targets. However, although more complex than cell-free biochemical test systems, HTS assays using monolayer or suspension cultures still reflect a highly artificial cellular environment and may thus have limited predictive value for the clinical efficacy of a compound. Today's strategies for drug discovery and development, be they hypothesis free or mechanism based, require facile, HTS-amenable test systems that mimic the human tissue environment with increasing accuracy in order to optimize preclinical and preanimal selection of the most active molecules from a large pool of potential effectors, for example, against solid tumors. Indeed, it is recognized that 3-dimensional cell culture systems better reflect the in vivo behavior of most cell types. However, these 3-D test systems have not yet been incorporated into mainstream drug development operations. This article addresses the relevance and potential of 3-D in vitro systems for drug development, with a focus on screening for novel antitumor drugs. Examples of 3-D cell models used in cancer research are given, and the advantages and limitations of these systems of intermediate complexity are discussed in comparison with both 2-D culture and in vivo models. The most commonly used 3-D cell culture systems, multicellular spheroids, are emphasized due to their advantages and potential for rapid development as HTS systems. Thus, multicellular tumor spheroids are an ideal basis for the next step in creating HTS assays, which are predictive of in vivo antitumor efficacy.

The use of 3-D cultures for high-throughput screening: the multicellular spheroid model.

Despite major scientific, medical and technological advances over the last few decades, a cure for cancer remains elusive. The disease initiation is complex, and including initiation and avascular growth, onset of hypoxia and acidosis due to accumulation of cells beyond normal physiological conditions, inducement of angiogenesis from the surrounding vasculature, tumour vascularization and further growth, and invasion of surrounding tissue and metastasis. Although the focus historically has been to study these events through experimental and clinical observations, mathematical modelling and simulation that enable analysis at multiple time and spatial scales have also complemented these efforts. Here, we provide an overview of this multiscale modelling focusing on the growth phase of tumours and bypassing the initial stage of tumourigenesis. While we briefly review discrete modelling, our focus is on the continuum approach. We limit the scope further by considering models of tumour progression that do not distinguish tumour cells by their age. We also do not consider immune system interactions nor do we describe models of therapy. We do discuss hybrid-modelling frameworks, where the tumour tissue is modelled using both discrete (cell-scale) and continuum (tumour-scale) elements, thus connecting the micrometre to the centimetre tumour scale. We review recent examples that incorporate experimental data into model parameters. We show that recent mathematical modelling predicts that transport limitations of cell nutrients, oxygen and growth factors may result in cell death that leads to morphological instability, providing a mechanism for invasion via tumour fingering and fragmentation. These conditions induce selection pressure for cell survivability, and may lead to additional genetic mutations. Mathematical modelling further shows that parameters that control the tumour mass shape also control its ability to invade. Thus, tumour morphology may serve as a predictor of invasiveness and treatment prognosis.

https://iopscience.iop.org/article/10.1088/0951-7715/23/1/R01/pdf

Nonlinear modelling of cancer: Bridging the gap between cells and tumours

A Multiscale Model for Avascular Tumor Growth

Previous studies of application usage show that the performance of collective communications are critical for high-performance computing. Despite active research in the field, both general and feasible solution to the optimization of collective communication problem is still missing. 

In this paper, we analyze and attempt to improve intra-cluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communication, such as Hockney, LogP/LogGP, and PLogP, to collective operations. We compare the predictions from models against the experimentally gathered data and using these results, construct optimal decision function for broadcast collective. We quantitatively compare the quality of the model-based decision functions to the experimentally-optimal one. Additionally, in this work, we also introduce a new form of an optimized tree-based broadcast algorithm, splitted-binary. 

Our results show that all of the models can provide useful insights into various aspects of the different algorithms as well as their relative performance. Still, based on our findings, we believe that the complete reliance on models would not yield optimal results. In addition, our experimental results have identified the gap parameter as being the most critical for accurate modeling of both the classical point-to-point-based pipeline and our extensions to fan-out topologies.

/pdf/performance-analysis-of-mpi-collective-operations-3bs7qwviid.pdf

Performance analysis of MPI collective operations

Previous studies of application usage show that the performance of collective communications are critical for high-performance computing and are often overlooked when compared to the point-to-point performance In this paper, we analyze and attempt to improve intra-cluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communication, such as Hockney, LogP/LogGP, and PLogP The predictions from the models were compared to the experimentally gathered data and our findings were used to optimize the implementation of collective operations in the FT-MPI library

/pdf/performance-analysis-of-mpi-collective-operations-3hys6czql0.pdf

This document describes extentions to the MPI-1.2 and MPI-2 standards for introducing process fault-tolerance in MPI.

http://icl.cs.utk.edu/projectsfiles/ftmpi/pubs/isc2004-FT-MPI.pdf

Extending the MPI Specification for Process Fault Tolerance on High Performance Computing Systems

With increasing numbers of processors on current machines, the probability for node or link failures is also increasing. Therefore, application-level fault tolerance is becoming more of an important issue for both end-users and the institutions running the machines. In this paper we present the semantics of a fault-tolerant version of the message passing interface MPI, the de-facto standard for communication in scientific applications, which gives applications the possibility to recover from a node or link error and continue execution in a well-defined way. We present the architecture of fault-tolerant MPI, an implementation of MPI using the semantics presented above as well as benchmark results with various applications. An example of a fault-tolerant parallel equation solver, performance results as well as the time for recovering from a process failure are furthermore detailed.

/pdf/process-fault-tolerance-semantics-design-and-applications-1kilv0znii.pdf

Jelena Pjesivac-Grbovic

Papers

A Multiscale Model for Avascular Tumor Growth

Performance analysis of MPI collective operations

Performance analysis of MPI collective operations

Extending the MPI Specification for Process Fault Tolerance on High Performance Computing Systems

Process Fault Tolerance: Semantics, Design and Applications for High Performance Computing