In-Vivo Real-Time Control of Protein Expression from Endogenous and Synthetic Gene Networks

We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells. We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We then built an automated platform based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. The computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our approach can be used to control expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available

silicon based technology for ligand receptor molecular identification

One of the most important goals in the fields of biology and medicine is the possibility to dispose of efficient tools for the characterization of the extraordinary complexity of ligand-receptor interactions. To approach this theme, we explored the use of crystalline silicon (cSi) technology for the realization of a biotechnological device in which the ligand-receptor interactions are revealed by means of optical measurements. Here, we describe a chemical procedure for the functionalization of microwell etched on silicon wafers, and the subsequent anchoring of biological molecules like an antibody anti-A20 murine lymphoma cell line. The optical analysis of the interaction on the biochips between the bound biomolecule and their corresponding ligand indicated that the functionalized cSi is suitable for this application

Microfluidics assisted platforms for biotechnological applications

The aim of this PhD work is to exploit microfluidics features to improve the performances of some lab-on-a-chip designed for biotechnological applications: microfabrication techniques developed in the frame of telecommunication systems have by far found many other fields of applications, in particular optical sensing of chemical substances. The experience developed in design, fabricate, and test optical components or MEMS systems, can be successfully applied to the realization of lab-on-a-chip for specific scopes: two kind of microfluidics circuits for biotechnology have been considered, one integrated with the microarray technology, and the other devoted to cell manipulation

Article Numerical Optimization of a Microfluidic Assisted Microarray for the Detection of Biochemical Interactions

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Model Adaptation with Least-Squares SVM for Adaptive Hand Prosthetics

Abstract — The state-of-the-art in control of hand prosthetics is far from optimal. The main control interface is represented by surface electromyography (EMG): the activation potentials of the remnants of large muscles of the stump are used in a nonnatural way to control one or, at best, two degrees-of-freedom. This has two drawbacks: first, the dexterity of the prosthesis is limited, leading to poor interaction with the environment; second, the patient undergoes a long training time. As more dexterous hand prostheses are put on the market, the need for a finer and more natural control arises. Machine learning can be employed to this end. A desired feature is that of providing a pre-trained model to the patient, so that a quicker and better interaction can be obtained. To this end we propose model adaptation with least-squares SVMs, a technique that allows the automatic tuning of the degree of adaptation. We test the effectiveness of the approach on a database of EMG signals gathered from human subjects. We show that, when pre-trained models are used, the number of training samples needed to reach a certain performance is reduced, and the overall performance is increased, compared to what would be achieved by starting from scratch. I