Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays.

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks.

Strain partitioning between nerves and axons: Estimating axonal strain using sodium channel staining in intact peripheral nerves.

BACKGROUND: Peripheral nerves carry afferent and efferent signals between the central nervous system and the periphery of the body. When nerves are strained above physiological levels, conduction blocks occur, resulting in debilitating loss of motor and sensory function. Understanding the effects of strain on nerve function requires knowledge of the multi-scale mechanical behaviour of the tissue, and how this is transferred to the cellular environment. NEW METHOD: The aim of this work was to establish a technique to measure the partitioning of strain between tissue and axons in axially loaded peripheral nerves. This was achieved by staining extracellular domains of sodium channels clustered at nodes of Ranvier, without altering tissue mechanical properties by fixation or permeabilisation. RESULTS: Stained nerves were imaged by multi-photon microscopy during in situ tensile straining, and digital image correlation was used to measure axonal strain with increasing tissue strain. Strain was partitioned between tissue and axon scales by an average factor of 0.55. COMPARISONS WITH EXISTING METHODS: This technique allows non-invasive probing of cell-level strain within the physiological tissue environment. CONCLUSIONS: This technique can help understand the mechanisms behind the onset of conduction blocks in injured peripheral nerves, as well as to evaluate changes in multi-scale mechanical properties in diseased nerves.