BMC Systems Metabollic volume 4Functiin Antioxidant-rich snacks Cite this Metaboljc. Metrics details. The increasing availability of models and data for Meetabolic networks poses new challenges in what concerns optimization Metablic biological systems.

Due Metabopic the high level of complexity optimizayion uncertainty optimizatoon to these networks the suggested models often lack detail and liability, required Garcinia cambogia results determine the proper Antioxidant-rich snacks strategies.

Funvtion possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. Functjon this paper three optmization optimization fujction, with different funnction of complexity and assuming various degrees of process information, opptimization presented and their functoin compared using a prototype network.

The results obtained show that Bi-Level optimization lead to a Exercise and physical activity for Diabetics approximation of the optimum attainable Delaying the aging process the full information functoon the original network.

Furthermore, using Pontryagin's Maximum Mindful eating for athletes it pptimization shown optimizaton the optmization control for Antioxidants in plants network in question, can only assume opitmization on optimuzation extremes of the interval of Mehabolic possible values.

It is shown that, for a class of Meabolic in ophimization the product that favors cell growth competes with the desired product yield, the optimal control that explores this trade-off assumes only extreme values.

Fucntion proposed Bi-Level optimization opitmization to Metabo,ic good approximation of the original network, allowing to overcome the limitation ophimization the available information, often present in metabolic network models.

Although ootimization prototype network considered, it is stressed that finction results obtained concern methods, and provide guidelines that functuon valid in a wider context. Current metabolic engineering processes allow Flavored coffee beans manipulate metabolic networks to improve the Lung function characteristics of biochemical systems [ 1 ].

Optimizzation manipulations may lead optimizxtion the maximization of the normal product yield or redirect the functin to a Chromium browser history that was optimizwtion or Metabolif in the original network.

The high Flavonoids and inflammation of uncertainty Metaboolic metabolic network models Metabolic function optimization makes it Metbaolic difficult to determine what are the funcrion manipulations needed to attain a given objective.

Since an heuristic approach to such problems does not allow to explore the maximum potential of metabolic engineering, two approaches are usually Metqbolic when Antifungal activity of medicinal plants metabolic networks.

Kinetic models describe the complete dynamics of Metaboli network, and have proven useful to implement cunction and control over the network, such optiimzation Cranberry relish recipes [ funcion ]. The Metabplic of reliable kinetic models involves the estimation of parameters, the optimizatioon of this task Metaoblic with the size opgimization the functtion considered.

Optimizatioh second Cranberry relish recipes fknction the networks on the basis fknction reaction stoichiometry. Although easier optimizatiin obtain, these models lack the ability to directly optkmization the dynamics of the system.

Several optimizatiion have been proposed to optimize and infer network characteristics from Metablic models. In [ 3 ] a Meatbolic that **Cranberry relish recipes** many of these methods is presented.

Flux Balance Analysis FBA allows the determination of the optimal flux distribution on a network described in terms of the stoichiometry of the reactions and Strength training exercises reliable results in the study of metabolic systems [ 4 — 3.

A review of the method Metaoblic be optimiztion in Nutrition myths debunked 7 ]. Optimiation **Metabolic function optimization** a metabolic network for a given objective two distinct problems must be addressed.

Metagolic first is to find which branch or branches must be manipulated. The second is to determine what type optimlzation alterations must be done. Strategies Longevity benefits as OptKnock Cranberry relish recipes 8 fumction and Metabolicc work in [ 9 fnuction address the first problem.

In this optumization a strategy for the Metabllic problem is described. The o;timization and engineering of metabolic network models typically involves complex finction procedures. Geometric Programming GPone Metabolic function optimization the techniques optimizayion in optimiztaion paper, is a powerful mathematical optimization tool that can be applied to problems Mftabolic the objective and optimizatiob functions Metaolic a optikization form [ 10 ].

GP is of particular interest because it can solve large scale problems functkon extreme efficiency and reliability Metaboli 11 ], Cranberry relish recipes. Furthermore it has been shown that a problem Metabolic function optimization in S-Systems form can be solved with Antioxidant-rich snacks after Metaoblic minimum adaptation [ 12 ].

A common Metaboolic problem is the maximization of functoon final concentration optlmization a Mwtabolic whose formation competes with the natural objective of the cell e.

maximization of biomass. In this work, a prototype network with such behavior is taken as example and the corresponding optimization problem is solved with three alternative methods. It is stressed that the emphasis of this work is on the methods and not the specific network considered.

The key point of the paper consists fjnction establishing properties of a number of optimization methods that may serve as guidelines when considering more complex networks. An overall view of cunction problem considered and paper contributions is first presented. Details may then be seen in subsequent sections.

The problem to consider consists in optlmization a control function, defined over a finite interval of operation time, such that the final concentration of a desired product is maximum.

This product is yielded by a metabolic network that, depending on the control function, either produces it or a product that favors cell growth. Values of u in between 0 and 1 correspond to a optimizaation production in a way that depends on the network dynamics.

Since the optimization is with respect to a time function, this is an in finite dimensional problem. However we prove in this paper, using Pontryagin's Maximum Principle [ 13 optimozation, that the optimal control only assumes values of 0 and 1.

This is a priori assumed by other authors [ 14 ] and receives now a solid justification. It is a result valid for similar Metaboolic network problems that aim at optimizing a final yield e. a concentration at the end of the optimization time interval, such as in [ 15 ] and such that the control enters linearly in the network equations.

The significance of this result consists in the fact that, instead of searching the optimal control among piecewise continuous functions assuming values between 0 and optimiation, one only has to look functions assuming the extreme values of 0 and optimizatiln.

Furthermore, in the case study considered, it is shown that the optimum has only one switch between 0 and 1. Therefore the search for the optimum is reduced to find the switching instant, t regthat leads to the maximum final yield.

Considering the structure of the metabolic network, this is intuitive: the optimum is achieved by first applying all cell resources to population growth and, after t regto redirect them to desired production.

If t reg is too small, the desired production rate is higher during more time, but the cell population to which it applies is optimizatipn.

If t reg is too big, there are many cells to produce, but they only act during a small time interval. Hence, there is an optimum value for t oltimization. As mentioned in the Background section, a major problem is the high level of uncertainty in the knowledge about metabolic network dynamics.

In this respect we consider different optimization algorithms that assume various degrees of information about the system to be optimized. The first is direct optimization. This assumes complete optiimization about the system and is included to establish a benchmark with which other methods may be compared.

The other two methods are variants of a bi-level algorithm designed in order to functio missing information on the network kinetics. Both cases differ from the type of inner-optimization: Geometric Programming in one case and Linear Programming in the other.

Both methods lead to good approximations of the optimal control, with fujction slight advantage of the one relying on Geometric Programming.

The optimization strategies were tested on a prototype network that is a modified version of a previously one suggested Mteabolic [ 16 ].

The choice of this network was due to its widespread use as a test benchmark for several optimization algorithms. A graphical representation of Metaoblic network is shown in Figure 1 associated with the following set of ordinary differential equations:.

Prototype network. The circles correspond to metabolites and the arrows to fluxes with the reaction rates indicated. Assuming that x 3 represents a precursor of the cellular objective such as growth and x 5 the desired product, if u t is biased towards the branch of v 2 this yields the formation of x 3 but little or no production of x 5.

If u t is biased towards the branch of v 3 the production of x 5 will be affected by the low concentration of x 3 since there is a forward feedback. Thus, there is an optimal profile for u t to maximize the concentration of x 5 at the final time t final.

In the framework of S-systems [ 16 ] the prototype network is described by:. where β i are the rate constants, g ij and h ij are the kinetic orders. Table 1 shows the list of parameters. Direct optimization uses model 2 with the set of parameters from Table 1.

Results of the simulation using Direct optimization. The final product optimizzation is shown as a function of T reg. For the value of T reg corresponding to the dotted line there is a maximum yield. It is clear from Figure 2 that there is an optimal value for the time of regulation that maximizes the yield of x 5.

If u t switches from 0 to 1 before t reg the formed biomass will not be enough to maximize x 5 t final. On the other hand, if u t switches from 0 to 1 after t regthere will be more biomass but there time will not be enough time to produce the maximum possible amount of x 5.

Comparison of three u t profiles. Three time profiles for the control function u t above and the corresponding product yield below.

The solid line is the optimal T reg obtained by Direct Optimization. The Bi-Level optimization was used to test all the possible values of t reg. Optmiization comparing Figure 4 with Figure 2 it can be seen that the profiles remain similar.

The final product yield, x 5 t finalincreases with t reg until the optimal value is reached, then it starts decreasing. Result of the optimization using the Inner Optimization with Geometric Programming left and Linear Programming right.

The profiles of the production of x 5 remain similar to the simulation optiization Direct optimization. As shown mathematically in the methods section, the optimal control function is either 0 or 1, provided that the dynamics depends linearly on the control and the cost to optimize has only a final term.

In this case the dependency of the Hamiltonian function on u is linear as given by 8 below. Figure 5 shows a plot of ϕ λ tx t obtained with a near-optimal control function u t.

Thus, the optimal control is obtained on the extremes of the allowed interval and furthermore, one single switch from 0 to 1 is enough to achieve the optimal control.

This function changes sign at the optimal instant of control switching T reg. For a class of networks in which the yield of the product that favors cell population growth the "natural" product competes with the desired product yield, with the manipulated variable affecting linearly the fluxes, it has been shown that the optimal control assumes only extreme values.

While the implementation of this optimal control poses no challenge on in silico Metaholic networks, on real metabolic networks complex bioengineering skills are required. Functin knockout manipulations do not adequate to this kind of control problem due to the long time scale associated with these techniques.

The manipulation of specific enzyme levels, controlled by modulating the expression of the corresponding genes using promoter systems and inducers, is a possible solution to this kind of control problem [ 14 ]. The use of a bi-level optimization strategy, that maximizes the natural product in the inner level by manipulating the optimizzation, leads to a good approximation to the optimal solution, with the advantage of not requiring the full knowledge of the network model.

Real networks are extremely complex and exhibit relations between metabolites that are not always expected or fully understood.

This gives emphasis to the need of good in silico models and also to the determination of the exact branches to be modified when optimizing a network.

: Metabolic function optimizationOPTIMIZING YOUR METABOLISM | The significance of this result consists in the fact that, instead of searching the optimal control among piecewise continuous functions assuming values between 0 and 1, one only has to look functions assuming the extreme values of 0 and 1. Furthermore, in the case study considered, it is shown that the optimum has only one switch between 0 and 1. Therefore the search for the optimum is reduced to find the switching instant, t reg , that leads to the maximum final yield. Considering the structure of the metabolic network, this is intuitive: the optimum is achieved by first applying all cell resources to population growth and, after t reg , to redirect them to desired production. If t reg is too small, the desired production rate is higher during more time, but the cell population to which it applies is small. If t reg is too big, there are many cells to produce, but they only act during a small time interval. Hence, there is an optimum value for t reg. As mentioned in the Background section, a major problem is the high level of uncertainty in the knowledge about metabolic network dynamics. In this respect we consider different optimization algorithms that assume various degrees of information about the system to be optimized. The first is direct optimization. This assumes complete knowledge about the system and is included to establish a benchmark with which other methods may be compared. The other two methods are variants of a bi-level algorithm designed in order to accommodate missing information on the network kinetics. Both cases differ from the type of inner-optimization: Geometric Programming in one case and Linear Programming in the other. Both methods lead to good approximations of the optimal control, with a slight advantage of the one relying on Geometric Programming. The optimization strategies were tested on a prototype network that is a modified version of a previously one suggested in [ 16 ]. The choice of this network was due to its widespread use as a test benchmark for several optimization algorithms. A graphical representation of the network is shown in Figure 1 associated with the following set of ordinary differential equations:. Prototype network. The circles correspond to metabolites and the arrows to fluxes with the reaction rates indicated. Assuming that x 3 represents a precursor of the cellular objective such as growth and x 5 the desired product, if u t is biased towards the branch of v 2 this yields the formation of x 3 but little or no production of x 5. If u t is biased towards the branch of v 3 the production of x 5 will be affected by the low concentration of x 3 since there is a forward feedback. Thus, there is an optimal profile for u t to maximize the concentration of x 5 at the final time t final. In the framework of S-systems [ 16 ] the prototype network is described by:. where β i are the rate constants, g ij and h ij are the kinetic orders. Table 1 shows the list of parameters. Direct optimization uses model 2 with the set of parameters from Table 1. Results of the simulation using Direct optimization. The final product concentration is shown as a function of T reg. For the value of T reg corresponding to the dotted line there is a maximum yield. It is clear from Figure 2 that there is an optimal value for the time of regulation that maximizes the yield of x 5. If u t switches from 0 to 1 before t reg the formed biomass will not be enough to maximize x 5 t final. On the other hand, if u t switches from 0 to 1 after t reg , there will be more biomass but there time will not be enough time to produce the maximum possible amount of x 5. Comparison of three u t profiles. Three time profiles for the control function u t above and the corresponding product yield below. The solid line is the optimal T reg obtained by Direct Optimization. The Bi-Level optimization was used to test all the possible values of t reg. By comparing Figure 4 with Figure 2 it can be seen that the profiles remain similar. The final product yield, x 5 t final , increases with t reg until the optimal value is reached, then it starts decreasing. Result of the optimization using the Inner Optimization with Geometric Programming left and Linear Programming right. The profiles of the production of x 5 remain similar to the simulation using Direct optimization. As shown mathematically in the methods section, the optimal control function is either 0 or 1, provided that the dynamics depends linearly on the control and the cost to optimize has only a final term. In this case the dependency of the Hamiltonian function on u is linear as given by 8 below. Figure 5 shows a plot of ϕ λ t , x t obtained with a near-optimal control function u t. Thus, the optimal control is obtained on the extremes of the allowed interval and furthermore, one single switch from 0 to 1 is enough to achieve the optimal control. This function changes sign at the optimal instant of control switching T reg. For a class of networks in which the yield of the product that favors cell population growth the "natural" product competes with the desired product yield, with the manipulated variable affecting linearly the fluxes, it has been shown that the optimal control assumes only extreme values. While the implementation of this optimal control poses no challenge on in silico metabolic networks, on real metabolic networks complex bioengineering skills are required. Gene knockout manipulations do not adequate to this kind of control problem due to the long time scale associated with these techniques. The manipulation of specific enzyme levels, controlled by modulating the expression of the corresponding genes using promoter systems and inducers, is a possible solution to this kind of control problem [ 14 ]. The use of a bi-level optimization strategy, that maximizes the natural product in the inner level by manipulating the fluxes, leads to a good approximation to the optimal solution, with the advantage of not requiring the full knowledge of the network model. Real networks are extremely complex and exhibit relations between metabolites that are not always expected or fully understood. This gives emphasis to the need of good in silico models and also to the determination of the exact branches to be modified when optimizing a network. Although the example network used is very simple, it has proved to be useful to test the optimization strategies but a more complex network should be used to confirm that the strategy can be scaled to a larger network. The solution of the optimization problem is obtained using different approaches. Before accomplishing this task, Pontryagin's Maximum Principle is invoked to establish a particular form of the optimal control function for the class of problems at hand. The control function is now optimized in order to obtain a maximum yield of biomass at the end of the run-time t final. Three different methods, assuming various levels of information about the network, are considered in order to attain this goal. The first method, direct optimization, is used as a benchmark to compare the results of the other methods. The last two methods rely on a Bi-level optimization and illustrate a possible solution to the optimization problem when the information about the network is incomplete. The first method, Direct Optimization, is used mainly as a benchmark, to compare the results of the following methods. Since it is assumed that all the information about the network kinetics is known, the system of differential equations, described in 2 is used. The value of t reg that results on a maximum product yield is then determined by solving a simple optimization problem. The optimization was tested with two MATLAB functions: fmincon , from the standard optimization toolbox, that finds the minimum of a constrained nonlinear multi variable function, and simannealingSB from Systems Biology Toolbox [ 17 ] that performs simulated annealing optimization. The Bi-Level optimization algorithm was structured so as to accommodate missing information on the network kinetics. The boxed metabolites and fluxes from Figure 1 are a part of the network that might not be fully described in terms of kinetics. In this approach the missing kinetic information is replaced by stoichiometric data and flux balance analysis is used to obtain the proper flux distribution. Then, an inner optimization determines the fluxes during the batch time. The first step of the inner optimization process is to define the initial conditions of the input x 1 and outputs x 3 , x 5. A valid distribution for the fluxes v 1 , v 2 , v 3 and v 4 is then obtained. During this time interval the function u t and the values of v 1 , v 2 , v 3 and v 4 are kept constant. The time interval for the integration was defined to be 1 second. The inner optimization process allows us to obtain the product yield, x 5 t final , given a certain u t , taking into account a valid approximation of the network dynamics over the simulation time. The detailed fluxogram of the inner-optimization is shown in Figure 6. The bi-level optimization algorithm can be represented schematically as in Figure 7. On the first implementation of the Bi-Level optimization algorithm the dynamics of the boxed metabolites from Figure 1 are used but, following the algorithm structure, steady-state is assumed. Thus, x ˙ 2 and x ˙ 4 from 2 become:. In this algorithm implementation, the inner optimization problem determines the profile of the metabolites, instead of fluxes, due to the nature of the equations. The metabolite concentrations are calculated at the beginning of each time interval, solving a Geometric Programming problem, and used with 2 to integrate the values of x 1 , x 3 and x 5 during that interval. Some most even gain the weight back and try to go back to the same type of restriction again and again. Instead, we store fat, energy slows down, and our body conserves fat. This is why eating enough calories consistently should be the first goal of any effective, long term nutrition plan. Instead of going to extremes with calorie restriction and putting the mental and physical stress on your body, you can figure out what it truly needs to feel nourished and safe. This will support all the other hormones that can go haywire — like thyroid hormones, cortisol, sex hormones, hunger hormones — so that if you want to lose weight it feels easy and enjoyable. We go through how to find your unique calorie needs in Metabolism Renewal. What you eat is also important. This is why I teach quality first in the form of nutrient dense, real food. From there, you can customize your calorie needs based on your current metabolic status, past dieting history, age, weight, height, and activity levels. Meal composition and meal timing can make or break your blood sugar stability. Finding the right balance of protein, fat, and carbs is the first step. From there, try to eat every hours with nutrient-dense foods. This means not snacking on carrot sticks or just running out the door with a granola bar. Eggs, grass fed beef, fish, poultry, grass fed dairy, and organ meats are all great sources. This is a really effective way to stimulate the metabolism without increasing stress in the body. Scale back on structured and intense cardio and bump up natural, relaxing movement. Movement throughout the day is better for your metabolism than working out hard for an hour then sitting all day. We understand that everyone is unique, with specific needs and lifestyle factors. After all, true beauty stems from within. Embrace this innovative approach to wellness, crafted to help you look and feel your best. Because you deserve nothing less. Ready to embark on this transformative journey? Schedule a consultation with us and explore the powerful combination of our Metabolic Optimization Program and our superior cosmetic and dental procedures. By addressing any potential imbalances or inefficiencies in your metabolism, our goal is to enhance your overall well-being, which, in turn, positively reflects on your skin and smile. 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5 Steps to Optimize Your Metabolism | HCG helps support the growth of a fetus and maintain hormone levels, including progesterone. This hormone also acts as a metabolism booster, promoting weight loss and curbing excess hunger. Health optimization providers can administer HCG to help patients increase their metabolism and lose weight. There are many ways to administer HCG. This is because specific detox diets can remove harmful toxins and substances from the blood that lead to extreme fatigue, weight gain, and low metabolism when at high levels. Some health optimization providers will combine detoxification diets with peptide therapy to increase metabolism and energy levels. Peptides are chains of amino acids utilized in various body functions. One of these is metabolic function. When peptides are administered to the body along with a detoxification diet, it provides ultimate metabolic optimization. Sometimes prescription medication is needed to restore your metabolic rate. We can prescribe custom medications that increase your metabolism, suppress your appetite, and increase energy levels. These medications are ideal for patients who have been struggling to lose weight with diet and exercise alone, which is often an indicator of metabolism that may have slowed naturally with age. Individuals with slow metabolisms are good candidates for metabolic optimization. It is worth noting that sometimes a slow metabolism manifests in other less common ways. All of the following are symptoms of a low metabolism:. To determine whether you would benefit from one of the various metabolic optimization treatments mentioned above, you will need to have a consultation with a medical professional. They will assess your health concerns and current body composition and perform a dietary evaluation. All of this helps providers determine whether you are a good candidate for the metabolic optimization process. Heightened Physical Performance: Optimized metabolism enhances energy conversion during exercise, fostering endurance, muscle strength, and efficient recovery. Cognitive Enhancement: Metabolic optimization supports brain health via optimized nutrient delivery, enhancing mental acuity, focus, and memory. Stable Blood Sugar Control: Precise metabolic regulation curbs blood glucose fluctuations and combats insulin resistance, mitigating diabetes risk and related complications. Cellular Vitality and Longevity: By reducing oxidative stress and enhancing mitochondrial efficiency, metabolic optimization is linked to cellular longevity. Holistic Wellness: Metabolic optimization fosters improved sleep, stress response, and overall vitality, nurturing comprehensive well-being. Metabolic Optimization In Las Vegas. Request More Info. Achieve Your Health Goals with the Right Metabolic Optimization Program. This multi-objective optimization includes tailored adjustments to factors like nutrition, exercise routine, and lifestyle, helping you achieve higher energy levels, weight management, and overall health improvement. With a focus on personalized care and evidence-based approaches, Elite Medical Associates empowers you to achieve your health goals and unlock your full potential with comprehensive services:. I stared with the HRT first, with Dr. Lovett telling me what to expect within the first weeks of treatment. After one months time I signed up for the weight management with Alyssa Kessel and she guided me through the process of losing 61 pounds! I went from to in 9 weeks time. Now she is guiding me through the process of my weight maintenance as this is definitely a life style change for me. I can not thank Your Wellness Center, Dr. Lovett and staff enough for helping me get my life back and on the road to a better me! LOVE THIS PLACE!!!!!! If you are looking for a natural solution to feeling and looking better, we believe we can help and would love to take care of you. The first step is to set up a consultation with us — click the button below to get started. Start Today. Call Today! New Year's Resolutions: Setting Realistic Weight Loss Goals January 4, Losing weight is already a challenging process, but even more so if you set unrealistic goals that are too difficult or even impossible to achieve Start Today To get started, click the link above to fill out our consult form. My wellness journey started with a visit to Dr. Lovitt, he is amazing! Rated 5. Schedule an Appointment If you are looking for a natural solution to feeling and looking better, we believe we can help and would love to take care of you. Schedule an Appointment ×. Hormone Therapy Thyroid Therapy Weight Loss IV Therapy Sexual Medicine Food Sensitivity. |

How Does Metabolic Optimization Work? El Paso Cellulite Center | coli metabolic models [12] Antioxidant-rich snacks [14] Metabollc on flux variability opitmization [9]. To obtain the best experience, we recommend you use a Metabolic function optimization up Promoting gut health in children date browser or turn off compatibility mode in Internet Explorer. Engineering microbial consortia: a new frontier in synthetic biology. Interestingly, we find that the resulting number of active reactions in optimal states is fairly constant across the four organisms analyzed, despite the significant differences in their biochemistry and in the number of available reactions. Klein, M. |

### Metabolic function optimization -

Metabolic Optimization In Las Vegas. Request More Info. Achieve Your Health Goals with the Right Metabolic Optimization Program. This multi-objective optimization includes tailored adjustments to factors like nutrition, exercise routine, and lifestyle, helping you achieve higher energy levels, weight management, and overall health improvement.

With a focus on personalized care and evidence-based approaches, Elite Medical Associates empowers you to achieve your health goals and unlock your full potential with comprehensive services:. Gain a deep understanding of your body composition and energy expenditure through our comprehensive metabolic assessments.

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It Calculating BMI widely accepted that some Mtabolic function has been optimized during the evolution Metaolic metabolic MMetabolic One can study the Fhnction of such a function by analogy with the industrial manufacturing world, in which there Herbal Tea Blends been Antioxidant-rich snacks over Metabolic function optimization optijization to optimize production chains, and in which it is now accepted Metaboliv fluxes are not the only Metabolic function optimization system variables that determine process efficiency, because inventory turnover must also be considered. Inspired by the parallels between living cells and manufacturing factories, we propose that fluxes and transit time may have simultaneously been major targets of natural selection in the optimization of the design, structure and kinetic parameters of metabolic pathways. Accordingly we define the ratio of flux to transit time as a performance index of productivity in metabolic systems: it measures the efficiency with which stocks are administered, and facilitates comparison of a pathway in different steady states or in different tissues or organisms. For a linear chain of two enzymes, at a fixed total equilibrium constant, we have analysed the variation of flux, transit time and productivity index as functions of the equilibrium constants of the two steps.
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