research-article

Design of robust metabolic pathways

Authors:

Giovanni Stracquadanio,

Anilkumar Sorathiya,

Alessio Papini,

Giuseppe NicosiaAuthors Info & Claims

DAC '11: Proceedings of the 48th Design Automation Conference

Pages 747 - 752

https://doi.org/10.1145/2024724.2024892

Published: 05 June 2011 Publication History

Abstract

In this paper we investigate plant photosynthesis and microbial fuel cells. We report the following: 1) we introduce and validate a novel multi-objective optimization algorithm, PMO2; 2) in photosynthesis we increase the yield of 135%, while in Geobacter sulfurreducens we determine the tradeoff for growth versus redox properties; 3) finally, we discuss Pareto-Front as an estimator of robust metabolic pathways.

References

[1]

S. A. Becker, A. M. Feist, M. L. Mo, G. Hannum, B. O. Palsson, and M. J. Herrgard. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat. Protocols, 2(3):727--738, Mar. 2007.

[2]

A. P. Burgard, P. Pharkya, and C. D. Maranas. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84(6):647--657, Dec. 2003.

[3]

V. Cutello, G. Narzisi, and G. Nicosia. A multi-objective evolutionary approach to the protein structure prediction problem. J. of the Royal Society Interface, 3(6):139--151, 2006.

[4]

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Comp., 6(2):182--197, 2002.

Digital Library

[5]

C. H. Foyer, A. J. Bloom, G. Queval, and G. Noctor. Photorespiratory metabolism: genes, mutants, energetics, and redox signaling. Ann Rev of Plant Biol., 60:455--484, 2009.

[6]

D. R. Geiger and J. C. S. JC. Diurnal regulation of photosynthetic carbon metabolism in c3 plants. Annu Rev Plant Phys Plant Mol Biol, 45:235--256, 1994.

[7]

B. Hirel, J. L. Gouis, B. Ney, and A. Gallais. The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. J. Exp. Bot., 58(9):2369--2387, 2007.

[8]

M. Jose, Y. Hu, R. Majumdar, and L. He. Rewiring for robustness. In Proceedings of the 47th Design Automation Conference, DAC '10, pages 469--474, New York, NY, USA, 2010. ACM.

Digital Library

[9]

Y. Liu, M. Yoshioka, K. Homma, T. Shibuya, and Y. Kanazawa. Generation of yield-embedded pareto-front for simultaneous optimization of yield and performances. In Proceedings of the 47th Design Automation Conference, DAC '10, pages 909--912, New York, NY, USA, 2010. ACM.

Digital Library

[10]

R. Mahadevan, D. R. Bond, J. E. Butler, A. Esteve-Nuñez, M. V. Coppi, B. O. Palsson, C. H. Schilling, and D. R. Lovley. Characterization of metabolism in the Fe(III)-Reducing organism geobacter sulfurreducens by Constraint-Based modeling. Applied and Environmental Microbiology, 72(2):1558--1568, Feb. 2006.

[11]

T. McConaghy, P. Palmers, G. Gielen, and M. Steyaert. Simultaneous multi-topology multi-objective sizing across thousands of analog circuit topologies. In Proceedings of the 44th annual Design Automation Conference, DAC '07, pages 944--947, New York, NY, USA, 2007. ACM.

Digital Library

[12]

G. Noctor, S. D. Veljovic-Jovanovic, S. Driscoll, L. Novitskaya, and C. H. Foyer. Drought and oxidative load in the leaves of c3 plants: a predominant role for photorespiration? Ann. Bot., 89:841--50, 2002.

[13]

C. A. Raines. The Calvin cycle revisited. Photosynthesis research, 75(1):1--10, 2003.

[14]

W. Sheng, L. Xiao, and Z. Mao. Soft error optimization of standard cell circuits based on gate sizing and multi-objective genetic algorithm. In Proceedings of the 46th Annual Design Automation Conference, DAC '09, pages 502--507, New York, NY, USA, 2009. ACM.

Digital Library

[15]

G. Stracquadanio and G. Nicosia. Computational energy-based redesign of robust proteins. Computers and Chemical Engineering, 35(3):464--473, 2011.

[16]

S. Takahashi, H. Bauwe, and M. Badger. Impairment of the photorespiratory pathway accelerates photoinhibition of photosystem II by suppression of repair but not acceleration of damage processes in Arabidopsis. Plant Physiology, 144(1):487--494, 2007.

[17]

D. Tilman, K. G. Cassman, P. A. Matson, R. Naylor, and S. Polasky. Agricultural sustainability and intensive production practices. Nature, 418:671--677, 2002.

[18]

I. E. Woodrow and J. A. Berry. Enzymic regulation of photosynthetic co2 fixation in c3 plants. Annu Rev Plant Physiol Plant Mol Biol, 39:533--594, 1988.

[19]

Q. Zhang and H. Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. on Evol. Comp., 11(6):712--731, 2007.

Digital Library

[20]

X.-G. Zhu, E. de Sturler, and S. P. Long. Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: A numerical simulation using an evolutionary algorithm. Plant Physiology, 145:513--526, 2007.

[21]

E. Zitzler, D. Brockhoff, and L. Thiele. The hypervolume indicator revisited: On the design of pareto-compliant indicators via weighted integration. Lecture Notes in Computer Science, 4403:862, 2007.

Digital Library

Cited By

Umeton RBellucci GBigi RRomano SBuscarinu MReniè RRinaldi VPizzolato Umeton RMorena ERomano CMechelli RSalvetti MRistori G(2022)Multiple sclerosis genetic and non-genetic factors interact through the transient transcriptomeScientific Reports10.1038/s41598-022-11444-w12:1Online publication date: 9-May-2022
https://doi.org/10.1038/s41598-022-11444-w
Amaradio MOjha VJansen GGulisano MCostanza JNicosia G(2022)Pareto optimal metabolic engineering for the growth‐coupled overproduction of sustainable chemicalsBiotechnology and Bioengineering10.1002/bit.28103119:7(1890-1902)Online publication date: 21-Apr-2022
https://doi.org/10.1002/bit.28103
Ojha VJansen GPatanè ALa Magna ARomano VNicosia G(2021)Design and characterization of effective solar cellsEnergy Systems10.1007/s12667-021-00451-x13:2(355-382)Online publication date: 18-May-2021
https://doi.org/10.1007/s12667-021-00451-x
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Index Terms

Design of robust metabolic pathways

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Published In

cover image ACM Conferences

DAC '11: Proceedings of the 48th Design Automation Conference

June 2011

1055 pages

ISBN:9781450306362

DOI:10.1145/2024724

General Chair:
Leon Stok
IBM Corp., Hopewell Jct., NY
,
Program Chairs:
Nikil Dutt
Univ. of California, Irvine, CA
,
Soha Hassoun
Tufts Univ., Medford, MA

Copyright © 2011 ACM.

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]

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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE Council on Electronic Design Automation (CEDA)

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Association for Computing Machinery

New York, NY, United States

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Published: 05 June 2011

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DAC '11

Sponsor:

EDAC
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DAC '11: The 48th Annual Design Automation Conference 2011

June 5 - 10, 2011

California, San Diego

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

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62nd ACM/IEEE Design Automation Conference

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Cited By

Umeton RBellucci GBigi RRomano SBuscarinu MReniè RRinaldi VPizzolato Umeton RMorena ERomano CMechelli RSalvetti MRistori G(2022)Multiple sclerosis genetic and non-genetic factors interact through the transient transcriptomeScientific Reports10.1038/s41598-022-11444-w12:1Online publication date: 9-May-2022
https://doi.org/10.1038/s41598-022-11444-w
Amaradio MOjha VJansen GGulisano MCostanza JNicosia G(2022)Pareto optimal metabolic engineering for the growth‐coupled overproduction of sustainable chemicalsBiotechnology and Bioengineering10.1002/bit.28103119:7(1890-1902)Online publication date: 21-Apr-2022
https://doi.org/10.1002/bit.28103
Ojha VJansen GPatanè ALa Magna ARomano VNicosia G(2021)Design and characterization of effective solar cellsEnergy Systems10.1007/s12667-021-00451-x13:2(355-382)Online publication date: 18-May-2021
https://doi.org/10.1007/s12667-021-00451-x
Pravin CMartino INicosia GOjha V(2021)Adversarial Robustness in Deep Learning: Attacks on Fragile NeuronsArtificial Neural Networks and Machine Learning – ICANN 202110.1007/978-3-030-86362-3_2(16-28)Online publication date: 7-Sep-2021
https://doi.org/10.1007/978-3-030-86362-3_2
Eleuteri COlla SVeroni CUmeton RMechelli RRomano SBuscarinu MFerrari FCalò GRistori GSalvetti MAgresti C(2017)A staged screening of registered drugs highlights remyelinating drug candidates for clinical trialsScientific Reports10.1038/srep457807:1Online publication date: 7-Apr-2017
https://doi.org/10.1038/srep45780
Patan ASantoro AConca PCarapezza GMagna ARomano VNicosia G(2017)Multi-objective optimization and analysis for the design space exploration of analog circuits and solar cellsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.08.01062:C(373-383)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.engappai.2016.08.010
Patane ASantoro ACarapezza GMagna ARomano VNicosia G(2015)A multi-objective clonal selection algorithm for analog circuit and solar cell design2015 International Workshop on Artificial Immune Systems (AIS)10.1109/AISW.2015.7469240(1-7)Online publication date: Jul-2015
https://doi.org/10.1109/AISW.2015.7469240
Angione CCostanza JCarapezza GLió PNicosia G(2013)Pareto epsilon-dominance and identifiable solutions for BioCAD modelingProceedings of the 50th Annual Design Automation Conference10.1145/2463209.2488787(1-9)Online publication date: 29-May-2013
https://dl.acm.org/doi/10.1145/2463209.2488787
Angione CCarapezza GCostanza JLio PNicosia G(2013)Pareto Optimality in Organelle Energy Metabolism AnalysisIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2013.9510:4(1032-1044)Online publication date: 1-Jul-2013
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Carapezza GUmeton RCostanza JAngione CStracquadanio GPapini ALió PNicosia G(2013)Efficient Behavior of Photosynthetic Organelles via Pareto Optimality, Identifiability, and Sensitivity AnalysisACS Synthetic Biology10.1021/sb300102k2:5(274-288)Online publication date: 24-Jan-2013
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