Carbon Metabolism, from “in silico” Optimization Towards Leaf Re-engineering

In collaboration with:
Department of Mathematics and Computer Science, University of Catania, Catania, CT, 95125, Italy.
Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Department of Plant Biology, University of Florence, Florence, FI, 50121, Italy.
The Whitaker Biomedical Engineering Institute, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
Computer Laboratory, University of Cambridge, Cambridge, UK.

Aims of this research are: (i) optimization of the rate of photosynthesis using a constant amount of protein nitrogen; (ii) identification and classification of the enzymes based on their sensitivity; (iii) robust optimization of production rate under uncertainty conditions; and (iv) multi-objective optimization for the understanding of a completely re-engineered leaf.
We have studied the C3 photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the C3 photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen involvement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the 135% from 15.486 μ mol m-² s-¹ to 36.382 μ mol m-² s-¹, and improving the previous best-found photosynthetic productivity value (27.261 μ mol m-² s-¹, 76% of improvement). Enzyme concentrations have a maximal local robustness (100%) and a high global robustness (97.2%), good properties for a possible “in vitro” manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we obtained the trade-off between the maximization of the leaf CO2 uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three Ci concentrations referring to the estimate of CO2 concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.

Papers:

1. [2012, inproceedings]
   R. Umeton, G. Stracquadanio, A. Papini, J. Costanza, P. Liò, and G. Nicosia, "Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism," in Advances in Systems Biology, 2012, pp. 441-459.
   @INPROCEEDINGS {umeton2012AdvExpMedBiol,
  AUTHOR = {Umeton, Renato and Stracquadanio, Giovanni and Papini, Alessio and Costanza, Jole and Li{\`o},
  Pietro and Nicosia, Giuseppe},
  affiliation = {Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA},
  title = {Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism},
  booktitle = {Advances in Systems Biology},
  series = {Advances in Experimental Medicine and Biology},
  editor = {Goryanin, Igor I. and Goryachev, Andrew B.},
  publisher = {Springer New York},
  isbn = {978-1-4419-7210-1},
  keyword = {Biomedical and Life Sciences},
  pages = {441-459},
  volume = {736},
  url = {http://dx.doi.org/10.1007/978-1-4419-7210-1_26},
  note = {10.1007/978-1-4419-7210-1_26},
  abstract = {Understanding and optimizing the CO 2 fixation process would allow human beings to address better current energy and biotechnology issues. We focused on modeling the C 3 photosynthetic Carbon metabolism pathway with the aim of identifying the minimal set of enzymes whose biotechnological alteration could allow a functional re-engineering of the pathway. To achieve this result we merged in a single powerful pipe-line Sensitivity Analysis (SA), Single- (SO) and Multi-Objective Optimization (MO), and Robustness Analysis (RA). By using our recently developed multipurpose optimization algorithms (PAO and PMO2) here we extend our work exploring a large combinatorial solution space and most importantly, here we present an important reduction of the problem search space. From the initial number of 23 enzymes we have identified 11 enzymes whose targeting in the C 3 photosynthetic Carbon metabolism would provide about 90% of the overall functional optimization. Both in terms of maximal CO 2 Uptake and minimal Nitrogen consumption, these 11 sensitive enzymes are confirmed to play a key role. Finally we present a RA to confirm our findings.},
  year = {2012}
}
2. [2011, inproceedings]
   R. Umeton, G. Stracquadanio, A. Sorathiya, A. Papini, P. Liò, and G. Nicosia, "Design of Robust Metabolic Pathways," in Proceedings of the 48th Design Automation Conference (DAC), San Diego, CA, USA, June 5-9, 2011, pp. 747-752.
   @INPROCEEDINGS{ umeton2011dac,
  AUTHOR = {Renato Umeton and Giovanni Stracquadanio and Anil Sorathiya and Alessio Papini and Pietro Li{\`o} and Giuseppe Nicosia},
  TITLE = {Design of Robust Metabolic Pathways},
  booktitle = {Proceedings of the 48th Design Automation Conference (DAC), San Diego, CA, USA, June 5-9},
  publisher = {ACM},
  year = {2011},
  pages = {747--752},
  isbn = {978-1-4503-0636-2},
  url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981995},
  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.}
}
3. [2010, article]
   A. Papini, G. Nicosia, G. Stracquadanio, P. Liò, and R. Umeton, "Key enzymes for the optimization of CO2 uptake rate and nitrogen concentration in the C3 photosynthetic Carbon metabolism," Journal of Biotechnology, vol. 150, iss. Supplement 1, pp. 525-526, 2010.
   @ARTICLE{ umeton2010jbiotech,
  AUTHOR = {Papini, Alessio and Nicosia, Giuseppe and Stracquadanio, Giovanni and Li\`o, Pietro and Umeton, Renato},
  TITLE = {Key enzymes for the optimization of CO2 uptake rate and nitrogen concentration in the C3 photosynthetic Carbon metabolism},
  JOURNAL = {Journal of Biotechnology},
  PUBLISHER = {Elsevier},
  YEAR = {2010},
  PAGES = {525--526},
  VOLUME = {150},
  NUMBER = {Supplement 1},
  MONTH = {November},
  URL = {http://dx.doi.org/10.1016/j.jbiotec.2010.09.846}
}
4. [2010, inproceedings]
   G. Stracquadanio, R. Umeton, A. Papini, P. Liò, and G. Nicosia, "Analysis and Optimization of C3 Photosynthetic Carbon Metabolism," in Proceedings of 10th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE), Philadelphia, PA, USA, May 31-June 3, 2010, pp. 44-51.
   @INPROCEEDINGS{ umeton2010ieee,
  AUTHOR = {Stracquadanio, Giovanni and Umeton, Renato and Papini, Alessio and Li\`o, Pietro and Nicosia, Giuseppe},
  TITLE = {Analysis and Optimization of C3 Photosynthetic Carbon Metabolism},
  BOOKTITLE = {Proceedings of 10th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE), Philadelphia, PA, USA, May 31-June 3},
  YEAR = {2010},
  PAGES = {44--51},
  EDITOR = {Rigoutsos, Isidore and Floudas, Christodoulos A.},
  PUBLISHER = {IEEE Computer Society},
  DOI = {10.1109/BIBE.2010.17},
  URL = {http://www.umeton.com/papers/Stracquadanio-BIBE10.pdf},
  ABSTRACT = {We have studied the C3 photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the C3 photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the 135% from 15.486 µmol m-2 s-1 to 36.382 µmol m-2 s-1 , and improving the previous best-found photosynthetic productivity value (27.261 µmol m-2 s-1 , 76% of enhancement). Optimized enzyme concentrations express a maximal local robustness (100%) and a high global robustness (97.2%), satisfactory properties for a possible “in vitro” manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf CO2 uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three ci concentrations referring to the estimate of CO2 concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.}
}

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