Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Logistics for decision support - an application in cases of natural disasters

Considering that every disaster is unique and doing research during natural disasters is difficult, we show the possibility to learn from solutions of logistics research. One main problem of logistics planning in cases of natural disasters is relief good distribution. While this problem belongs to the class of vehicle routing problems (VRP) under uncertainty, the task of transshipping relief goods needs to be considered more intensively. Especially, assigning trucks to doors to load or discharge has a high impact on the performance of a transshipment facility. In our approach, known and specially developed scheduling heuristics are applied to logistic operations in disaster relief. Therefore, we transfer the given problem of relief goods handling to transshipment of less-than-truckload (LTL) items. Here, our main objective is to improve efficiency of handling relief goods in terminals under consideration of urgent shipments. Based on limited resources for cargo handling in disaster areas, various weights and sizes of relief goods, time-sensitive shipments like medicaments or food, improper truck arrival times and different departure times, we apply and evaluate several strategies to schedule trucks in the unloading process. We apply and compare strategies to schedule incoming trucks, buffer areas and handling equipment like forklift trucks based on real data. Therefore, we developed fast decision algorithms to react to uncertain and changing information. Additionally, six different scheduling heuristics were developed to solve the complex problem of planning the utilization of unloading gates, one buffer area belonging to each unloading zone and a fleet of forklift trucks. The algorithms allow the implementation into a real-time system