A fine balance among key biophysical factors is required for recovery of bipolar mitotic spindle from monopolar and multipolar abnormalities.

During mitosis, equal partitioning of chromosomes into two daughter cells requires assembly of a bipolar mitotic spindle. Because the spindle poles are each organized by a centrosome in animal cells, centrosome defects can lead to monopolar or multipolar spindles. However, the cell can effectively recover the bipolar spindle by separating the centrosomes in monopolar spindles and clustering them in multipolar spindles. To interrogate how a cell can separate and cluster centrosomes as needed to form a bipolar spindle, we developed a biophysical model, based on experimental data, which uses effective potential energies to describe key mechanical forces driving centrosome movements during spindle assembly. Our model identified general biophysical factors crucial for robust bipolarization of spindles that start as monopolar or multipolar. These factors include appropriate force fluctuation between centrosomes, balance between repulsive and attractive forces between centrosomes, exclusion of the centrosomes from the cell center, proper cell size and geometry, and a limited centrosome number. Consistently, we found experimentally that bipolar centrosome clustering is promoted as mitotic cell aspect ratio and volume decrease in tetraploid cancer cells. Our model provides mechanistic explanations for many more experimental phenomena and a useful theoretical framework for future studies of spindle assembly.

A collaborative learning health system agent-based model: Computational and face validity.

INTRODUCTION: Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) - network organizations that allow all healthcare stakeholders to collaborate at scale - are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity. METHODS: CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient- and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter - the degree to which patients influence other patients - and trace its effects on patient engagement, shared knowledge, and outcomes. RESULTS: The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes. CONCLUSIONS: We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.