Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineeringReportar como inadecuado

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BMC Bioinformatics

, 10:S4

First Online: 15 October 2009


BackgroundMechanistic 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.

ResultsWe 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.

ConclusionWe 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.

Download fulltext PDF

Autor: Filippo Menolascina - Domenico Bellomo - Thomas Maiwald - Vitoantonio Bevilacqua - Caterina Ciminelli - Angelo Paradiso - St


Documentos relacionados