Integrating Population Heterogeneity Indices with Microfluidic Cell-Based Assays

MIL-CELL: a tool for multi-scale simulation of yeast replication and prion transmission

The single-celled baker's yeast, Saccharomyces cerevisiae, can sustain a number of amyloid-based prions, the three most prominent examples being [URE3], [PSI+], and [PIN+]. In the laboratory, haploid S. cerevisiae cells of a single mating type can acquire an amyloid prion in one of two ways (i) spontaneous nucleation of the prion within the yeast cell, and (ii) receipt via mother-to-daughter transmission during the cell division cycle. Similarly, prions can be lost due to (i) dissolution of the prion amyloid by its breakage into non-amyloid monomeric units, or (ii) preferential donation/retention of prions between the mother and daughter during cell division. Here we present a computational tool (Monitoring Induction and Loss of prions in Cells; MIL-CELL) for modelling these four general processes using a multiscale approach describing both spatial and kinetic aspects of the yeast life cycle and the amyloid-prion behavior. We describe the workings of the model, assumptions upon which it is based and some interesting simulation results pertaining to the wave-like spread of the epigenetic prion elements through the yeast population. MIL-CELL is provided as a stand-alone GUI executable program for free download with the paper. MIL-CELL is equipped with a relational database allowing all simulated properties to be searched, collated and graphed. Its ability to incorporate variation in heritable properties means MIL-CELL is also capable of simulating loss of the isogenic nature of a cell population over time. The capability to monitor both chronological and reproductive age also makes MIL-CELL potentially useful in studies of cell aging.

What do cellular responses to acidity tell us about cancer?

The notion that invasive cancer is a product of somatic evolution is a well-established theory that can be modelled mathematically and demonstrated empirically from therapeutic responses. Somatic evolution is by no means deterministic, and ample opportunities exist to steer its trajectory towards cancer cell extinction. One such strategy is to alter the chemical microenvironment shared between host and cancer cells in a way that no longer favours the latter. Ever since the first description of the Warburg effect, acidosis has been recognised as a key chemical signature of the tumour microenvironment. Recent findings have suggested that responses to acidosis, arising through a process of selection and adaptation, give cancer cells a competitive advantage over the host. A surge of research efforts has attempted to understand the basis of this advantage and seek ways of exploiting it therapeutically. Here, we review key findings and place these in the context of a mathematical framework. Looking ahead, we highlight areas relating to cellular adaptation, selection, and heterogeneity that merit more research efforts in order to close in on the goal of exploiting tumour acidity in future therapies.

Biomicrofluidic Systems for Hematologic Cancer Research and Clinical Applications

A persistent challenge in developing personalized treatments for hematologic cancers is the lack of patient specific, physiologically relevant disease models to test investigational drugs in clinical trials and to select therapies in a clinical setting. Biomicrofluidic systems and organ-on-a-chip technologies have the potential to change how researchers approach the fundamental study of hematologic cancers and select clinical treatment for individual patient. Here, we review microfluidics cell-based technology with application toward studying hematologic tumor microenvironments (TMEs) for the purpose of drug discovery and clinical treatment selection. We provide an overview of state-of-the-art microfluidic systems designed to address questions related to hematologic TMEs and drug development. Given the need to develop personalized treatment platforms involving this technology, we review pharmaceutical drugs and different modes of immunotherapy for hematologic cancers, followed by key considerations for developing a physiologically relevant microfluidic companion diagnostic tool for mimicking different hematologic TMEs for testing with different drugs in clinical trials. Opportunities lie ahead for engineers to revolutionize conventional drug discovery strategies of hematologic cancers, including integrating cell-based microfluidics technology with machine learning and automation techniques, which may stimulate pharma and regulatory bodies to promote research and applications of microfluidics technology for drug development.