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Engineering microbes to produce plant-derived natural products provides an alternate solution to obtain bioactive products. Here we report a systematic approach to sequentially identify the rate-limiting steps and improve the biosynthesis of the cannabinoid precursor olivetolic acid (OLA) inYarrowia lipolytica. We find thatPseudomonas spLvaE encoding a short-chain acyl-CoA synthetase can efficiently convert hexanoic acid to hexanoyl-CoA. The co-expression of the acetyl-CoA carboxylase, the pyruvate dehydrogenase bypass, the NADPH-generating malic enzyme, as well as the activation of peroxisomal β-oxidation pathway and ATP export pathway are effective strategies to redirect carbon flux toward OLA synthesis. Implementation of these strategies led to an 83-fold increase in OLA titer, reaching 9.18 mg/L of OLA in shake flask culture. This work may serve as a baseline for engineering cannabinoids biosynthesis in oleaginous yeast species.

We consider variants of a recently developed Newton-CG algorithm for nonconvex problems (Royer, C. W. & Wright, S. J. (2018) Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization. SIAM J. Optim., 28, 1448–1477) in which inexact estimates of the gradient and the Hessian information are used for various steps. Under certain conditions on the inexactness measures, we derive iteration complexity bounds for achieving $\epsilon $-approximate second-order optimality that match best-known lower bounds. Our inexactness condition on the gradient is adaptive, allowing for crude accuracy in regions with large gradients. We describe two variants of our approach, one in which the step size along the computed search direction is chosen adaptively, and another in which the step size is pre-defined. To obtain second-order optimality, our algorithms will make use of a negative curvature direction on some steps. These directions can be obtained, with high probability, using the randomized Lanczos algorithm. In this sense, all of our results hold with high probability over the run of the algorithm. We evaluate the performance of our proposed algorithms empirically on several machine learning models. Our approach is a first attempt to introduce inexact Hessian and/or gradient information into the Newton-CG algorithm of Royer & Wright (2018, Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization. SIAM J. Optim., 28, 1448–1477).

Verification activities are intended to reduce the costs of system development by identifying design errors before deploying the system. However, subcontractors in multi‐firm projects are motivated to implement locally cost‐effective verification strategies over verification strategies that benefit the main contractor. Incentivizing verification activities is one mechanism by which the contractor can motivate subcontractors to implement verification strategies desirable to the contractor. In this paper, we present a belief‐based modeling concept for determining optimal verification strategies for general development plans. The results show that the optimal incentives are a function of the subordinate firm's beliefs and the influence exerted by the subordinate firm on the supervising firm with respect to verification activities.

Correction activities (CAs), which can take the form of redesign, rework, or repair, are essential to system development. Whereas verification activities (VAs) provide information about the state of the system, CAs modify the state of the system to facilitate its correct operation. However, existing approaches to modeling and optimizing verification strategies take a simplistic approach to CAs. Specifically, CAs are modeled as an expected cost to achieve a desired confidence level after a VA has failed and are inherent to such VAs. In this paper, we present a modeling paradigm based on Bayesian networks (BNs) that captures the effects of different types of CAs. This modeling paradigm allows for the integration of verification and correction decisions (CDs) under a common framework. The modeling paradigm is illustrated in the notional case of a communication system.