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Howley, E.

Publications and source records attributed to Howley, E..

2 recordsLinked to original sources

Stress-dependent growth of breast cancer models emerges from a cellular volume checkpoint

Mechanoresponsive cell proliferation is a feature of growing tumours, despite the suppression of many other regulatory checkpoints in cancer, but the underlying cell-scale mechanisms driving this behaviour have not yet been established. In this study we propose a biophysical model for cell growth as governed by actively controlled osmolarity, which we integrate with a discrete particle framework to simulate growth and remodelling of breast cancer spheroids. Confinement and biomechanical feedback from the extracellular environment are analysed through a neuralnetwork-accelerated finite element solver. Combining the framework with experiments, our model reveals that stress-dependent spheroid growth can arise from a sizing checkpoint for mitosis. Under sufficient extracellular loading, cell growth is restricted by high hydrostatic forces in competition with osmotic pressure from biomolecule synthesis, which prevents cells from surpassing a critical volume. Our model provides new insight into mechanosensitive growth arrest in breast cancer, potentially serving as a computational tool for analysing growth in a wider range of normal and malignant biological tissues.

cancer biology↗

PyADM1: a Python implementation of Anaerobic Digestion Model No. 1

Anaerobic Digestion (AD) is a waste treatment technology widely used for wastewater and solid waste treatment, with the advantage of being a source of renewable energy in the form of biogas. Anaerobic digestion model number 1 (ADM1) is the most common mathematical model available for AD modelling. Commercial software implementations of ADM1 are available but have limited flexibility and availability due to the closed sources and licensing fees. Python is the fastest growing programming language and is open source freely available. Python implementation of ADM1 makes this AD model available to the mass user base of the Python ecosystem and its libraries. The open easy to use implementation in PyADM1 makes it more accessible and provides possibilities for flexible direct use of the model linked to other software, e.g. machine learning libraries or Linux operating system on embedded hardware.

systems biology↗