skip to main content
10.1145/2463209.2488787acmconferencesArticle/Chapter ViewAbstractPublication PagesdacConference Proceedingsconference-collections
research-article

Pareto epsilon-dominance and identifiable solutions for BioCAD modeling

Published: 29 May 2013 Publication History

Abstract

We propose a framework to design metabolic pathways in which many objectives are optimized simultaneously. This allows to characterize the energy signature in models of algal and mitochondrial metabolism. The optimal design and assessment of the model is achieved through a multi-objective optimization technique driven by epsilon-dominance and identifiability analysis. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the Pareto front. Our framework is also suitable for black-box analysis, enabling to investigate and optimize any biological pathway modeled with ODEs, DAEs, FBA and GPR.

References

[1]
U. Sengupta, S. Ukil, N. Dimitrova, and S. Agrawal. Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications. PloS one, 4(12):e8100, 2009.
[2]
J. N. Bazil, G. T. Buzzard, and A. E. Rundell. Modeling mitochondrial bioenergetics with integrated volume dynamics. PLoS computational biology, 6(1):e1000632, 2010.
[3]
A. C. Smith and A. J. Robinson. A metabolic model of the mitochondrion and its use in modelling diseases of the tricarboxylic acid cycle. BMC systems biology, 5(1):102, 2011.
[4]
J. D. Orth, I. Thiele, and B. O. Palsson. What is flux balance analysis? Nature Biotechnology, 28(3):245--248, 2010.
[5]
R. L. Chang, L. Ghamsari, A. Manichaikul, E. F. Y. Hom, S. Balaji, W. Fu, Y. Shen, T. Hao, B. O. Palsson, and K. Salehi-Ashtiani. Metabolic network reconstruction of chlamydomonas offers insight into light-driven algal metabolism. Molecular systems biology, 7(1):518, 2011.
[6]
O. Shoval, H. Sheftel, G. Shinar, Y. Hart, O. Ramote, A. Mayo, E. Dekel, K. Kavanagh, and U. Alon. Evolutionary trade-offs, pareto optimality, and the geometry of phenotype space. Science, 2012.
[7]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002.
[8]
R. Umeton, G. Stracquadanio, A. Sorathiya, P. Liò, A. Papini, and G. Nicosia. Design of robust metabolic pathways. In Proceedings of the 48th Design Automation Conference, pages 747--752. ACM, 2011.
[9]
M. Laumanns, L. Thiele, K. Deb, and E. Zitzler. Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput., 10(3):263--282, September 2002.
[10]
L. Breiman and J. H. Friedman. Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association, 80(391):580--598, 1985.
[11]
S. Hengl, C. Kreutz, J. Timmer, and T. Maiwald. Data-based identifiability analysis of non-linear dynamical models. Bioinformatics, 23(19):2612--2618, 2007.
[12]
J. Costanza, G. Carapezza, C. Angione, P. Lió, and G. Nicosia. Robust design of microbial strains. Bioinformatics, 28(23):3097--3104, 2012.
[13]
M. E. Dumas. Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes. Molecular BioSystems, 2012.
[14]
C. Angione, G. Carapezza, J. Costanza, P. Lió, and G. Nicosia. Rational design of organelle compartments in cells. EMBnet. journal, 18(B):p. 20, 2012.
[15]
M. D. Morris. Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2):161--174, 1991.

Cited By

View all
  • (2019)A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on $\epsilon$ -DominanceIEEE Access10.1109/ACCESS.2019.28969627(18267-18283)Online publication date: 2019
  • (2016)Metabolic Circuit Design Automation by Multi-objective BioCADMachine Learning, Optimization, and Big Data10.1007/978-3-319-51469-7_3(30-44)Online publication date: 25-Dec-2016
  • (2015)Pareto Optimal Design for Synthetic BiologyIEEE Transactions on Biomedical Circuits and Systems10.1109/TBCAS.2015.24672149:4(555-571)Online publication date: Aug-2015

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DAC '13: Proceedings of the 50th Annual Design Automation Conference
May 2013
1285 pages
ISBN:9781450320719
DOI:10.1145/2463209
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2013

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Conference

DAC '13
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

Upcoming Conference

DAC '25
62nd ACM/IEEE Design Automation Conference
June 22 - 26, 2025
San Francisco , CA , USA

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 08 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2019)A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on $\epsilon$ -DominanceIEEE Access10.1109/ACCESS.2019.28969627(18267-18283)Online publication date: 2019
  • (2016)Metabolic Circuit Design Automation by Multi-objective BioCADMachine Learning, Optimization, and Big Data10.1007/978-3-319-51469-7_3(30-44)Online publication date: 25-Dec-2016
  • (2015)Pareto Optimal Design for Synthetic BiologyIEEE Transactions on Biomedical Circuits and Systems10.1109/TBCAS.2015.24672149:4(555-571)Online publication date: Aug-2015

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media