Understanding Protein Networks Using Vester's Sensitivity Model.

Magnesium-based biomaterials belong to the third generation of biomaterials that are also bioactive. These smart materials combine bioactivity and biodegradability, and elicit specific cellular responses at the molecular level. In fact, osteoinductive properties have been observed in mesenchymal stem cells in the presence of Magnesium. The mechanistic understanding of the physiological effects however, remains a difficult task as Mg is involved in a multitude of biological reactions. The study of protein interactions may shed light on the molecular processes in Mg-stimulated cells, therefore, suitable data mining tools are required to analyze the large amount data generated via proteomics. Protein compositions over time between two conditions (human mesenchymal stem cells cultured with and without Mg degradation products) were analyzed using Vester's Sensitivity Model. Proteins whose dynamics significantly change from one setup to the other were classified into four categories: passive, active, critical, and buffering according to their regulatory activity. In this work, we demonstrated the use of Vester's Sensitivity Model as an appropriate data mining tool. Protein network analyses highlighted the primary role of Mg-based implant degradation on cell metabolism without deleterious effect on cell viability. Furthermore, key proteins involved in calcium-dependant cellular activities were emphasized leading to further studies.

Tight upper bounds for semi-online scheduling on two uniform machines with known optimum.

We consider a semi-online version of the problem of scheduling a sequence of jobs of different lengths on two uniform machines with given speeds 1 and s. Jobs are revealed one by one (the assignment of a job has to be done before the next job is revealed), and the objective is to minimize the makespan. In the considered variant the optimal offline makespan is known in advance. The most studied question for this online-type problem is to determine the optimal competitive ratio, that is, the worst-case ratio of the solution given by an algorithm in comparison to the optimal offline solution. In this paper, we make a further step towards completing the answer to this question by determining the optimal competitive ratio for s between [Formula: see text] and [Formula: see text], one of the intervals that were still open. Namely, we present and analyze a compound algorithm achieving the previously known lower bounds.