Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering

<p>Abstract</p> <p>Background</p> <p>Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.</p> <p>Results</p> <p>We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification.</p> <p>Conclusion</p> <p>We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich-data poor' paradox in Systems Biology.</p

Kinetic Study on Homogeneous Alkene Hydrogenation by Model Discrimination

Model discrimination is one of the methods of choice to obtain a valid kinetic description of a catalytic reaction with minimal experimental effort. It allows fast judgement of catalyst behavior and its suitability for process development. Using dynamic experiments and modeling, the kinetics of a homogeneous hydrogenation with cationic rhodium-PyrPhos {[Rh(PyrPhos)(COD)]BF4) were investigated. A set of three batch experiments allowed the discrimination between 6 models. Qualitative and quantitative descriptions of the kinetic behavior could be derived. Most importantly, evidence for catalyst deactivation was gained

Application of model discriminating experimental design for modeling and development of a fermentative fed-batch L-valine production process

A model discriminating experimental design approach for fed-batch processes has been developed and applied to the fermentative production of L-valine by a genetically modified Corynebacterium glutamicum strain possessing multiple auxotrophies as an example. Being faced with the typical situation of uncertain model information based on preliminary experiments, model discriminating design was successfully applied to improve discrimination between five competing models. Within the same modeling and experimental design framework, also the planning of an optimized production process with respect to the total volumetric productivity is shown. Simulation results were experimentally affirmed, yielding an increased total volumetric productivity of 6.2 mM L-valine per hour. However, also so far unknown metabolic mechanisms were observed in the optimized process, underlining the importance of process optimization during modeling to avoid problems of extreme extrapolation of model predictions during the final process optimization