EMILiO: A fast algorithm for genome-scale strain design

Yang, Laurence T.; Cluett, William R.; Mahadevan, Radhakrishnan

doi:10.1016/j.ymben.2011.03.002

Cited by 112 publications

(87 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the scalability problems of traditional MIP formulations in mind (94), Mahadevan and coworkers proposed the EMILIO (enhancing metabolism with iterative linear optimization) approach (111). The foundational framework employed by EMILIO is very similar to that of OptReg, with the exception that the inner problem's objective function enforces the maximization of a minimal production rate, thus addressing the concerns first raised by Tepper and Shlomi (93).…”

Section: A Taxonomy For Computational Strain Optimization Methodsmentioning

confidence: 99%

“…To do this, the complexity of the mathematical formulations is usually incremented in such a way that, for larger models or for a higher number of tested perturbations, they usually do not scale well. Some authors addressed these scalability problems by suggesting ways to simplify the problems in MIP-based methods (95,111) or by computing subsets of the solution space in EMA-based approaches (145). The recent developments from Klamt and coworkers (145) hold the promise to scale up EMA-based strain optimization and boost their utility in real-world applications.…”

Section: A Taxonomy For Computational Strain Optimization Methodsmentioning

confidence: 99%

See 1 more Smart Citation

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Maia

Rocha

2016

Microbiol Mol Biol Rev

116

107

View full text Add to dashboard Cite

SUMMARYShifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together within silicostrain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the mainin silicoconstraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

show abstract

Section: A Taxonomy For Computational Strain Optimization Methodsmentioning

confidence: 99%

Section: A Taxonomy For Computational Strain Optimization Methodsmentioning

confidence: 99%

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Maia

Rocha

2016

Microbiol Mol Biol Rev

116

107

View full text Add to dashboard Cite

show abstract

“…Interestingly, a simple sensitivity analysis shows (Table 5b) that the maximal theoretical carbon yield can only be reached under aerobic conditions. E. coli mutants, which produce succinate under aerobic conditions, and a theoretically designed high-performance aerobic strain have been reported [44,45]. The sensitivity analysis can also guide the development of an anaerobic cultivation process.…”

Section: Estimation Of the Culture Medium Compositionmentioning

confidence: 99%

Computer-Guided Metabolic Engineering

Valderrama-Gómez

Wagner

Kremling

2015

Springer Protocols Handbooks

View full text Add to dashboard Cite

Computational methods and tools are nowadays widely applied for rational Metabolic Engineering approaches. However, what is still missing are clear advices on the right order of the application of these tools. The availability of genomic information for a large number of cellular systems especially requires the use of computers to store, analyze, and process knowledge of single enzymes, metabolic pathways, and cellular networks. The trend of integrating measured quantities for the metabolome, the transcriptome, and the proteome into mathematical models, combined with methods for the rational design of cellular networks, has led to the research field Systems Metabolic Engineering, a field that extends and amplifies the classical field of Metabolic Engineering. This chapter describes mathematical and computational approaches on the cellular and the process levels. In the Material section, modeling approaches and methods for model analysis are introduced, and the current state of the art is reviewed. In the Method section, we propose a protocol for efficiently combining various approaches for the optimal production of desired biotechnological products.

show abstract

“…Example specific to the field of metabolic engineering are: genetic design through local search (GDLS) (Lun et al 2009) in which the MILP is iteratively solved in small region of the design space; enhancing metabolism with iterative linear optimization (EMILiO) (Yang et al 2011), that use a successive linear programming approach in order to solve efficiently a MILP obtained through the Karush-Kuhn-Tucker method. A recent survey of the state-of-the-art is given in Long et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of genome-scale metabolic models: the case of ethanol production

et al. 2018

View full text Add to dashboard Cite

Ethanol is among the largest fermentation product used worldwide, accounting for more than 90% of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to 19.74 mmol gDW −1 h −1 (+ 832.88% with respect to wild-type) and biomass production of 0.02 h −1 (− 98.06%). The analyses on E. coli highlighted a single knockout strategy producing 16.49 mmol gDW −1 h −1 (+ 679.29%) ethanol, with biomass equals to 0.23 h −1 (− 77.45%). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica.We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is + 195.24%.

show abstract

EMILiO: A fast algorithm for genome-scale strain design

Cited by 112 publications

References 48 publications

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Computer-Guided Metabolic Engineering

Multi-objective optimization of genome-scale metabolic models: the case of ethanol production

Contact Info

Product

Resources

About