Data-driven model discovery and model selection for noisy biological systems.

Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy).

Robotic arm-assisted medial compartment knee arthroplasty is a cost-effective intervention at ten-year follow-up.

Aims: The aim of this study was to perform an incremental cost-utility analysis and assess the impact of differential costs and case volume on the cost-effectiveness of robotic arm-assisted medial unicompartmental knee arthroplasty (rUKA) compared to manual (mUKA).

Methods: Ten-year follow-up of patients who were randomized to rUKA (n = 64) or mUKA (n = 65) was performed. Patients completed the EuroQol five-dimension health questionnaire preoperatively, at three months, and one, two, five, and ten years postoperatively, which was used to calculate quality-adjusted life years (QALY) gained and the incremental cost-effectiveness ratio (ICER).

Affinity maturation of antibody responses is mediated by differential plasma cell proliferation.

Increased antibody affinity over time after vaccination, known as affinity maturation, is a prototypical feature of immune responses. Recent studies have shown that a diverse collection of B cells, producing antibodies with a wide spectrum of different affinities, are selected into the plasma cell (PC) pathway. How affinity-permissive selection enables PC affinity maturation remains unknown.

Transition paths across the EMT landscape are dictated by network logic.

During development and cancer metastasis, cells transition reversibly from epithelial to mesenchymal via intermediate cell states during epithelial-mesenchymal transition (EMT). EMT is controlled by gene regulatory networks (GRNs) and can be described by a three-node GRN that permits tristable EMT landscapes. In this GRN, multiple inputs regulate the transcription factor ZEB that induces EMT.