Over-expression of Adenine Nucleotide Translocase 1 (ANT1) Induces Apoptosis and Tumor Regression in vivo

Background: Adenine nucleotide translocase (ANT) is located in the inner mitochondrial membrane and catalyzes the exchange of mitochondrial ATP for cytosolic ADP. ANT has been known to be a major component of the permeability transition pore complex of mitochondria and contributes to mitochondria-mediated apoptosis. Human ANT has four isoforms (ANT1, ANT2, ANT3, and ANT4), and the expression of the ANT isoforms is variable depending on the tissue and cell type, developmental stage, and proliferation status. Among the isoforms, ANT1 is highly expressed in terminally-differentiated tissues, but expressed in low levels in proliferating cells, such as cancer cells. In particular, over-expression of ANT1 induces apoptosis in cultured tumor cells. Methods: We applied an ANT1 gene transfer approach to induce apoptosis and to evaluate the anti-tumor effect of ANT1 in a nude mouse model. Results: We demonstrated that ANT1 transfection induced apoptosis of MDA-MB-231 cells, inactivated NF-κB activity, and increased Bax expression. ANT1-inducing apoptosis was accompanied by the disruption of mitochondrial membrane potential, cytochrome c release and the activation of caspases-9 and -3. Moreover, ANT1 transfection significantly suppressed tumor growth in vivo. Conclusion: Our results suggest that ANT1 transfection may be a useful therapeutic modality for the treatment of cancer

A data-driven optimization framework for routing mobile medical facilities

We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage

On multi-objective evolutionary algorithms

In this chapter Multi-Objective Evolutionary Algorithms (MOEAs) are introduced and some details discussed. It starts by giving a presentation of some of the concepts in which this type of algorithms are based on, in order to address the multi-objective nature of the real-world problems. Then, a summary of the main algorithms behind these approaches and their applications is provided, together with a brief discussion including their advantages and disadvantages, the degree of applicability, and some known applications. Finally, future trends in this area and some possible paths for further research are pointed out