Improved usability of the minimal model of insulin sensitivity based on an automated approach and genetic algorithms for parameter estimation. Clin Sci (Lond

A B S T R A C T Minimal model analysis of glucose and insulin data from an IVGTT (intravenous glucose tolerance test) is widely used to estimate insulin sensitivity; however, the use of the model often requires intervention by a trained operator and some problems can occur in the estimation of model parameters. In the present study, a new method for minimal model analysis, termed GAMMOD, was developed based on genetic algorithms for the estimation of model parameters. Such an algorithm does not require the fixing of initial values for the parameters (that may lead to unreliable estimates). Our method also implements an automated weighting scheme not requiring manual intervention of the operator, thus improving the usability of the model. We studied a group of 170 women with a history of previous gestational diabetes. Results obtained by GAMMOD were compared with those obtained by MINMOD (a traditional gradient-based algorithm for minimal model analysis). Insulin sensitivity by GAMMOD was (3.86 + − 0.19) compared with (4.33 + − 0.20) × 10 −4 µ-units · ml −1 · min −1 by MINMOD; glucose effectiveness was 0.0236 + − 0.0005 compared with 0.0229 + − 0.0005 min −1 respectively. The difference in the estimation by the two methods was within the precision expected for such metabolic parameters and is probably of no clinical relevance. Moreover, both the coefficient of variation of the estimated parameters and the error of fit were generally lower in GAMMOD, despite the fact that it does not require manual intervention. In conclusion, the GAMMOD approach for parameter estimation in the minimal model provides a reliable estimation of the model parameters and improves the usability of the model, thus facilitating its further use and application in a clinical context

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Microscale evaluation of de novo engineered whole cell biocatalysts

Biocatalysis has emerged as a powerful tool for the synthesis of high value optically pure compounds. With advances in synthetic biology, it is now possible to design de novo non-native pathways to perform non-natural chiral bioconversions. However these systems are difficult to assemble and operate productively, severely hampering their industrial application. The purpose of this study was to develop a microscale toolbox for the rapid design and evaluation of synthetic pathways, in order to increase their operational productivities and speed-up their process development. The first aim of this work was to establish a microscale platform to accelerate the evaluation of different variants of transketolase (TK) and transaminase (TAm), in order to design and construct a de novo pathway for the one-pot synthesis of chiral amino alcohols. The second aim was to develop a microscale methodology to rapidly establish the complete kinetic models of the selected TKs and TAms, which would allow efficient operation of the one-pot synthesis. The third aim was to scale-up the production of the biocatalyst to pilot plant, while controlling and maintaining the desired level of expression of each enzyme. Finally the fourth aim of this project was to scale-up and simulate the complete one-pot syntheses to preparative scale, while predicting and applying the best reaction strategies and reactor configurations. The experimental microscale toolbox was based on 96 microwell plates with automation capacities, where the one-pot syntheses of the diastereoisomers (2S,3S)-2 aminopentane-1,3-diol (APD) and (2S,3R)-2-amino-1,3,4-butanetriol (ABT) were designed and performed with final product yields of 90% and 87% mol/mol respectively. For the synthesis of ABT and APD, the wild type E. coli TK and the engineered D469E TK were identified as the best candidates respectively, and both enzymes were paired with the TAm from Chromobacterium violaceum. A microscale methodology for kinetic model establishment was developed based on programmable non linear methods. The TAm step was found to be the bottleneck of the multi-step syntheses, due to the high a Michaelis constant of intermediate substrate erythrulose for the synthesis of ABT, and the low catalytic constants for the synthesis of APD. Also the amino donor substrate was discovered to be toxic for the TAm, as well as causing side reactions, thus affecting the overall performance of the de novo pathway. The production of the E. coli whole cells containing the de novo pathway were successfully scaled-up to pilot plant without losing catalytic activity. By manipulating the fermentation temperature and induction time of TAm, it was found the desired level of expression of each enzyme could be achieved. Finally, the complete one-pot syntheses were simulated using the previously established microscale kinetic models, which were found to be predictive of preparative scale bioconversions. A reactor with fed-batch addition of the amino donor was predicted as the best operating strategy in each case. Using this strategy, the one-pot syntheses allowed up to a 6-fold increase in product yield (% mol/mol), while using concentrations one order of magnitude higher than previously published preparative scale data. As a conclusion, this work is the first of its kind to develop such a microscale modelling toolbox, which is designed to exploit the synthetic potential of engineered and recombinant enzymes, in order to design, simulate and optimize de novo engineered pathways. This makes the results of this work an original contribution for the process development of synthetic pathways