Predicting Efficiency of Gain in Swine

Animal Scienc

Understanding and improving microbial biofuel tolerance as a result of efflux pump expression through genetic engineering and mathematical modeling

Recent advances in synthetic biology have enabled the construction of non-native metabolic pathways for production of next-generation biofuels in microbes. One such biofuel is the jet-fuel precursor α-pinene, which can be processed into high-energy pinene dimers. However, accumulation of toxic biofuels in the growth medium limits the possible fuel yield. Overexpression of transporter proteins such as efflux pumps can increase tolerance to biofuels by pumping them out of the cell, thus improving fuel yields. However, too many efflux pumps can compromise the cell as well, creating a trade-off between biofuel toxicity and pump toxicity. In this work we improve the conditions of this trade-off in order to increase pinene tolerance in E. coli. We do so by constructing strains incorporating multiple efflux pumps from a variety of organisms and then testing them for tolerance in growth assay experiments. Previous research has suggested that certain combinations of efflux pumps can confer additional tolerance compared to the individual pumps themselves. However, the functional form of the combination of the tolerance provided by each pump and the toxicity due to their simultaneous activity is unknown. Using differential equations, we developed a growth model incorporating the trade-offs between toxicity of α-pinene and efflux pump activity to describe the dynamics of bacterial growth under these conditions. By analyzing biofuel toxicity and the effects of each efflux pump independently through a series of experiments and mathematical models, we propose a functional form for their combined effect on growth rate. We model the mean exponential growth rate as a function of pump induction and biofuel concentration and compare these results to experimental data. We also apply this technique to modeling toxicity of ionic liquids, a class of corrosive salts that has emerged as and effective chemical for pretreatment of biofuel production feedstock. We compare a model for a variety of ionic liquid responsive efflux pump controllers to that of an IPTG inducible controller and show agreement with experimental data, supporting the model\u27s utility to test control schemes before conducting experiments. The overall goal of this project is to use modeling to guide design of tolerance mechanisms to improve overall biofuel yield

Generative Modelling of L\'{e}vy Area for High Order SDE Simulation

It is well known that, when numerically simulating solutions to SDEs, achieving a strong convergence rate better than O(\sqrt{h}) (where h is the step size) requires the use of certain iterated integrals of Brownian motion, commonly referred to as its "L\'{e}vy areas". However, these stochastic integrals are difficult to simulate due to their non-Gaussian nature and for a d-dimensional Brownian motion with d > 2, no fast almost-exact sampling algorithm is known. In this paper, we propose L\'{e}vyGAN, a deep-learning-based model for generating approximate samples of L\'{e}vy area conditional on a Brownian increment. Due to our "Bridge-flipping" operation, the output samples match all joint and conditional odd moments exactly. Our generator employs a tailored GNN-inspired architecture, which enforces the correct dependency structure between the output distribution and the conditioning variable. Furthermore, we incorporate a mathematically principled characteristic-function based discriminator. Lastly, we introduce a novel training mechanism termed "Chen-training", which circumvents the need for expensive-to-generate training data-sets. This new training procedure is underpinned by our two main theoretical results. For 4-dimensional Brownian motion, we show that L\'{e}vyGAN exhibits state-of-the-art performance across several metrics which measure both the joint and marginal distributions. We conclude with a numerical experiment on the log-Heston model, a popular SDE in mathematical finance, demonstrating that high-quality synthetic L\'{e}vy area can lead to high order weak convergence and variance reduction when using multilevel Monte Carlo (MLMC)

Assessment of Computational Fluid Dynamics (CFD) Models for Shock Boundary-Layer Interaction

A workshop on the computational fluid dynamics (CFD) prediction of shock boundary-layer interactions (SBLIs) was held at the 48th AIAA Aerospace Sciences Meeting. As part of the workshop numerous CFD analysts submitted solutions to four experimentally measured SBLIs. This paper describes the assessment of the CFD predictions. The assessment includes an uncertainty analysis of the experimental data, the definition of an error metric and the application of that metric to the CFD solutions. The CFD solutions provided very similar levels of error and in general it was difficult to discern clear trends in the data. For the Reynolds Averaged Navier-Stokes methods the choice of turbulence model appeared to be the largest factor in solution accuracy. Large-eddy simulation methods produced error levels similar to RANS methods but provided superior predictions of normal stresses