One of the most common applications of metabolic circuits is to produce a desired chemical in a chassis organism, such as the Escherichia coli (E. coli), by importing heterologous genes encoding for the enzymes that participate in the biosynthetic pathway. Recently, an automated pipeline named RetroPath was developed to synthesise embedded metabolic circuits [1]. These circuits are to be embedded in E. coli for a wide range of applications such as regulating biomass productions, sensing specifc molecules, processing specific molecules, and releasing specific molecules. In this paper, we improve the efficiency of RetroPath via quadratic programming

Carbonell, Pablo

El Menjra, Lina

Faulon, Jean-Loup

Kulkarni, Vishwesh V.

Warwick Research Archives Portal Repository

     warwick.ac.uk/lib-publications      Original citation: Kulkarni, Vishwesh V., El Menjra, Lina, Carbonell, Pablo and Faulon, Jean-Loup (2016) Integrated predictive genome-scale models to improve the metabolic re-engineering efficiency. In: 8th International Workshop on Bio-Design Automation, Newcastle-upon-Tyne, UK, 16-18 Aug 2016.  Permanent WRAP URL: http://wrap.warwick.ac.uk/84219   Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.  Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.   A note on versions: The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher’s version. Please see the ‘permanent WRAP URL’ above for details on accessing the published version and note that access may require a subscription.  For more information, please contact the WRAP Team at: wrap@warwick.ac.uk  Integrated Predictive Genome-Scale Models to Improve theMetabolic Re-Engineering Efficiency[Extended Abstract]Vishwesh V. KulkarniUniversity of WarwickSchool of EngineeringCoventry, CV4 7ALV.Kulkarni@warwick.ac.ukLina El MenjraUniversity of WarwickSchool of EngineeringCoventry, CV4 7ALL.el-Menjra@warwick.ac.ukPablo CarbonellUniversity of ManchesterSYNBIOCHEMManchester, M1 7DNpablo.carbonell@manchester.ac.ukJean-Loup FaulonUniversity of ManchesterSYNBIOCHEMManchester, M1 7DNjean-loup.faulon@manchester.ac.ukIntroductionOne of the most common applications of metabolic circuitsis to produce a desired chemical in a chassis organism, suchas the Escherichia coli (E. coli), by importing heterologousgenes encoding for the enzymes that participate in the biosyn-thetic pathway. Recently, an automated pipeline namedRetroPath was developed to synthesise embedded metaboliccircuits [1]. These circuits are to be embedded in E. colifor a wide range of applications such as regulating biomassproductions, sensing specific molecules, processing specificmolecules, and releasing specific molecules. In RetroPath,the available circuit design space, constrained by the set ofdesign specifications, is searched and a set of optimal circuitsis obtained. In this process, the basic steps are as follows:1. Step 1: Define the input set of metabolites S, the out-put (target metabolite set) T, and the metabolic space,i.e., the set of all possible metabolites and chemical re-actions that can be generated in vivo, M;2. Step 2: Define the specifications of the desired circuit;3. Step 3: Compute the set of enzymes involved in atleast one minimal pathway that converts S into T;4. Step 4: Enumerate all metabolic pathways convertingS into T (see [2]); and5. Step 5: Solve a nonlinear optimization problem whereinthe objective function comprises the expected reactionPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.IWBDA 2016 Newcastle-upon-Tyrne, August 2016Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$15.00.efficiencies, inhibition effects, and perturbation effects.Here, the flux balance analysis (FBA) is used.The FBA (see [4]–[6]) predicts metabolic flux distributionsat a steady state by solving the linear programming problemmaximize wT v subject to Sv = 0 and α ≤ v ≤ β,where wT v denotes the biomass, v denotes the vector ofmetabolic fluxes, S is the stoichiometric matrix, and (α, β)are the a priori known bounds on the fluxes. Thus, it isinherently assumed in RetroPath that the chassis organismhas reached a steady state following the insertion of the het-erologous gene. As shown in [4], such an assumption of opti-mality may not be valid for genetically engineered knockoutsand bacterial strains that were not exposed to long-termevolutionary pressure. Following [4], we show that Step 5can indeed be executed more efficiently by assuming thatthe metabolic fluxes undergo a minimal redistribution withrespect to the flux configuration of the wild type. This min-imization of metabolic adjustment (MOMA) is computed bysolving the quadratic programming problemminimize12‖v‖2 + vT v∗ s. t. Sv = 0 and α ≤ v ≤ β,where v∗ denotes the flux distribution predicted by the FBA.We next show that the efficiency of RetroPath can be im-proved if transcriptomic data is available. This is achievedby replacing the use of FBA in Step 5 first with the PROMalgorithm derived in [3] and then with an extension PROM-E derived by us. PROM refines the upper bounds and thelower bounds in the metabolic flux constraints of the FBAby using Bayesian estimation on the available transcriptomicand metabolomic datasets. Here, the probabilities on thegene-TF interactions are empirically determined using avail-able datasets; the more the number of datasets, the better isthe expected performance. Effectively, PROM implementsa slight modification of the FBA using linear programmingand achieves an improvement in not only the capacity togenerate larger genome-scale models but also in the accu-9 3Comparison of Growth Rate PredictionsPredicted Growth Rate ([3])Predicted Growth Rate (PROM Local)Predicted Growth Rate (PROM-E)Measured Growth Rate ([9])Figure 1: Our proposed algorithm PROM-E predicts the growth rates better than PROM [3] and RFBA[8] on the datasets of [8] across a variety of anaerobic conditions. For aerobic conditions, the PROM-E andPROM perform equally well and slightly better than RFBA.racy of predicting the flux states. We then show how furtherimprovement are obtained using our extension PROM-E.ResultsWe now illustrate how our extension PROM-E of PROMestimates the flux rates more accurately than PROM on thedatasets of [8] wherein growth phenotypes from A System-atic Annotation Package (ASAP) are predicted for commu-nity analysis of genomes database. The ASAP database hasgrowth phenotypes of several E. coli gene KOs under vari-ous conditions. In [3], 15 TFs for which growth phenotypesunder different 125 conditions are considered and it shownthat PROM predicts the growth phenotypes more accuratelythan RFBA [8]. In [3], six strains with KOs of key transcrip-tional regulators in the oxygen response (∆arcA, ∆appY,∆fnr, ∆oxyR, ∆soxS, and the double KO ∆arcA∆fnr) wereconstructed and then the growth rates were measured in aer-obic and anaerobic glucose minimal medium conditions. AsFig. 1 shows, our proposed PROM-E algorithm predicts thegrowth rate more accurately than PROM in anaerobic con-ditions and equally well in aerobic conditions. Furthermore,the standard deviation of the prediction error is significantlylower in PROM-E compared to PROM. We obtained theseresults in MATLAB R2015b interfaced with COBRA Tool-box 2.0 [7] and Gurobi Optimizer 6.5. As the cost of col-lecting omics datasets is reducing at Moore’s law, we expectthat our approach will soon be useful in practical contexts.AcknowledgmentsResearch supported by BBSRC under grant BB/M017702/1,Centre for Synthetic Biology of Fine and Speciality Chemi-cals (SYNBIOCHEM).1. REFERENCES[1] Carbonell, P., Parutto, P., Baudier, C., Junot, C. andFaulon, J-L.: ‘Retropath: Automated Pipeline forEmbedded Metabolic Circuits’, ACS Synthetic Biology,vol. 3, pp. 565–577, 2014.[2] Carbonell, P., Fichera, D., Pandit, S. and Faulon, J-L.:‘Enumerating Metabolic Pathways for the Production ofHeterologous Target Chemicals in Chassis Organisms’,BMC Systems Biology, 6(10), pp. 1–18, 2012.[3] Chandrasekaran, S. and Price, N.: ‘ProbabilisticIntegrative Modeling of Genome-Scale Metabolic andRegulatory Networks in Escherichia Coli andMycobacterium Tuberculosis’, PNAS, 107 (41), pp.17845–50, 2010.[4] Segre`, D., Vitkup, D. and Church, G.: ‘Analysis ofOptimality in Natural and Perturbed MetabolicNetworks’, PNAS, 99(23), pp. 15112–17, 2002.[5] Papoutsakis, E.: ‘Equations and Calculations forFermentations of Butyric Acid Bacteria’, Biotechnologyand Bioengineering, 26(2), pp. 174–187, 1984.[6] Watson, M.: ‘Metabolic Maps for the Apple II’,Biochemical Society Transactions, 12(6), pp. 1093–1094,1984.[7] Schellenberger, J., et al.: ‘Quantitative Prediction ofCellular Metabolism with Constraint-Based Models: TheCOBRA Toolbox v2.0’, Nature Protocols, 6(9), pp.1290–1307, 2011.[8] Covert, M., Knight, E., Reed, J., Herrgard, M. andPalsson, B.: ‘Integrating High-Throughput andComputational Data Elucidates Bacterial Networks’,Nature, 429(6987), pp. 92–96, May 2004.

Integrated predictive genome-scale models to improve the metabolic re-engineering efficiency

http://wrap.warwick.ac.uk/84219/7/WRAP_bioretrosynthprome-IWBDA2016abstract.pdf

Integrated predictive genome-scale models to improve the metabolic re-engineering efficiency

Abstract

Similar works

Full text

Available Versions

Warwick Research Archives Portal Repository