Adaptive Laboratory Evolution of Escherichia coli MG1655 WT and tolC Knock-out towards Bile Acid and Antibiotic Resistance

The human microbiome is composed of various commensal and pathogenic microorganisms. Collective research efforts and recent technology have revealed enormous information and contributed to the knowledge of the field so far. Studies have described the structure and functional capabilities of the human microbiome in the healthy state and in a variety of disease states. Multi-drug resistance (MDR) poses one of the greatest threats to human health worldwide as bacterial pathogens evolved to withstand antimicrobials and ever-changing environmental conditions more than ever. As the research regarding drug resistance reveals more information over the past decades, one of the most prominent intrinsic self-defense mechanisms, membrane bound tripartite bacterial multi-drug efflux protein acrAB-tolC which removes a wide range of drugs and toxic compounds taken up by bacteria is found to be greatly associated with MDR in gut microbiome. The overexpression of these efflux system causes MDR and resistance optimization is driven by mutations in regulatory genes such as marR and acrR which known to increase the expression level of the many other resistance factors including acrAB-tolC protein itself. In this master thesis, the role of tolC efflux channel and in what extend it contributes to MDR in commensal gut bacteria Escherichia coli is assessed in multiple drug evolution settings with chloramphenicol, tetracycline, bile acid mixture and deoxycholic acid using de novo adaptive laboratory evolution method. The findings showed that the absence of fully intact acrAB-tolC and especially tolC channel has a substantial decrease in drug resistance and could not be compensated with any of the most common resistance regulation factors.Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark (DTU

Optimal allocation and processing time decisions on non-identical parallel CNC machines: epsilon-constraint approach

When the processing times of jobs are controllable, selected processing times affect both the manufacturing cost and the scheduling performance. A well known example for such a case that this paper specifically deals with is the turning operation on a CNC machine. Manufacturing cost of a turning operation is a nonlinear convex function of its processing time. In this paper, we deal with making optimal machine-job assignments and processing time decisions so as to minimize total manufacturing cost while the makespan being upper bounded by a known value, denoted as E-constraint approach for a bicriteria problem. We then give optimality properties for the resulting single criterion problem. We provide alternative methods to compute cost lower bounds for partial schedules, which are used in developing an exact (branch and bound) algorithm. For the cases where the exact algorithm is not efficient in terms of computation time, we present a recovering beam search algorithm equipped with an improvement search procedure. In order to find improving search directions, the improvement search algorithm uses the proposed cost bounding properties. Computational results show that our lower bounding methods in branch and bound algorithm achieve a significant reduction in the search tree size that we need to traverse. Also, our recovering beam search and improvement search heuristics achieve solutions within 1% of the optimum on the average while they spent much less computational effort than the exact algorithm

Machining conditions-based preventive maintenance

In this study we propose an operating conditions-based preventive maintenance (PM) approach for computer numerical control (CNC) turning machines. A CNC machine wears according to how much it is used and the conditions under which it is used. Higher power or production rates result in more wear and higher failure rates. This relationship between the operating conditions and maintenance requirements is usually overlooked in the literature. On CNC turning machines we can control the machining conditions such as cutting speed and feed rate, which in turn affect the PM requirements of the CNC machine. We provide a new model to link the PM decisions to the machining conditions selection decisions, so that these two decision-making problems can be solved together by considering their impact on each other. We establish that our proposed geometric programming model captures the related cost terms along with the technological restrictions of CNC machines. The proposed preventive maintenance index function can be used to provide an intelligent CNC machine degradation assessment. Keywords: Preventive maintenance; Condition-based maintenance; CNC machines; Machining conditions selection; Geometric programming 1

Scheduling parallel CNC machines with time/cost trade-off considerations

www.elsevier.com/locate/cor When the processing times of jobs are controllable, selected processing times affect both the manufacturing cost and the scheduling performance.A well-known example for such a case that this paper specifically deals with is the turning operation on a CNC machine. Manufacturing cost of a turning operation is a nonlinear convex function of its processing time. We also know that scheduling decisions are quite sensitive to the processing times. Therefore, this paper considers minimizing total manufacturing cost (F1) and total completion time (F2) objectives simultaneously on identical parallel CNC turning machines. Since decreasing processing time of a job increases its manufacturing cost, we cannot minimize both objectives at the same time, so the problem is to generate non-dominated solutions. We consider the problem of minimizing F1 subject to a given F2 level and give an effective formulation for the problem. For this problem, we prove some optimality properties which facilitated designing an efficient heuristic algorithm to generate approximate non-dominated solutions. Computational results show that proposed algorithm performs almost equal with the GAMS/MINOS commercial solver although it spends much less computation time

Using cost change estimates in a local search heuristic for the Pollution Routing Problem

We consider the pollution routing problem (PRP) with deadlines and heterogeneous fleet for which we implement a local search heuristic using inter-route relocate, exchange and intra-route relocate moves. The subproblem of finding optimal speed levels of a truck for a given tour gives optimality properties which relate the marginal speedup costs for each leg on the tour. We use the derived optimality properties and marginal speedup costs to evaluate possible search moves and choose the most promising ones to implement in local search heuristic. Computational results show that this approach improves solution times significantly while improving solution quality for large instances

Stochastic geometric programming with an application

summary:In applications of geometric programming, some coefficients and/or exponents may not be precisely known. Stochastic geometric programming can be used to deal with such situations. In this paper, we shall indicate which stochastic programming approaches and which structural and distributional assumptions do not destroy the favorable structure of geometric programs. The already recognized possibilities are extended for a tracking model and stochastic sensitivity analysis is presented in the context of metal cutting optimization. Illustrative numerical results are reported