CN103440435B - The flux territory comparative approach of the specific metabolite output of predicted impact key reaction - Google Patents
The flux territory comparative approach of the specific metabolite output of predicted impact key reaction Download PDFInfo
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Abstract
The invention discloses the flux territory comparative approach of the specific metabolite output of a kind of predicted impact key reaction, it is characterized in that the method is by twice calculating, once compare, once sort and form. It is the comparison that utilizes flux territory, on improving the key reaction of specific metabolite synthetic quantity and the impact of product output being carried out to computer forecast, thereby instructs targetedly the carrying out of Bioexperiment. The present invention also discloses described method simultaneously and in minimal medium, has carried out the application in riboflavin production careless bacillus. The present invention can be applicable to any species with genome yardstick metabolism network, and any product within the scope of simulation and forecast network model computing capability, and the metabolic engineering bacteria being still not clear for gene order especially has good directive significance.
Description
Technical field
The application obtains state natural sciences fund (NO.21106095) and Tianjin Normal University introduces talent's fund projectSubsidy.
The invention belongs to bioinformatics technique field, relate to the logical of the specific metabolite output of predicted impact key reactionAmount territory comparative approach.
Background technology
At present, in international and domestic research, the genome yardstick Metabolic Network Model having built exceedes 100, existingComputer simulation method simulate by optimized method (as flux equilibrium analysis) mainly for wild type species andAnalyze. The method of analyzing comprises:
(1) Flux Distribution of the inner each reaction of species when specific product maximum production, i.e. synthetic anti-with specific productShould be object function, under given culture medium condition, calculate the amount of flux of each reaction in metabolism network;
(2) knock out prediction indispensable gene by single-gene, in the situation that other setting parameters are constant, by individual geneCorresponding reaction flux is set as 0, the value of calculating target function, and target function value is that 0 this individual gene is indispensable gene, orderScalar functions is not that 0 this individual gene is dispensable gene;
(3) robust analysis, the impact of the changes in flow rate of analyzing single reaction on object function optimal value.
(4) flux Mutability analysis, flux Mutability analysis, in given stable state space, does twice each reactionOptimize, obtain respectively maximum and minimum of a value. Measure the changeability of reaction by the interval size between minimum and maximum value.
But, in the method for a lot of application optimization algorithms, because the result of optimization algorithm itself does not have uniqueProperty, thereby according to the result of once calculating, reaction flux is analyzed and had very large particularity and one-sidedness. Secondly, existingMethod is only confined to the analysis for wild type species, wild type species do not combined with the simulation of optimizing bacterial classification, therebyBeing difficult to produce has predicting the outcome of actual directive significance to species transformation strategy.
The inventor has applied for (application number: 201210099321.2) based on genome yardstick metabolism network in 2012The metabolic engineering design Forecasting Methodology patent of model, along with constantly finding of experiment, single has only been considered in this patent applicationThe result that flux equilibrium is analyzed, and does not analyze other optimal solutions in flux territory, and not to predict the outcome intoLine ordering, thereby this patent utilizes the relatively further perfect content of a upper patent in flux territory, and to predicting the outcomeSort, thereby provide to Bioexperiment the analog result that has more directive significance.
Summary of the invention
Method provided by the invention has been introduced the mode of flux territory comparison, and by the growth conditions of wild type species and optimizationType engineering species combine, can be for specific metabolites, and obtain and improve the closely-related key reaction of its output, therebyBioexperiment and industrial experimental design are produced to actual guiding value. The method is used for Metabolic Network Model to thingPlant improved calculating and prediction, there is important directive significance for Bioexperiment and industrial production.
For achieving the above object, the invention discloses following technology contents:
A flux territory comparative approach for the specific metabolite output of predicted impact key reaction, is characterized in that the methodBy twice calculating, once compare, once sort and form, undertaken by following step:
(1) the flux territory of calculating wild type target species self-sow state:
First the method that adopts flux equilibrium to analyze, utilizes known genome yardstick Metabolic Network Model, calculates biologicalThe maximum of amount synthesis rate, when calculating, parameter setting method is as follows: according to the condition of culture of known target species and phenotype,Culture medium composition, dissolved oxygen, target product output, other accessory substance output in model are carried out to identical establishing according to experiment conditionPut; Object function is set as biomass synthetic reaction, calculates biomass synthesis rate optimal value f;
Afterwards, adopt the method for flux Mutability analysis, the network-based Metabolic Flux territory that obtains wild type species distributes, and calculatesTime parameter setting method as follows: be the maximum f obtaining in previous step by the rate setting of biomass synthetic reaction, otherPart is identical with previous step, calculates two vectorial Vw_minAnd Vw_max, wherein Vw_minRepresent each reaction flux in metabolism networkThe vector of minimum of a value (the being flux lower limit) composition of value, Vw_maxThe maximum that represents each reaction amount of flux in metabolism network is (logicalAmount the upper limit) composition vector; The region of the flux lower limit of calculating gained and flux upper limit composition is the metabolism of wild type speciesFlux territory distributes;
(2) growth conditions of simulative optimization type species:
The first, the method that adopts flux equilibrium to analyze, utilizes known genome yardstick Metabolic Network Model, calculates biologicalThe maximum of amount synthesis rate, when calculating, parameter setting method is as follows: according to the condition of culture of known target species and phenotype,Culture medium composition, dissolved oxygen in model are carried out to identical setting according to experiment condition, and to target product output and other by-productsThe output of thing is not restricted, and the flux of biomass synthetic reaction is set to the optimal value f of biomass synthetic reaction in step (1),Object function is set as the output of specific metabolite, simulates for the second time, calculates the maximum of specific product outputp；
The second, the method for employing flux Mutability analysis, the network-based Metabolic Flux territory of the type that is optimized species distributes, and calculatesTime parameter setting method as follows: the maximum p that the output of specific metabolite is set as obtaining in previous step, other conditionIdentical with previous step, calculate two vectorial Ve_minAnd Ve_max, wherein Ve_minRepresent each reaction amount of flux in metabolism networkThe vector of minimum of a value (being flux lower limit) composition, Ve_maxThe maximum that represents each reaction amount of flux in metabolism network (is fluxThe upper limit) composition vector; The metabolism that the region of calculating flux lower limit and the flux upper limit composition of gained is Optimization-type species is led toAmount territory distributes;
(3) once compare: the flux territory of twice calculating is compared, determine between Optimization-type and wild type and react fluxThe difference in territory, thus determine test-type is transformed into the required metabolic engineering carrying out of Optimization-type, and then formulate corresponding wetExperimental strategy, comparison and Forecasting Methodology are as follows:
For each reaction in metabolism network,
Wherein, vw_maxExpression wild type species are simulated the flux upper limit obtaining, vw_minRepresent that the simulation of wild type species obtainsFlux lower limit, ve_maxExpression Optimization-type species are simulated the flux upper limit obtaining, ve_minRepresent what the simulation of Optimization-type species obtainedFlux lower limit;
The above-mentioned special circumstances that predicted the outcome, as (ve_max-vw_max)/vw_maxWith (ve_max-vw_max)/vw_maxMeterWhen the outcome symbol that obtains is contrary, predict the outcome as the unknown, cannot be predicted the outcome accurately;
(4) one minor sorts: the reaction obtaining for each prediction, is set to respectively wild type flux by its metabolic fluxThe upper limit or lower limit, have like this two kinds of set-up modes, with vlRepresent the lower limit of reaction flux, vuRepresent the upper limit of reaction flux,These two kinds of set-up modes can be expressed as:
vl=vu=vw_minAnd vl=vu=vw_max
The setting of other parameters is identical with step (2), after each setting, all enters taking the output of specific product as object functionRow calculates, and computational methods are flux equilibrium analysis, and the setting of two kinds of conditions respectively obtains a result of calculation, gets two result of calculationsMaximum, be designated as q, the change amount C of specific product output after calculating this reaction flux and changing,
The C value of all pre-measured reactions is arranged according to order from big to small, be pre-measured reaction to specific productThe sequence of yield effect size, instructs the experimental strategy of metabolic engineering genetic manipulation according to this sequence.
Utilize the flux territory that wild type species and Optimization-type species are calculated to compare, to improving specific metabolism productThe key reaction of thing synthetic quantity and the impact of product output is carried out to computer forecast, thus instruct targetedly biological realThe carrying out of testing. It should be noted that: first ensure to there is higher matter as the full genome yardstick Metabolic Network Model on basisAmount. Secondly implementer need possess and utilizes the COBRA kit ability of calculating of programme, has simultaneously a large amount of analog results are led toCross the ability that computer programming compares, possess reconstruct genome yardstick Metabolic Network Model.
The present invention further discloses the flux territory comparative approach of the specific metabolite output of predicted impact key reactionIn minimal medium, carry out the application in riboflavin production preparation bacillus subtilis.
Application number: 201210099321.2 have also used the method, two methods are applied to same model, in this example, adoptBy flux territory comparison method, thereby the result of prediction is more accurate, also can find out in quantity, last patent forecast127, this has predicted 112 reactions, has got rid of some and has been worth little predicting the outcome, and in addition, this method is anti-to predictionShould carry out the sequence of influence power, thereby have and instruct better effect, method detailed is as follows:
The method is used in to the riboflavin carrying out in bacillus subtilis minimal medium produces. The model adopting isThe wild type bacillus subtilis genome yardstick Metabolic Network Model of having delivered, the data of experimental bacteria growth conditions are large from historyRich grade publishes thesis for 2009 on ME[1]The experimental data of middle Bacillussubtilis168, this bacterial classification is in minimal mediumOn cultivate, element that minimal medium comprises composition and in model the bound of flux as shown in table 1. Each flux is handed overChange the upper and lower setting Main Basis of reaction what time below:
(1)K+，Na+，Mg2+，Ca2+，Fe3+，CO2，H2O and H+ can freely enter and deviated from network, and therefore they are logicalOn quantitative response, be limited to 1000mmolgDW-1h-1, under be limited to-1000mmolgDW-1h-1;
(2) because bacillus subtilis is aerobic bacteria, therefore establish oxygen and can freely enter network, but can not leaveNetwork (generating oxygen), therefore establish oxygen flux exchange reaction under be limited to-1000mmolgDW-1h-1, be above limited to 0;
(3) carbon source, nitrogenous source, sulphur source, phosphorus source be as the limited substrate obtaining from culture medium, its absorption maximum speed rootTest factually and be set as 5mmolgDW-1h-1, be i.e. limited to-5mmolgDW-1h-1 under its flux exchange reaction, the upper limitFor 1000mmolgDW-1h-1. Wherein, according to experiment condition, the carbon source that thalline utilizes is alpha-D-glucose.
The minimum cultivation of table 1 and condition and flux setting
。
According to the method for above introducing, step is as follows:
(1) the culture medium composition and the flux of flux bound to respective components exchange reaction in model that provide according to table 1Bound arranges, and the setting of metabolite (comprising acetic acid and riboflavin) exchange reaction bound is according to Shi Shuobo etc. 2009Year publishes thesis on ME[1]In data arrange, acetic acid exchange reaction bound is 1.63mmolgDW-1h-1, riboflavin exchange reaction bound is 0. Taking biomass synthetic reaction as object function, carry out analog computation for the first time,To the maximum f=0.334gh of biomass synthetic quantity-1, then the bound of biomass synthetic reaction is set as to 0.334 simultaneouslygh-1, carry out FVA analysis, the flux territory that obtains wild mushroom distributes.
(2), in analog computation for the second time, the bound of biomass synthetic reaction is set as 0.334gh simultaneously-1, acetic acidExchange reaction bound is set as respectively default value, is limited to down-1000mmolgDW-1h-1, be above limited to 1000mmolgDW-1h-1, other restrictive conditions with calculate for the first time identically, calculate taking riboflavin exchange reaction as object function, obtainThe maximum synthetic quantity of riboflavin is 0.109mmolgDW-1h-1. Again the exchange reaction flux bound of riboflavin is established simultaneouslyBe decided to be 0.109mmolgDW-1h-1, carry out FVA analysis, the flux territory of the bacterium that is optimized distributes.
(3) the reaction flux territory calculating for twice is compared, when the flux territory model of a reaction in Optimization-typeWhile enclosing higher than wild type 1%, show that this reaction needed expresses (overexpress) by mistake and be optimized; When this reaction is being optimizedFlux territory scope in type is during lower than wild type 1%, shows that this reaction needed undertaken excellent by low expression (underexpress)Change; When the bound in this reaction flux territory in Optimization-type is 0, and one of the flux territory upper limit or lower existence be not in wild typeBe 0 o'clock, show that this reaction needed is optimized by knocking out (knockout). Result shows to have 112 reactions and need changeMake, wherein have 80 reactions that need high expressed, the reaction of 27 low expression of need, separately has the upper limit of 5 reactions due to its flux territoryWith the opposite direction of lower limit change, thus unpredictable. These react distribution in different metabolic system as shown in Figure 1. NeedThe reaction of high expressed is distributed in carbohydrate and nucleic acid metabolism more, two precursor (Ribulose-5-of this and riboflavinPhosphate and GTP) be distributed in respectively in carbohydrate and nucleic acid metabolism relevant. Wherein prs[1],purFMNHD[2]WithribABGHT[3,4]The high expressed prediction that gene pairs should react has obtained confirmation in research before.
(4), to above-mentioned 112 pre-measured reactions, in Optimization-type model, its metabolic flux is set to respectively to wild type logicalThe upper limit (the v in amount territoryw_max) or lower limit (vw_min), after each setting, all calculate taking the output of riboflavin as object function. RightIn each reaction, get the maximum that calculates result under its min and max condition, calculate this reaction flux and change rear riboflavinThe change amount C of output, arranges the C value of all pre-measured reactions according to order from big to small, be pre-measured reaction to coreThe sequence of flavine impact. In sequence riboflavin production being affected in reaction, other that comprise in the approach by the synthetic GTP of PRPPPur gene (purQLCEK) and ndk gene were predicted to be expression, and to the having the greatest impact of riboflavin production, in addition, some closeBecome crossing of glutamate or glycine reactant to express very large on the impact of riboflavin, and consume glutamate or glycine reactantLow expression very large to the output of riboflavin equally, this point has shown that glutamate and glycine are in riboflavin building-up processIn have very important effect.
It is excellent that the specific metabolite output of predicted impact disclosed by the invention key reaction flux territory comparative approach hasPoint is:
(1) by the comparison in flux territory, complete covering all possible Flux Distribution situation.
(2) predict in conjunction with the result of calculation of wild type and Optimization-type species, crossing of reaction expressed and low expression feelingsCondition is predicted, has embodied the situation of change of Optimization-type species metabolic fluxs.
(3) carry out the sequence of influence power size by calculating the reaction that prediction is obtained, thereby wet experiment and production are producedThe raw guidance with actual application value. The present invention can be applicable to any species with genome yardstick metabolism network, andAny product within the scope of simulation and forecast network model computing capability, the metabolic engineering bacteria being still not clear for gene order especiallyThere is good directive significance.
In order to verify the application of the method on metabolic engineering bacteria, according to document " TranscriptomeanalysisguidedmetabolicengineeringofBacillussubtilisforriboflavinproduction”And document " Increasedproductionofriboflavinbymetabolicengineeringoft hePurinepathwayinBacillussubtilis " two sections of articles experiment condition respectively simulation and forecast RH33 andThe riboflavin condition of production of B.subtilisPK two strain engineering bacterias, result shows testing under given minimal medium condition,The simulation of RH33 has been obtained to 118 pre-measured reactions, and wherein 77 was to express, and 40 is low expression, and 1 is unknown. RightThe simulation of B.subtilisPK has obtained 102 pre-measured reactions, and wherein 65 was to express, and 35 is low expression, and 2 for notKnow. The method to the analog result of RH33 and B.subtilisPK further confirmed in biological wet experiment prs andPurFMNHD, and the effect of expression to raising riboflavin production of crossing of ribABGHT. Meanwhile, predict the outcome and show to be closed by PRPPBecome other pur genes (purQLCEK) and the ndk gene that in the approach of GTP, comprise to be predicted to be expression, its corresponding reaction is rightHaving the greatest impact of riboflavin production. In addition, a large amount of pre-measured reactions without experimental verification are for further testing and provideBy instructing targetedly. RH33 and B.subtilisPK are through Evolutionary experiments repeatedly and the bacterial type that obtains, their geneGroup sequence is still unintelligible. The above-mentioned predictive genes through confirmation has shown what the present invention predicted for the design of engineering bacteria metabolic engineeringAccuracy. These two experiments show that the method has good application for the unclear engineering bacteria of sequence, and this point is fineSolved the problem that wild-type strain genome yardstick Metabolic Network Model is difficult to be applied to engineering bacteria experiment instruction aspect.
Brief description of the drawings
Fig. 1 riboflavin production improves needs the distribution of the pre-measured reaction of transformation in different metabolic system; Wherein carbon:Carbohydrate metabolism; Amino: amino acid metabolism; Cofact: co-factor and vitamin metabolism; Fatty: fatty acid metabolism;Nucleo: nucleic acid and nucleotide metabolism;
Fig. 2 is the flow chart of the flux territory comparative approach of the specific metabolite output of predicted impact key reaction.
Detailed description of the invention
Below in conjunction with embodiment, the present invention is described, the scheme of embodiment described here does not limit the present invention, this area speciallyIndustry personnel can make improvements and change according to spirit of the present invention, and these described improvement and variation all should be considered as at thisIn scope of invention, scope of the present invention and essence are limited by claim. The present invention's raw material used, reagent Jun You citySell. Bacillus subtilis can obtain at China Committee for Culture Collection of Microorganisms's common micro-organisms preservation center, numbering:1.3376。
Embodiment 1
A flux territory comparative approach for the specific metabolite output of predicted impact key reaction, the method is by twiceCalculate, once compare, once sort and form, undertaken by following step:
(1) the flux territory of calculating wild type target species self-sow state:
First the method that adopts flux equilibrium to analyze, utilizes known genome yardstick Metabolic Network Model, calculates biologicalThe maximum of amount synthesis rate, when calculating, parameter setting method is as follows: according to the condition of culture of known target species and phenotype,Culture medium composition, dissolved oxygen, target product output, other accessory substance output in model are carried out to identical establishing according to experiment conditionPut; Object function is set as biomass synthetic reaction, calculates biomass synthesis rate optimal value f; Afterwards, employing flux canThe method that sex change is analyzed, the network-based Metabolic Flux territory that obtains wild type species distributes, and when calculating, parameter setting method is as follows: will give birth toThe rate setting of thing amount synthetic reaction is the maximum f obtaining in previous step, and other condition is identical with previous step, calculatesTo two vectorial Vw_minAnd Vw_max, wherein Vw_minRepresent the minimum of a value (being flux lower limit) of each reaction amount of flux in metabolism networkThe vector of composition, Vw_maxRepresent the vector of maximum (the being the flux upper limit) composition of each reaction amount of flux in metabolism network; CalculateThe metabolic flux territory that the region of the flux lower limit of gained and flux upper limit composition is wild type species distributes;
(2) growth conditions of simulative optimization type species:
The first, the method that adopts flux equilibrium to analyze, utilizes known genome yardstick Metabolic Network Model, calculates biologicalThe maximum of amount synthesis rate, when calculating, parameter setting method is as follows: according to the condition of culture of known target species and phenotype,Culture medium composition, dissolved oxygen in model are carried out to identical setting according to experiment condition, and to target product output and other by-productsThe output of thing is not restricted, and the flux of biomass synthetic reaction is set to the optimal value f of biomass synthetic reaction in step (1),Object function is set as the output of specific metabolite, simulates for the second time, calculates the maximum of specific product outputp。
The second, the method for employing flux Mutability analysis, the network-based Metabolic Flux territory of the type that is optimized species distributes, and calculatesTime parameter setting method as follows: the maximum p that the output of specific metabolite is set as obtaining in previous step, other conditionIdentical with previous step, calculate two vectorial Ve_minAnd Ve_max, wherein Ve_minRepresent each reaction amount of flux in metabolism networkThe vector of minimum of a value (being flux lower limit) composition, Ve_maxThe maximum that represents each reaction amount of flux in metabolism network (is fluxThe upper limit) composition vector; The metabolism that the region of calculating flux lower limit and the flux upper limit composition of gained is Optimization-type species is led toAmount territory distributes.
(3) once compare: the flux territory of twice calculating is compared, determine between Optimization-type and wild type and react fluxThe difference in territory, thus determine test-type is transformed into the required metabolic engineering carrying out of Optimization-type, and then formulate corresponding wetExperimental strategy: comparison and Forecasting Methodology are as follows:
For each reaction in metabolism network,
。
Wherein, vw_maxExpression wild type species are simulated the flux upper limit obtaining, vw_minRepresent that the simulation of wild type species obtainsFlux lower limit, ve_maxExpression Optimization-type species are simulated the flux upper limit obtaining, ve_minRepresent what the simulation of Optimization-type species obtainedFlux lower limit;
The above-mentioned special circumstances that predicted the outcome, as (ve_max-vw_max)/vw_maxWith (ve_max-vw_max)/vw_maxCalculateWhen the outcome symbol that obtains is contrary, predict the outcome as the unknown, cannot be predicted the outcome accurately;
(4) one minor sorts: the reaction obtaining for each prediction, is set to respectively wild type flux by its metabolic fluxThe upper limit or lower limit, have like this two kinds of set-up modes, with vlRepresent the lower limit of reaction flux, vuRepresent the upper limit of reaction flux,These two kinds of set-up modes can be expressed as:
vl=vu=vw_minAnd vl=vu=vw_max
The setting of other parameters is identical with step (2), after each setting, all enters taking the output of specific product as object functionRow calculates, and computational methods are flux equilibrium analysis, and the setting of two kinds of conditions respectively obtains a result of calculation, gets two result of calculationsMaximum, be designated as q, the change amount C of specific product output after calculating this reaction flux and changing,
The C value of all pre-measured reactions is arranged according to order from big to small, be pre-measured reaction to specific productThe sequence of yield effect size, instructs the experimental strategy of metabolic engineering genetic manipulation according to this sequence.
Embodiment 2
Actual guidance to the wet experiment of biology:
1. the result that pair bacillus subtilis produces riboflavin prediction shows to have 112 reactions need be transformed, whereinHave 80 reactions that need high expressed, the reaction of 27 low expression of need, separately has 5 reactions because the upper and lower bound in its flux territory changesThe opposite direction becoming, thus unpredictable. Wherein prs[1],purFMNHD[2]And ribABGHT[3,4]The height that gene pairs should reactExpress prediction and obtained confirmation in research before. And other high expresseds that not yet confirm through experiment and ground expression response can be doneFor the research object of further experiment.
2. in the sequence in reaction, riboflavin production being affected, other pur bases that comprise in the approach by the synthetic GTP of PRPPBecause (purQLCEK) and ndk gene were predicted to be expression, and to the having the greatest impact of riboflavin production, in addition, some synthetic paddyExcessively expressing of propylhomoserin salt or glycine reactant is very large on the impact of riboflavin, and consumes the low of glutamate or glycine reactantExpression is same very large to the output of riboflavin, and this point has shown that glutamate and glycine have in riboflavin building-up processVery important effect. In wet experimental study, can carry out for key reaction according to order from high to low according to ranking resultsRelevant experimental design.
Embodiment 3
Contrast test
Conventional carries out Simulation and analysis for wild type species by optimized method (as flux equilibrium analysis),
Method: flux equilibrium analysis
Step: utilize known genome yardstick Metabolic Network Model, calculate the maximum of biomass synthesis rate. CalculateTime parameter setting method as follows: according to the condition of culture of known target species and phenotype, by culture medium composition, molten in modelOxygen, target product output, other accessory substance output and biomass synthesis rate carry out identical setting according to experiment condition; TargetFunction is set as the output of specific metabolite.
Result: obtain the Flux Distribution of wild type species under experiment condition, not with the comparison of Optimization-type species, therebyLittle to experimental design reference value.
Flux of the present invention territory comparative approach:
Method: flux territory comparative approach is with embodiment 1.
Result: the reaction list, the improvement side that obtain can improving by experiment to improve target metabolite outputMethod, and the influence power sequence of these reactions to target metabolite output.
Embodiment 4
The checking situation of Forecasting Methodology of the present invention:
In order to verify that the application of the method on metabolic engineering bacteria is according to according to document " TranscriptomeanalysisguidedmetabolicengineeringofBacillussubtilisforriboflavinproduction”And document " Increasedproductionofriboflavinbymetabolicengineeringoft hePurinepathwayinBacillussubtilis " experiment condition of two sections of articles, and the side of introducing according to this patentMethod, simulation and forecast the condition of production of RH33 and B.subtilisPK two strain engineering bacteria riboflavin, result shows in experiment givenMinimal medium condition under, the simulation of RH33 has been obtained to 118 pre-measured reactions, wherein 77 be express, 40 is lowExpress, 1 is unknown. The simulation of B.subtilisPK has been obtained to 102 pre-measured reactions, and wherein 65 was to express, 35For low expression, 2 is unknown. The method has further been confirmed biological wet reality to the analog result of RH33 and B.subtilisPKTest middle prs and purFMNHD, and the effect of expression to raising riboflavin production of crossing of ribABGHT. Meanwhile, the table that predicts the outcomeOther pur genes (purQLCEK) and the ndk gene that in the bright approach by the synthetic GTP of PRPP, comprise were predicted to be expression, itsCorresponding reaction having the greatest impact to riboflavin production. In addition be, further without the pre-measured reaction of experimental verification in a large numberExperiment provides by instructing targetedly. RH33 and B.subtilisPK are through Evolutionary experiments repeatedly and the bacterial type that obtains,Their genome sequence is still unintelligible. The above-mentioned predictive genes through confirmation has shown that the present invention is for engineering bacteria metabolic engineeringThe accuracy of design prediction. These two experiments show that the method has good application for the unclear engineering bacteria of sequence,This point has solved well wild-type strain genome yardstick Metabolic Network Model and has been difficult to be applied to engineering bacteria experiment instruction sideThe problem of face.
Bibliography
[1]Shi,S.,Chen,T.,Zhang,Z.,Chen,X.,Zhao,X.,2009a.TranscriptomeanalysisguidedmetabolicengineeringofBacillussubtilisforriboflavinproduction.MetabEng.11,243-52.
[2]Shi,S.,Zhang,Z.,Chen,X.,Chen,T.,Zhao,X.,2009b.IncreasedproductionofriboflavinbymetabolicengineeringofthepurinepathwayinBacillussubtilis.BiochemicalEngineeringJournal.46,28-33.
[3]HümbelinM,GriesserV,KellerT,SchurterW,HaikerM,HohmannHP,RitzH,RichterG,BacherA,1999.GTPcyclohydrolaseIIand3,4-dihydroxy-2-butanone4-phosphatesynthaseareratelimitingenzymesinriboflavinsynthesisofanindustrialBacillussubtilisstrainusedforriboflavinproduction.JIndMicrobiolBiotech.22,1-7.
[4]LiXJ,ZhouSQ,ChenT,ChenX,ZhaoXM,2005.EffectoftranscriptionalmodifiedriboflavinoperononriboflavinbiosyntheticabilityinBacillussubtilis.2ndChineseNationalChemicalandBiochemicalEngineeringAnnualMeeting.Beijing,China.。
Claims (2)
1. a flux territory comparative approach for the specific metabolite output of predicted impact key reaction, it is characterized in that the method byTwice calculating, once compares, once sorts and form, and is undertaken by following step:
(1) the flux territory of calculating wild type target species self-sow state:
First the method that adopts flux equilibrium to analyze, utilizes known genome yardstick Metabolic Network Model, calculates biomass and closesThe maximum that becomes speed, when calculating, parameter setting method is as follows: according to the condition of culture of known target species and phenotype, by mouldIn type, culture medium composition, dissolved oxygen, target product output, other accessory substance output are carried out identical setting according to experiment condition; OrderScalar functions is set as biomass synthetic reaction, calculates biomass synthesis rate optimal value f;
Afterwards, adopt the method for flux Mutability analysis, the network-based Metabolic Flux territory that obtains wild type species distributes, and when calculating, joinsNumber methods to set up as follows: be the optimal value f obtaining in previous step by the rate setting of biomass synthetic reaction, other condition withPrevious step is identical, calculates two vectorial Vw_minAnd Vw_max, wherein Vw_minRepresent each reaction amount of flux in metabolism networkMinimum of a value, i.e. flux lower limit, the vector of composition, Vw_maxRepresent the maximum of each reaction amount of flux in metabolism network, on fluxThe vector of limit composition; The region of the flux lower limit of calculating gained and flux upper limit composition is the metabolic flux territory of wild type speciesDistribute;
(2) growth conditions of simulative optimization type species:
The first, the method that adopts flux equilibrium to analyze, utilizes known genome yardstick Metabolic Network Model, calculates biomass and closesThe maximum that becomes speed, when calculating, parameter setting method is as follows: according to the condition of culture of known target species and phenotype, by mouldIn type, culture medium composition, dissolved oxygen carry out identical setting according to experiment condition, and to target product output and other accessory substancesOutput is not restricted, and the flux of biomass synthetic reaction is set to the optimal value f of biomass synthetic reaction in step (1), targetFunction is set as the output of specific metabolite, simulates for the second time, calculates the maximum p of specific product output;
The second, the method for employing flux Mutability analysis, the network-based Metabolic Flux territory of the type that is optimized species distributes, and when calculating, joinsNumber methods to set up are as follows: the output of specific metabolite is set as to the maximum p that obtains in (2) first step, other conditionIdentical with (2) first step, calculate two vectorial Ve_minAnd Ve_max, wherein Ve_minRepresent that in metabolism network, each reaction is logicalThe minimum of a value of value, i.e. the vector of flux lower limit composition, Ve_maxRepresent the maximum of each reaction amount of flux in metabolism network, logicalThe vector of amount upper limit composition; The metabolism that the region of calculating flux lower limit and the flux upper limit composition of gained is Optimization-type species is led toAmount territory distributes;
(3) once compare: the flux territory of twice calculating is compared, determine and between Optimization-type and wild type, react flux territoryDifference, thus determine test-type is transformed into the required metabolic engineering carrying out of Optimization-type, and then formulate corresponding wet experimentStrategy, comparison and Forecasting Methodology are as follows:
For each reaction in metabolism network,
Wherein, vw_maxExpression wild type species are simulated the flux upper limit obtaining, vw_minRepresent that the simulation of wild type species obtains logicalAmount lower limit, ve_maxExpression Optimization-type species are simulated the flux upper limit obtaining, ve_minExpression Optimization-type species are simulated the flux obtainingLower limit;
The above-mentioned special circumstances that predicted the outcome, whenCalculateWhen the outcome symbol that obtains is contrary, predict the outcome as the unknown, cannot be predicted the outcome accurately;
(4) one minor sorts: the reaction obtaining for each prediction, is set to respectively the upper of wild type flux by its metabolic fluxLimit or lower limit, have two kinds of set-up modes, like this with vlRepresent the lower limit of reaction flux, vuRepresent reaction flux the upper limit, this twoPlanting set-up mode is expressed as:
The setting of other parameters is identical with step (2), after each setting, all carries out taking the output of specific product as object functionCalculate, computational methods are flux equilibrium analysis, and the setting of two kinds of conditions respectively obtains a result of calculation, get two result of calculationsLarge value, is designated as q, calculates the change amount C of the rear specific product output of this reaction flux change,
The C value of all pre-measured reactions is arranged according to order from big to small, be pre-measured reaction to specific product outputAffect big or small sequence, according to this sequence, the experimental strategy of metabolic engineering genetic manipulation is instructed.
2. described in claim 1, method is produced the application in riboflavin employing bacillus subtilis.
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