Pass-back chain extension expands multimodular assembly line biosynthesis.

Modular nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) enzymatic assembly lines are large and dynamic protein machines that generally effect a linear sequence of catalytic cycles. Here, we report the heterologous reconstitution and comprehensive characterization of two hybrid NRPS-PKS assembly lines that defy many standard rules of assembly line biosynthesis to generate a large combinatorial library of cyclic lipodepsipeptide protease inhibitors called thalassospiramides. We generate a series of precise domain-inactivating mutations in thalassospiramide assembly lines, and present evidence for an unprecedented biosynthetic model that invokes intermodule substrate activation and tailoring, module skipping and pass-back chain extension, whereby the ability to pass the growing chain back to a preceding module is flexible and substrate driven. Expanding bidirectional intermodule domain interactions could represent a viable mechanism for generating chemical diversity without increasing the size of biosynthetic assembly lines and challenges our understanding of the potential elasticity of multimodular megaenzymes

Deep Reinforcement Learning-based Image Captioning with Embedding Reward

Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a "policy network" and a "value network" to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics

Two-stage Continual Reassessment Method and Patient Heterogeneity for Dose-finding Studies

The continual reassessment method (CRM) is a widely used model-based design in Phase I dose-finding studies. This dissertation examines two extensions of CRM: one is a two-stage method and the other is a method that accounts for patient heterogeneity. Originally proposed in the Bayesian framework, CRM starts by testing the first patient at the prior guess of the maximum tolerated dose (MTD). However, there are safety concerns with this approach as practitioners often prefer to start from the lowest dose level and are reluctant to escalate to higher dose levels without testing the lower ones with a sufficient number of patients. This calls for a two-stage design, where the model-based phase is preceded by a pre-specified dose escalation phase, and the phase transitions when any dose-limiting toxicity (DLT) occurs. In the first part of this dissertation, I propose a theoretical framework to build a two-stage CRM based on the coherence principle and prove the unique existence of the most conservative and coherent initial design. An accompanying calibration algorithm is formulated to facilitate design implementation. We demonstrate that by using real trial examples, the algorithm yields designs with competitive performance compared to the conventional design which uses a much more labor intensive trial-and-error approach. Furthermore, we show that this algorithm can be applied in a timely and reproducible manner. In addition to the two-stage method, we also take into account of patient's heterogeneity in drug metabolism rate that can result in different susceptibility to drug toxicity. This led to a risk-adjusting design for identifying patient-specific MTDs. The existing dose-finding designs which incorporate patient heterogeneity deal either with only categorical risk factor or with continuous risk factor using models based on strong parametric assumptions. We propose a method that uses a flexible semi-parametric model to identify patient-specific MTDs, adjusting for either categorical or continuous risk factor. Initially, our method assigns dose to patients using the aforementioned two-stage CRM ignoring any patient heterogeneity, and tests the risk effect as trial proceeds. It then transitions to a risk-adjusting stage only if sufficient risk effect on toxicity outcome is observed. The performance of this multi-stage design is evaluated under various scenarios, using dosing accuracy measures calculated based on the final model estimate at the end of a trial and on the intra-trial dose allocation. The results are compared to the conventional two-stage CRM without considering patient heterogeneity. Simulation results demonstrate a substantial improvement in dosing accuracy in scenarios where there are true risk effects on toxicity probability; and in situations where risk factors do not have an effect, the performance of the proposed method is also comparable to that of the conventional design

Selection of the initial design for the two-stage continual reassessment method

The continual reassessment method (CRM) was proposed in a Bayesian framework whereby the first patient is assigned to the prior guess of the maximum tolerated dose which is usually not the lowest dose level. This assignment may lead to safety concerns in practice because physicians usually prefer not to skip lower dose levels before escalating to the higher dose levels. The two-stage CRM was proposed to address such concern whereby model based dose escalation is preceded by a pre-specified escalating sequence starting from the lowest dose level. While a theoretical framework to build the two-stage CRM has been proposed, the selection of the initial dose escalating sequence, generally referred to as the initial design, remains arbitrary, either by specifing cohorts of three patients or by trial and error through extensive simulations. Motivated by a currently ongoing oncology dose finding study for which physicians stated their desire to start from the lowest dose even though the maximum tolerated dose was thought to be one of the higher dose levels, we proposed a systematic approach for selecting the initial design for the two-stage CRM. The initial design obtained using the proposed algorithm yields better operating characteristics compared to using a cohort of three initial design with a calibrated CRM. The proposed algorithm simplifies and provides a systematic approach for the selection of initial design for the two-stage CRM. Moreover, initial designs to be used as reference for planning a two-stage CRM are provided