No abstract

Synthetic biology is a useful tool to investigate the dynamics of small biological networks and to assess our capacity to predict their behavior from computational models. In this work we report the construction of three different synthetic networks in Escherichia coli based upon the incoherent feed-forward loop architecture. The steady state behavior of the networks was investigated experimentally and computationally under different mutational regimes in a population based assay. Our data shows that the three incoherent feed-forward networks, using three different macromolecular inhibitory elements, reproduce the behavior predicted from our computational model. We also demonstrate that specific biological motifs can be designed to generate similar behavior using different components. In addition we show how it is possible to tune the behavior of the networks in a predicable manner by applying suitable mutations to the inhibitory elements.

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemodynamic variables. After the model bank was determined, mean arterial pressure was held within +/- 5 mm Hg 88.9% of the time with a standard deviation of 3.9 mm Hg. The cardiac output was held within +/- 1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mm Hg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min.

Electrical impedance spectroscopy (EIS) is an electrokinetic method that allows for the characterization of intrinsic dielectric properties of cells. EIS has emerged in the last decade as a promising method for the characterization of cancerous cells, providing information on inductance, capacitance, and impedance of cells. The individual cell behavior can be quantified using its characteristic phase angle, amplitude, and frequency measurements obtained by fitting the input frequency-dependent cellular response to a resistor–capacitor circuit model. These electrical properties will provide important information about unique biomarkers related to the behavior of these cancerous cells, especially monitoring their chemoresistivity and sensitivity to chemotherapeutics. There are currently few methods to assess drug resistant cancer cells, and therefore it is difficult to identify and eliminate drug-resistant cancer cells found in static and metastatic tumors. Establishing techniques for the real-time monitoring of changes in cancer cell phenotypes is, therefore, important for understanding cancer cell dynamics and their plastic properties. EIS can be used to monitor these changes. In this review, we will cover the theory behind EIS, other impedance techniques, and how EIS can be used to monitor cell behavior and phenotype changes within cancerous cells.

The impact of mixing on the promotion of microorganism growth rate has been analyzed using a multiphase forced-circulation pipe-loop reactor model capable of identifying conditions under which it is possible to convert natural gas into Single-Cell Protein. The impact of mixing in the interphase mass transfer was found to exert a critical role in determining the overall productivity of the bioreactor, particularly at the high cell loadings needed to reduce the capital costs associated with the large-scale production needed for the production of relatively low-value SCP in a sustainable manner.