The conceptualisation of health and disease in veterinary medicine

<p>Abstract</p> <p>Background</p> <p>The concept of health, as well as the concept of disease, is central in veterinary medicine. However, the definitions "health" and "disease" are not generally acknowledged by veterinarians. The aim of this study was to examine how the concepts "health" and "disease" are defined in veterinary textbooks.</p> <p>Methods</p> <p>Veterinary textbooks in several disciplines were investigated, but only textbooks with explicit definitions of the concepts were selected for examination.</p> <p>Results</p> <p>Eighty out of the 500 relevant books within veterinary medicine were written for non-veterinarians. Eight percent of the books had an explicit definition of health and/or disease. More frequently, textbooks written for non veterinarians did have definitions of health or disease, compared to textbooks written for professionals. A division of health definitions in five different categories was suggested, namely:</p> <p>1. Health as normality, 2. Health as biological function, 3. Health as homeostasis, 4. Health as physical and psychological well-being and 5. Health as productivity including reproduction.</p> <p>Conclusion</p> <p>Few veterinary textbooks had any health or disease definition at all. Furthermore, explicit definitions of health stated by the authors seemed to have little impact on how health and disease are handled within the profession. Veterinary medicine would probably gain from theoretical discussions about health and disease.</p

Adjusting Phenotypes by Noise Control

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks