CN105740645B - Based on Gene regulation fast simulated annealing algorithm - Google Patents
Based on Gene regulation fast simulated annealing algorithm Download PDFInfo
- Publication number
- CN105740645B CN105740645B CN201610062683.2A CN201610062683A CN105740645B CN 105740645 B CN105740645 B CN 105740645B CN 201610062683 A CN201610062683 A CN 201610062683A CN 105740645 B CN105740645 B CN 105740645B
- Authority
- CN
- China
- Prior art keywords
- algorithm
- opt
- processor
- glycerol
- variable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 33
- 238000002922 simulated annealing Methods 0.000 title claims abstract description 15
- 230000033228 biological regulation Effects 0.000 title claims abstract description 9
- 108090000623 proteins and genes Proteins 0.000 title claims abstract description 9
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 claims abstract description 66
- 238000000855 fermentation Methods 0.000 claims abstract description 13
- 230000004151 fermentation Effects 0.000 claims abstract description 11
- 230000000813 microbial effect Effects 0.000 claims abstract description 7
- 230000003834 intracellular effect Effects 0.000 claims abstract description 6
- 239000002245 particle Substances 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 9
- 239000000126 substance Substances 0.000 claims description 6
- 238000005266 casting Methods 0.000 claims description 3
- 239000012895 dilution Substances 0.000 claims description 3
- 238000010790 dilution Methods 0.000 claims description 3
- 230000035484 reaction time Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 5
- 239000000463 material Substances 0.000 abstract description 4
- 239000000243 solution Substances 0.000 description 11
- 230000006870 function Effects 0.000 description 10
- 238000005457 optimization Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000000137 annealing Methods 0.000 description 4
- OKWLCUWJPPORKE-UHFFFAOYSA-N 1,2,3,4,5-pentanitro-6-(2-nitrophenyl)sulfanylbenzene Chemical compound [O-][N+](=O)C1=CC=CC=C1SC1=C([N+]([O-])=O)C([N+]([O-])=O)=C([N+]([O-])=O)C([N+]([O-])=O)=C1[N+]([O-])=O OKWLCUWJPPORKE-UHFFFAOYSA-N 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 241000193171 Clostridium butyricum Species 0.000 description 2
- LYCAIKOWRPUZTN-UHFFFAOYSA-N Ethylene glycol Chemical compound OCCO LYCAIKOWRPUZTN-UHFFFAOYSA-N 0.000 description 2
- 241000588748 Klebsiella Species 0.000 description 2
- 238000007323 disproportionation reaction Methods 0.000 description 2
- 238000005183 dynamical system Methods 0.000 description 2
- 230000002255 enzymatic effect Effects 0.000 description 2
- BRARRAHGNDUELT-UHFFFAOYSA-N 3-hydroxypicolinic acid Chemical compound OC(=O)C1=NC=CC=C1O BRARRAHGNDUELT-UHFFFAOYSA-N 0.000 description 1
- 241000894006 Bacteria Species 0.000 description 1
- 241000588923 Citrobacter Species 0.000 description 1
- 241001112696 Clostridia Species 0.000 description 1
- 241000588914 Enterobacter Species 0.000 description 1
- 241000588747 Klebsiella pneumoniae Species 0.000 description 1
- 230000009056 active transport Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- WGCNASOHLSPBMP-UHFFFAOYSA-N hydroxyacetaldehyde Natural products OCC=O WGCNASOHLSPBMP-UHFFFAOYSA-N 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 230000037317 transdermal delivery Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B99/00—Subject matter not provided for in other groups of this subclass
Abstract
The present invention relates to one kind to be based on Gene regulation fast simulated annealing algorithm, and the algorithm solves glycerol microbial fermentation control problem, and function x (t) is set as
Description
Technical field
The invention belongs to technical field of bioengineering, and in particular to one kind is based on Gene regulation fast simulated annealing algorithm.
Background technique
In the presence of the microorganism that can convert glycerol into 1,3-PD, mainly several bacteriums in nature.Such as:
Klebsiella, Citrobacters, Enterobacter and Clostridia etc., wherein klebsiella
(Klebsiella pneumoniae) and with higher turn of clostridium butyricum (Clostridium butyricum)
Rate and 1,3-PD production capacity, thus more paid close attention to.Early in nineteen fifty-three, annealing algorithm has been introduced into control field,
Annealing thought is successfully introduced into Combinatorial Optimization field by nineteen eighty-three, S.Kirkpatrick etc..1,3-PD production was controlled
Journey can solve the random optimization problem that strategy is iteratively solved based on Monte-Carlo using annealing algorithm, and starting point is to be based on
Similitude in physics between the annealing process of solid matter and general combinatorial optimization problem.Simulated annealing is from a certain higher
Initial temperature is set out, and with the continuous decline of temperature parameter, join probability kick characteristic finds objective function at random in solution space
Globally optimal solution can be jumped out probabilityly in locally optimal solution and finally tend to global optimum.Simulated annealing is a kind of
General optimization algorithm, theoretically algorithm has the global optimization performance of probability, is applied in engineering at present, such as
The fields such as VLSI, production scheduling, control engineering, machine learning, neural network, signal processing.With simulated annealing theory
, there is the fast simulated annealing algorithm of numerous species in research, this speed for allowing for calculating greatly is promoted, due to asking
Topic includes 26112 system continuous parameters and 9217 path discrete parameters, and calculation amount is very huge, only on serial computer
It can not solve the problems, such as.
Summary of the invention
For overcome the deficiencies in the prior art, a kind of fast simulated annealing calculation continuously fermented based on Gene regulation is proposed
Method, algorithm optimization method solves control problem encountered, using the concentration of extracellular 1,3-PD as performance indicator
Mix dynamical system according to fast simulated annealing algorithm solution is non-linear.
The technical solution of the present invention is as follows: being based on Gene regulation fast simulated annealing algorithm, the algorithm solves the micro- life of glycerol
Object ferment control problem, function x (t) are set as the concentration in the glycerol microbial fermentation controlling element of t moment: that is,
X (t)=(x1(t),x2(t),…,x14(t)) I, is enabledN={ 1,2 ..., N }, N are test number (TN), and W is path set, | W
| it is number of path, t
For reaction time, t ∈ [0, T], T ∈ R+,For state variable, j
∈IN,k∈I|W|Indicate that controlling element is in the concentration of t moment when jth time test kth paths；DjFor the glycerol dilution rate of jth time test,For the glycerol initial concentration j ∈ I of jth time testN, under 37 DEG C, PH=7 anaerobic condition, each point of state vector x
Amount control is in the range of the lower bound of state vector x and the upper bound provide, the lower bound of state vector x and the upper bound are as follows:
x*=(0.0001,0.01,0,0,0,0,0,0,0,0,0,0,0,0)T,
x*=(10,2039,939.5,1026,360.9,2039,200,939.5,30,30,30,30,30,3 0)T,
Then feasible set is expressed asMeanwhile the feasible set of system parameter u are as follows:
If the interval of glycerol dissimilation production 1,3-PD is respectively [0, t with the time range continuously fermentedb] and [tb, T], then
To j ∈ IN,k∈I|W|Glycerol biology dismutation can be described as following the non-linear of enzymatic and gene regulation and mix power
System HNDS (j, k):
Wherein f=(f1,f2,...,f14)T, uk∈R30, [0, T] ∈ R+, this System describe is glycerol elder generation batch fermentation
(1) continuously ferment after the process of (2), tbFor the end time of batch fermentation, D in Batch fermentation processj=0. sets glycerol dissimilation life
It produces the interval of 1,3-PD and the time range continuously fermented is respectively [0, tb] and [tb, T], wherein 0 < tb< T <+∞ is to giving
Fixed k ∈ IW,j∈IN, it is assumed that system NHDS (j, k) is in moment tsReach approximation steady state, remembers that extracellular approximation steady state concentration is x
(ts；x0,uk,j,wk),i∈I3Enable yj(i),i∈I3When reaching stable state for the extracellular first three substance that jth time test measures
Concentration, the test bit of extracellular substances are defined as with the relative error for calculating data
The robustness of intracellular matter is defined as
Wherein U is the disturbance space of u, u' ∈ Bσ(u), σ > 0 be about u neighborhood ball radius, φ (u'-u) be about
(u'-u) objective function of the probability density function of ∈ U, foundation is as follows:
J (u, j, k)=τ1SSD(u,j,k)+τ2MSD(u,j,k),
Utilize Controlling model
||f(x,uk,j,wk)||≤ξ,
u(j,k)∈U(x0,j,k),
wk∈W
In this algorithm, all variables with opt combination letter for label are expressed as processed variable, and wherein ξ is
One sufficiently small constant greater than zero, the parallel fast simulated annealing algorithm key step of construction are as follows:
Step 1: n is distributed for algorithmpA processor, total number of particles Psize, then the population in each processor isThe following primary data of typing in root processor:
Step 1.1, typing given data u*,u*,x*,x*,T0,Tf, ε, N and each known parameters
Step 1.2, typing path set W,
Step 1.3, typing test data DC,yj(i),i∈I3
Step 1.4 sets k=1, dt=1, T=T0
Step 1.5, takesλ is random between 0 and 1
Number, enables Jopt=J (uopt,j,k),kopt=k
Step 2: by the datacast MPI_Bcast in root processor into each sub-processor
Step 3: in each sub-processorIt is upper to execute following process:
Step 3.1, from UadIn be randomly generatedThe number of a particle, each particle isCalculate the corresponding target function value of each particle
Step 3.2, if n > N, is transferred to Step4.2, otherwise takesIt calculatesWith
If Δ J < 0 orThenIt is no
ThenIt is constant.
Step 4: casting sub-processorOnInto root processor, then execute following steps:
Step 4.1 is calculated
Step 4.2, if Tf> T is then transferred in next step, otherwise T=T δdt, δ is the random number between 0 and 1, goes to step
1.5。
Step 4.3, ifThenOtherwise enter in next step；
Step 4.4, if k > | W |, it is transferred in next step, otherwise k=k+1, is transferred to step3.
Beneficial effect of the present invention
1, the present invention is not required to require the partial derivative of objective function that can find a globally optimal solution, and is easy to be added
Constraint condition, this algorithm weaken the correlation that calculated result quality is chosen with initial point, while also avoiding algorithm in part
The case where optimal solution is without solution；
2, the present invention utilizes approximate stability, intraor extracellular material concentration relative error and the cell for system of continuously fermenting
Biological Robustness of interior material concentration etc. is main constraints, constructs the calculation method of Optimal Control Problem.
Specific embodiment
Algorithm of the present invention solves glycerol microbial fermentation control problem, and function x (t) is set as the glycerol in t moment
The concentration of microbial fermentation controlling element: that is,
X (t)=(x1(t),x2(t),…,x14(t)), x1(t),x2(t),…,x14(t), I is enabledN=1,2 ..., and N }, N is
Test number (TN), W are path set, | W | it is number of path,T is reaction time, t
∈[0,T],T∈R+,For state variable, j ∈ IN,k∈I|W|Table
Concentration of the controlling element in t moment when showing jth time test kth paths；DjFor the glycerol dilution rate of jth time test,For the glycerol initial concentration j ∈ I of jth time testN, under 37 DEG C, PH=7 anaerobic condition, each point of state vector x
Amount control is in the range of the lower bound of state vector x and the upper bound provide, the lower bound of state vector x and the upper bound are as follows:
x*=(0.0001,0.01,0,0,0,0,0,0,0,0,0,0,0,0)T,
x*=(10,2039,939.5,1026,360.9,2039,200,939.5,30,30,30,30,30,3 0)T,
Then feasible set is expressed asMeanwhile the feasible set of system parameter u are as follows:
If the interval of glycerol dissimilation production 1,3-PD is respectively [0, t with the time range continuously fermentedb] and [tb, T],
In 0 < tb< T <+∞, then to j ∈ IN,k∈I|W|Glycerol biology dismutation can be described as following enzymatic and gene regulation
Non-linear mix dynamical system HNDS (j, k):
Wherein f=(f1,f2,...,f14)T, uk∈R30, [0, T] ∈ R+.This System describe is glycerol elder generation batch fermentation
(1) continuously ferment after the process of (2), tbFor the end time of batch fermentation, D in Batch fermentation processj=0.
In view of the possible inhibiting mechanism of 3-HPA, it is assumed that glycerol and 1, it is active transport that 3-PD, which flies transdermal delivery mode,
It is combined with Passive diffusion, to given k ∈ I|W|,j∈IN, it is assumed that system NHDS (j, k) is in moment tsReach approximation steady state, remembers
Extracellular approximation steady state concentration is x (ts；x0,uk,j,wk),i∈I3Y is enabledj(i),i∈I3It is measured for jth time test extracellular
First three substance reaches concentration when stable state, and the test bit of extracellular substances is defined as with the relative error for calculating data
For this purpose, the objective function established is as follows:
J (u, j, k)=τ1SSD(u,j,k)+τ2MSD(u,j,k),
Wherein τ1,τ2It is weight coefficient, is the importance for balanced relative error and robustness, with extracellular 1,3- third
The concentration of glycol is performance indicator, to mix the approximate stability of nonlinear dynamic system, system of continuously fermenting, intracellular foreign object
Biological Robustness of matter concentration relative error and intracellular matter concentration etc. is main constraints, and establishing model is
||f(x,uk,j,wk)||≤ξ,
u(j,k)∈U(x0,j,k),
wk∈W
Wherein ξ is a sufficiently small constant greater than zero, for judging whether service system HNDS (j, k) reaches approximate
Stable state.
In this algorithm, all variables with opt combination letter for label are expressed as processed variable, including variable
kopt, Jopt, uopt, up opt, equally, all variables with current and next combination letter for label are expressed as currently becoming
Amount and next variable, the concrete meaning that all of above variable represents is constant, the parallel fast simulated annealing algorithm that we construct
(SA) key step is as follows:
Step 1: n is distributed for algorithmpA processor, total number of particles Psize, then the population in each processor isThe following primary data of typing in root processor:
Step 1.1, typing given data u*,u*,x*,x*,T0,Tf, ε, N and each known parameters
Step 1.2, typing path set W,
Step 1.3, typing test data DC,yj(i),i∈I3
Step 1.4 sets k=1, dt=1, T=T0
Step 1.5, takesλ is random between 0 and 1
Number, enables Jopt=J (uopt,j,k),kopt=k
Step 2: by the datacast MPI_Bcast in root processor into each sub-processor
Step 3: in each sub-processorIt is upper to execute following process:
Step 3.1, from UadIn be randomly generatedThe number of a particle, each particle isCalculate the corresponding target function value of each particle
Step 3.2, if n > N, is transferred to Step4.2,
Otherwise it takes
It calculatesWith
If Δ J < 0 or
ThenOtherwiseIt is constant.
Step 4: casting sub-processorOnInto root processor, then execute following steps:
Step 4.1 is calculated
Step 4.2, if Tf> T is then transferred in next step, otherwise T=T δdt, δ is the random number between 0 and 1, goes to step
1.5。
Step 4.3, ifThenOtherwise enter in next step.
Step 4.4, if k > | W |, it is transferred in next step, otherwise k=k+1, is transferred to step 3.
The present invention is not required to require the partial derivative of objective function that can find a globally optimal solution, and is easy to be added about
Beam condition, this algorithm weaken the correlation that calculated result quality is chosen with initial point, at the same also avoid algorithm part most
The case where excellent solution is without solution；The present invention using the approximate stability of system of continuously fermenting, intraor extracellular material concentration relative error with
And Biological Robustness of intracellular matter concentration etc. is main constraints, constructs the calculation method of Optimal Control Problem.
Claims (1)
1. being based on Gene regulation fast simulated annealing algorithm, the algorithm solves glycerol microbial fermentation control problem, function x
(t) it is set as the concentration of the glycerol microbial fermentation controlling element at t: that is,
X (t)=(x1(t),x2(t),…,x14(t)) I, is enabledN={ 1,2 ..., N }, N are test number (TN), and W is path set, and W is road
Diameter number, T is reaction
Time, t ∈ [0, T], T ∈ R+, DcFor the c times test glycerol dilution rate, under 37 DEG C, PH=7 anaerobic condition, state to
Each component control of x is measured in the range of the lower bound of the state vector x and the upper bound provide, the lower bound of the state vector x
The upper bound and are as follows:
x*=(0.0001,0.01,0,0,0,0,0,0,0,0,0,0,0,0)T,
x*=(10,2039,939.5,1026,360.9,2039,200,939.5,30,30,30,30,30,3 0)T, then feasible set
It is expressed asMeanwhile the feasible set of system parameter u are as follows:
To given k ∈ IW,j∈IN, remember that extracellular approximation steady state concentration is x (ts；x0,uk,j,wk),i∈I3, enable yj(i),i∈
I3Reach concentration when stable state, the test bit and meter of extracellular substances for the extracellular first three substance that jth time test measures
The relative error for the evidence that counts are as follows:
The robustness of intracellular matter:
Wherein U is the disturbance space of u, u' ∈ Bσ(u), σ > 0 is the radius of the neighborhood ball about u, and φ (u'-u) is about (u'-
U) objective function of the probability density function of ∈ U, foundation is as follows:
J (u, j, k)=τ1SSD(u,j,k)+τ2MSD(u,j,k),
τ1,τ2It is weight coefficient, is the importance for balanced relative error and robustness, ξ is one sufficiently small greater than zero
Constant utilizes Controlling model
||f(x,uk,j,wk)||≤ξ,
u(j,k)∈U(x0,j,k),
wk∈W
In this algorithm, all variables with opt combination letter for label are expressed as processed variable, including variable kopt,
Jopt, uoptOr up opt, equally, the variable with current and next combination letter for label is expressed as current variable under
One variable, the specific physical meaning that all of above variable represents is constant, it is characterized in that:
The fast simulated annealing algorithm key step of construction is as follows:
Step 1: n is distributed for algorithmpA processor, total number of particles Psize, then the population in each processor isThe following primary data of typing in root processor:
Step 1.1, typing given data u*,u*,x*,x*,T0,Tf, ε, N and each known parameters；
Step 1.2, typing path set W,
Step 1.3, typing test data DC,yj(i),i∈I3；
Step 1.4 sets k=1, dt=1, T=T0；
Step 1.5, takesRandom number of the λ between 0 and 1 enables
Jopt=J (uopt,j,k),kopt=k；
Step 2: by the datacast MPI_Bcast in root processor into each sub-processor；
Step 3: in each sub-processorIt is upper to execute following process:
Step 3.1, from UadIn be randomly generatedThe number of a particle, each particle isCalculate the corresponding target function value of each particle
Step 3.2, if n > N, is transferred to Step 4.2,
Otherwise, it takes
It calculatesWith
If Δ J < 0 or
ThenOtherwiseIt is constant；
Step 4: casting sub-processorOnInto root processor, then execute following steps:
Step 4.1 is calculated
Step 4.2, if Tf> T is then transferred in next step, otherwise T=T δdt, δ is the random number between 0 and 1, goes to step
1.5；
Step4.3, if
ThenOtherwise enter in next step；
Step 4.4, if k > | W |, it is transferred in next step, otherwise k=k+1, is transferred to step 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610062683.2A CN105740645B (en) | 2016-01-29 | 2016-01-29 | Based on Gene regulation fast simulated annealing algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610062683.2A CN105740645B (en) | 2016-01-29 | 2016-01-29 | Based on Gene regulation fast simulated annealing algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105740645A CN105740645A (en) | 2016-07-06 |
| CN105740645B true CN105740645B (en) | 2019-02-26 |
Family
ID=56246803
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610062683.2A Active CN105740645B (en) | 2016-01-29 | 2016-01-29 | Based on Gene regulation fast simulated annealing algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105740645B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108333942B (en) * | 2018-02-07 | 2021-06-15 | 湖州师范学院 | Strong stability model of nonlinear time-lag power system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101639902A (en) * | 2009-08-12 | 2010-02-03 | 江苏大学 | Modeling method of support vector machine (SVM)-based software measurement instrument in biological fermentation process |
| CN102517399A (en) * | 2012-01-09 | 2012-06-27 | 青岛理工大学 | Thermal transmission detection method based on DNA (Deoxyribonucleic Acid) amplification |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6607902B2 (en) * | 2000-05-04 | 2003-08-19 | Novozymes A/S | Cell-wall degrading enzyme variants |
-
2016
- 2016-01-29 CN CN201610062683.2A patent/CN105740645B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101639902A (en) * | 2009-08-12 | 2010-02-03 | 江苏大学 | Modeling method of support vector machine (SVM)-based software measurement instrument in biological fermentation process |
| CN102517399A (en) * | 2012-01-09 | 2012-06-27 | 青岛理工大学 | Thermal transmission detection method based on DNA (Deoxyribonucleic Acid) amplification |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105740645A (en) | 2016-07-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Henson et al. | Dynamic flux balance analysis for synthetic microbial communities | |
| Kim et al. | OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains | |
| Angelova et al. | Tuning genetic algorithm parameters to improve convergence time | |
| González-Figueredo et al. | Fermentation: Metabolism, kinetic models, and bioprocessing | |
| Choon et al. | Gene knockout identification using an extension of bees hill flux balance analysis | |
| Niu et al. | Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression | |
| Hunag et al. | Optimization of fed-batch fermentation using mixture of sugars to produce ethanol | |
| Ye et al. | Nonlinear hybrid system and parameter identification of microbial fed-batch culture with open loop glycerol input and pH logic control | |
| Bengtsson et al. | Identification of common traits in improved xylose‐growing Saccharomyces cerevisiae for inverse metabolic engineering | |
| Biazi et al. | A differential evolution approach to estimate parameters in a temperature-dependent kinetic model for second generation ethanol production under high cell density with Spathaspora passalidarum | |
| CN105740645B (en) | Based on Gene regulation fast simulated annealing algorithm | |
| Shen et al. | Parameter identification and optimization algorithm in microbial continuous culture | |
| Itto-Nakama et al. | Prediction of ethanol fermentation under stressed conditions using yeast morphological data | |
| Shokrkar et al. | Model‐based evaluation of continuous bioethanol production plant | |
| Henriques et al. | The Monod model is insufficient to explain biomass growth in nitrogen-limited yeast fermentation | |
| Mazutti et al. | Mathematical modeling of Kluyveromyces marxianus growth in solid-state fermentation using a packed-bed bioreactor | |
| Yang et al. | Biomass concentration prediction via an input-weighed model based on artificial neural network and peer-learning cuckoo search | |
| Parambil et al. | In silico analysis of bioethanol overproduction by genetically modified microorganisms in coculture fermentation | |
| Niu et al. | Modeling and parameter updating for nosiheptide fed-batch fermentation process | |
| CN105511269B (en) | A kind of Gene regulation nonlinear dynamic system Controlling model | |
| Gao et al. | Modeling and identification of microbial batch fermentation using fuzzy expert system | |
| CN105740599B (en) | A kind of Gene regulation, which is continuously fermented, mixes dynamical system | |
| Li et al. | Research on neural network quality prediction model based on genetic algorithm | |
| Sriyudthsak et al. | Identification of bottleneck enzymes with negative dynamic sensitivities: ethanol fermentation systems as case studies | |
| Jatoi et al. | Mathematical modeling for ethanol production from molasses using thermotolerant kluyeromyces marxians |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200430 Address after: Room 402, floor 4, building a, Chuangzhi building, No. 17, Xinghuo Road, Jiangbei new district, Nanjing City, Jiangsu Province Patentee after: Nanjing mcwell Electromagnetic Technology Co.,Ltd. Address before: 266000 Shandong province Qingdao City, Fushun Road No. 11 Patentee before: QINGDAO TECHNOLOGICAL University |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231116 Address after: Room B501, Huajian Building, No.148 Xinhua Road, Dachang Street, Jiangbei New District, Nanjing City, Jiangsu Province, 211899 Patentee after: Nanjing Zhihui robot Information Technology Co.,Ltd. Address before: Room 402, 4th Floor, Building A, Chuangzhi Building, No. 17 Xinghuo Road, Jiangbei New District, Nanjing City, Jiangsu Province, 210044 Patentee before: Nanjing mcwell Electromagnetic Technology Co.,Ltd. |
|
| TR01 | Transfer of patent right |