Mathematical Modeling of a Bioluminescent E. Coli Based Biosensor

In this work we present a mathematical model for the bioreporter activity of an E. coli based bioluminescent bioreporter. This bioreporter is based on a genetically modified E. coli which harbors the recA promoter, a member of the bacterial SOS response, fused to the bacterial luminescence (lux) genes. This bioreporter responds to the presence of DNA damaging agents such as heavy metals, H2O2 and Nalidixic Acid (NA) that activate the SOS response. In our mathematical model we implemented basic physiological mechanisms such as: the penetration of the NA into the biosensor; gyrase enzyme inhibition by the NA; gyrase level regulation; creation of chromosomal DNA damage; DNA repair and release of ssDNA into the cytoplasm; SOS induction and chromosomal DNA repair; activation of lux genes by the fused recA promoter carried on a plasmidal DNA; transcription and translation of the luminescence responsible enzymes; luminescence cycle; energy molecules level regulation and the regulation of the O2 consumption. The mathematical model was defined using a set of ordinary differential equations (ODE) and solved numerically. We simulated the system for different concentrations of NA in water for specific biosensors concentration, and under limited O2 conditions. The simulated results were compared to experimental data and satisfactory matching was obtained. This manuscript presents a proof of concept showing that real biosensors can be modeled and simulated. This sets the ground to the next stage of implementing a comprehensive physiological model using experimentally extracted parameters. Following the completion of the next stage, it will be possible to construct a “Computer Aided Design” tool for the simulation of the genetically engineered biosensors. We define a term “bioCAD” for a Biological System Computer Aided Design. The specific bioCAD that is described here is aimed towards whole cell biosensors which are under investigation today for functional sensing. Usage of the bioCAD will improve the biosensors design process and boost their performance. It will also reduce Non Recurring Engineering (NRE) cost and time. Finally, using a parameterized solution will allow fair and quick evaluation of whole cell biosensors for various applications

Successful Weight Loss Surgery Improves Eating Control and Energy Metabolism: A Review of the Evidence

Eating behavior is determined by a balance of memories in terms of reward and punishment to satisfy the urge to consume food. Refilling empty energy stores and hedonistic motivation are rewarding aspects of eating. Overfeeding, associated adverse GI effects, and obesity implicate punishment. In the current review, evidence is given for the hypothesis that bariatric surgery affects control over eating behavior.Moreover, any caloric overload will reduce the feeling of satiety. Durable weight loss after bariatric surgery is probably the result of a new equilibrium between reward and punishment, together with a better signaling of satiation due to beneficial metabolic changes.We propose to introduce three main treatment goals for bariatric surgery: 1) acceptable weight loss, 2) improvement of eating control, and 3) metabolic benefit. To achieve this goal, loss of 50% to 70% of excess weight will be appropriate (i.e. 30% to 40% loss of initial weight), depending on the degree of obesity prior to operation

The Relationship Between Worry and Dimensions of Anxiety Symptoms in Children and Adolescents.

BACKGROUND: Worry is a common feature across many anxiety disorders. It is important to understand how and when worry presents from childhood to adolescence to prevent long-term negative outcomes. However, most of the existing studies that examine the relationship between worry and anxiety disorders utilize adult samples. AIMS: The present study aimed to assess the level of worry in children and adolescents and how relationships between worry and symptoms of separation anxiety disorder (SAD) and social anxiety disorder (Soc) may present differently at different ages. METHOD: 127 children (age 8-12 years) and adolescents (age 13-18 years), diagnosed with any anxiety disorder, presenting at a child anxiety out-patient clinic, completed measures of worry, anxiety and depression. RESULTS: Worry scores did not differ by age group. Soc symptoms were significantly correlated with worry in both age groups; however, SAD symptoms were only significantly correlated with worry in younger participants. After the inclusion of covariates, SAD symptoms but not Soc symptoms remained significant in the regression model with younger children, and Soc symptoms remained significant in the regression model with older children. CONCLUSIONS: The finding that worry was comparable in both groups lends support for worry as a stable construct associated with anxiety disorders throughout late childhood and early adolescence