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

Filippo Menolascina, Domenico Bellomo, Thomas Maiwald, Vitoantonio Bevilacqua, Caterina Ciminelli, Angelo Paradiso, Stefania Tommasi

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Background: 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. Results: 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. Conclusion: 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.

Original languageEnglish
Article number1471
JournalBMC Bioinformatics
Volume10
Issue numberSUPPL. 12
Publication statusPublished - Oct 15 2009

Fingerprint

Lab-On-A-Chip Devices
Systems Biology
Microfluidics
Cancer
Engineering
Identification (control systems)
Information Systems
Uncertainty
Experiment
Pathway
Neoplasms
Cell
Availability
Experiments
Fisher information matrix
Optimal Experimental Design
Systems Theory
Reaction Network
Oncology
Fisher Information Matrix

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering. / Menolascina, Filippo; Bellomo, Domenico; Maiwald, Thomas; Bevilacqua, Vitoantonio; Ciminelli, Caterina; Paradiso, Angelo; Tommasi, Stefania.

In: BMC Bioinformatics, Vol. 10, No. SUPPL. 12, 1471, 15.10.2009.

Research output: Contribution to journalArticle

Menolascina F, Bellomo D, Maiwald T, Bevilacqua V, Ciminelli C, Paradiso A et al. Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering. BMC Bioinformatics. 2009 Oct 15;10(SUPPL. 12). 1471.
Menolascina, Filippo ; Bellomo, Domenico ; Maiwald, Thomas ; Bevilacqua, Vitoantonio ; Ciminelli, Caterina ; Paradiso, Angelo ; Tommasi, Stefania. / Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering. In: BMC Bioinformatics. 2009 ; Vol. 10, No. SUPPL. 12.
@article{9a69d91391da492bb007d64aecca6dd0,
title = "Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering",
abstract = "Background: 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. Results: 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. Conclusion: 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.",
author = "Filippo Menolascina and Domenico Bellomo and Thomas Maiwald and Vitoantonio Bevilacqua and Caterina Ciminelli and Angelo Paradiso and Stefania Tommasi",
year = "2009",
month = "10",
day = "15",
language = "English",
volume = "10",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central Ltd.",
number = "SUPPL. 12",

}

TY - JOUR

T1 - Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering

AU - Menolascina, Filippo

AU - Bellomo, Domenico

AU - Maiwald, Thomas

AU - Bevilacqua, Vitoantonio

AU - Ciminelli, Caterina

AU - Paradiso, Angelo

AU - Tommasi, Stefania

PY - 2009/10/15

Y1 - 2009/10/15

N2 - Background: 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. Results: 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. Conclusion: 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.

AB - Background: 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. Results: 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. Conclusion: 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.

UR - http://www.scopus.com/inward/record.url?scp=70449515181&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449515181&partnerID=8YFLogxK

M3 - Article

C2 - 19828080

AN - SCOPUS:70449515181

VL - 10

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - SUPPL. 12

M1 - 1471

ER -