There is overwhelming yet underappreciated evidence for the ability of certain medicines to prevent all-cause mortality. A reduction in all-cause mortality is strong evidence that these pharmaceuticals can intervene in the aging process at a cellular level.

We will discuss the potential benefits if your blood work warrants using these agents. No plan works without execution and accountability but into the process.

When you need it, a follow-up coaching call will be built in at and then 60—day windows to check the status. Repeat labs and M. visit again every 90 days will move you forward with reviewing your lab and biometric scores to chart progress. Changes may be made to accommodate your specific needs. The MOP program was developed to give people the power to control their health.

Many feel the health care system has failed them with little time spent with their busy physician, insufficient blood work, high cost, and long waits for scheduling appointment visits. The MOP program allows you to see and understand your health on a new level by providing insights into your body that are not typically offered in a traditional healthcare setting while eliminating the high costs and challenges of conventional healthcare.

Through your local Quest Lab easy blood draws, a streamlined virtual home platform, best-in-class supplements, and medications sent to your doorstep; the program will easily integrate into your schedule. Then Repeat Labs and Reenlist and we suggest looking at labs quarterly for a year to verify trends and improvement.

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Metabolic Optimization Protocol MOP. How is MOP different? Why is bloodwork done so frequently in the MOP program? What is the blood work performed? We look at many laboratory markers, but here are some examples:. HOMA-IR: Homeostatic Model Assessment for Insulin Resistance HOMA-IR can give a very early signal that your metabolism is becoming less efficient.

It detects insulin resistance, a condition that is a causative factor in almost every chronic illness. 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. Call Revitta to schedule your appointment or for more information: or click here to contact us.

Call us today Book your appointment. Follow us on. Association of methanogenic bacteria with rumen ciliates. Lee, M. Association of methanogenic bacteria with flagellated protozoa from a termite hindgut. Article Google Scholar. Gonzalez-Gil, G. Cluster structure of anaerobic aggregates of an expanded granular sludge bed reactor.

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Boone, D. Propionate-degrading bacterium, Syntrophobacter wolinii sp. Dwyer, D. Bioenergetic conditions of butyrate metabolism by a syntrophic, anaerobic bacterium in coculture with hydrogen-oxidizing methanogenic and sulfidogenic bacteria. Raven, J. The evolution of inorganic carbon concentrating mechanisms in photosynthesis.

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Direct exchange of electrons within aggregates of an evolved syntrophic coculture of anaerobic bacteria. Science , — Shapiro, J.

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Eukaryotic evolution, changes and challenges. Howe, C. The origin of plastids. B , — van der Klei, I. The significance of peroxisomes in methanol metabolism in methylotrophic yeast. Acta , — Yurimoto, H.

Yeast methylotrophy: metabolism, gene regulation and peroxisome homeostasis. Biosynthesis and assembly of alcohol oxidase, a peroxisomal matrix protein in methylotrophic yeasts: a review.

Yeast 7 , — Ozimek, P. Alcohol oxidase: a complex peroxisomal, oligomeric flavoprotein. FEMS Yeast Res.

Vonck, J. Architecture of peroxisomal alcohol oxidase crystals from the methylotrophic yeast Hansenula polymorpha as deduced by electron microscopy. Roggenkamp, R. Targeting signals for protein import into peroxisomes. Cell Biochem. Bayer, T. Synthesis of methyl halides from biomass using engineered microbes.

Lin, J. An effective strategy for the co-production of S -adenosyl- L -methionine and glutathione by fed-batch fermentation.

Agapakis, C. Towards a synthetic chloroplast. PLoS ONE 6 , e Savage, D. Spatially ordered dynamics of the bacterial carbon fixation machinery. Iancu, C. The structure of isolated Synechococcus strain WH carboxysomes as revealed by electron cryotomography.

So, A. A novel evolutionary lineage of carbonic anhydrase e class is a component of the carboxysome shell. Price, G. Expression of human carbonic anhydrase in the cyanobacterium Synechococcus PCC creates a high CO2-requiring phenotype: evidence for a central role for carboxysomes in the CO2 concentrating mechanism.

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Effective Weight Management : Addressing daily energy expenditure and storage, metabolic optimization combats excess weight and offers sustainable weight control beyond conventional methods. 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.

When your metabolic rate slows down, it fails to convert your calories into the energy you need. This leads to sluggishness, weight gain, and a long list of other potential symptoms.

Cyrus Advanced Institute provides metabolic optimization. Metabolic optimization aims to help patients increase their metabolism and lose weight more effectively. We can work with you through treatments like HCG therapy, detoxification diets, and customized medications to improve your health and reach your ideal body weight.

Read on to learn more about how metabolic optimization works. Metabolism refers to the chemical processes that turn your food into the energy you need to get through your day.

Metabolic optimization sometimes known as metabolic pathway optimization aims to boost metabolism. Metabolic optimization treatments provide several benefits for patients.

This includes but is not limited to the following:. Patients struggling with low metabolism burn fewer calories during rest and often feel sluggish and weighed down. Providers may approach metabolic optimization in several different ways,but Cyrus Advanced Institute offer various treatment methods that can increase metabolism i.

Learn more about each of these treatments here:. Human chorionic gonadotropin HCG is a hormone found in early pregnancy and used in pregnancy tests. 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. The most efficient pathway, P1, is active under maximum growth in glucose minimal medium. P2 and P3 are inactive, but if P1 is knocked out, P2 becomes active, and if both P1 and P2 are knocked out, P3 becomes active.

In both knockout scenarios, the growth is predicted to be suboptimal. B Isocitrate lyase reaction in the pathway bypassing the tricarboxylic acid TCA cycle is predicted to be inactive under maximum growth due to its irreversibility. If it were to operate in the opposite direction, it would serve as a transverse pathway which redirects metabolic flow to growth-limiting reactions, increasing the maximum biomass production rate slightly.

In both panels, single and double arrows are used to indicate irreversible and reversible reactions, respectively, and colors indicate the behavior of the reactions under maximum growth: active red , inactive due to the irreversibility green , and inactive due to cascading yellow.

A different silencing scenario is identified when no clear parallel pathway structure is recognizable. This includes transverse reactions that establish one-way communication between pathways that lead to different building blocks of the biomass when one of them is more limiting than the others.

In the E. coli model, for example, isocitrate lyase in the glyoxylate bypass is predicted to be inactive under maximum growth, as shown in Figure 3B. This prediction is consistent with the observation from growth experiments in glucose media [17]. Again, the irreversibility of the reaction Note 2 is essential for this argument because, if this constraint is hypothetically relaxed, we predict that the reaction becomes active in the opposite direction, which leads to a slight increase in the maximum growth rate about 0.

A third scenario for the irreversibility mechanism arises when a transport reaction is irreversible because the corresponding substrate is absent in the medium.

For example, since acetate, a possible carbon and energy source, is absent in the given medium, the corresponding transport reaction is irreversible; acetate can only go out of the cell Note 3.

For E. coli under maximum growth, we computationally predict that this transport reaction is inactive. This indicates that E. coli growing maximally in the given glucose medium wastes no acetate by excretion, which is consistent with experimental observation in glucose-limited culture at low dilution rate [36].

Our predictions in the previous section, in contrast, imply that acetate transport would be active in typical non-optimal states, suggesting that suboptimal growth may induce behavior that mimics acetate overflow metabolism. More generally, we predict that a suboptimal cell will activate more transport reactions, and hence excrete larger number of metabolites than a growth-optimized cell.

This experimentally testable prediction can be further supported by our single-reaction knockout computations considered in a subsequent section Experimental Evidence to model suboptimal response to perturbation.

We interpret these inactivation mechanisms involving reaction irreversibility as a consequence of the linear programming property that the set of optimal solutions M opt must be part of the boundary of M [37].

As such, M opt is characterized by a set of binding constraints, defined as inequality constraints e. In two dimensions, for example, M opt would be an edge of M , characterized by a single binding constraint, or a corner of M , characterized by two binding constraints.

In general, at least d — d opt linearly independent constraints must be binding, where d and d opt are the dimensions of M and M opt , respectively. On one hand, if all the reactions that produce a metabolite are inactive, any reaction that consumes this metabolite must be inactive.

On the other hand, if all the reactions that consume a metabolite are inactive, any reaction that produces this metabolite must be inactive to avoid accumulation, as this would violate the steady-state assumption. Therefore, the inactivity caused by the irreversibility mechanism triggers a cascade of inactivity both in the forward and backward directions along the metabolic network.

In general, there are many different sets of reactions that, when inactivated, can create the same cascading effect, thus providing flexibility in potential applications of this effect to the design of optimal strains [25]. The cascades in the growth-maximizing states, however, are spontaneous, as opposed to those that would be induced in metabolic knockout applications [25] or in reaction essentiality and robustness studies [38] — [40].

Different but related to the cascades of inactivity are the concepts of enzyme subsets [41] , coupled reaction sets [26] and correlated reaction sets [10] , which describe groups of reactions that operate together and are thus concurrently inactivated in cascades. To explore this dependence, we use the duality principle of linear programming problems [37] to identify all the binding constraints generating the set of optimal solutions M opt Text S1 , Section 3.

Note that the upper bounds are consistently smaller than for typical non-optimal states, indicating that reaction silencing necessarily occurs for all growth-maximizing states.

coli , these results are consistent with a previous study comparing reaction utilization under a range of different growth conditions [10].

They are also consistent with existing results for different E. coli metabolic models [12] — [14] based on flux variability analysis [9]. This behavior is expected, however, under the concurrent optimization of additional metabolic objectives, which generally tend to drive the flux distribution toward the boundary of M opt.

Figure 2 summarizes the inactivity mechanisms for the four organisms under maximum growth in glucose media see also Figure 1 , showing the inactive reactions caused by the irreversibility green and cascading yellow mechanisms, as well as those that are conditionally inactive orange.

Observe that in sharp contrast to the number of active reactions, which depends little on the size of the network, the number of inactive reactions either separated by mechanisms or lumped together varies widely and shows non-trivial dependence on the organisms.

Although we have focused so far on maximizing the biomass production rate, the true nature of the fitness function driving evolution is far from clear [44] — [47].

Organisms under different conditions may optimize different objective functions, such as ATP production or nutrient uptake, or not optimize a simple function at all. In particular, some metabolic behaviors, such as the selection between respiration and fermentation in yeast, cannot be explained by growth maximization [48].

Other behaviors may be systematically different in situations mimicking natural environments [49]. Moreover, various alternative target objectives can be conceived and used in metabolic engineering applications.

We emphasize, however, that while specific numbers may differ in each case, all the arguments leading to Eqs. To obtain further insights, we now study the number of active reactions in organisms optimizing a typical linear objective function by means of random uniform sampling.

We find that the number of active reactions in typical optimal states is narrowly distributed around that in growth-maximizing states, as shown in Figure 4.

This implies that various results on growth maximization extend to the optimization of typical objective functions.

In particular, we see that a typical optimal state is surprisingly close to the onset of cellular growth estimated and shown as dashed vertical lines in Figure 4. Despite the closeness, however, the organism maintains high versatility , which we define as the number of distinct functions that can be optimized under growth conditions.

To demonstrate this, consider the H. pylori model, which has reactions that can be active, among which at least must be active to sustain growth Table 3.

While only a few more than active reactions are sufficient to optimize any objective function, the number of combinations for choosing them can be quite large.

For example, there are combinations for choosing, say, 5 extra reactions to be active. Moreover, this number increases quickly with the network size: S. cerevisiae , for example, has less than 2. pylori , but about times more combinations. The red solid lines indicate the corresponding number in the growth-maximizing state of Figure 2 middle bar, red , and the red dashed lines indicate our estimates of the minimum number of reactions required for the organism to grow Materials and Methods.

Our results help explain previous experimental observations. Analyzing the 22 intracellular fluxes determined by Schmidt et al. For the 44 fluxes in the S.

cerevisiae metabolism experimentally measured by Daran-Lapujade et al. This higher probability for reduced fluxes in irreversible reactions is consistent with our theory and simulation results Table 6 combined with the assumption that the system operates close to an optimal state.

For the E. The S. cerevisiae data was also found to be consistent with the fluxes computed under the assumption of maximum growth [52]. Additional evidence for our results is derived from the inspection of 18 intracellular fluxes experimentally determined by Emmerling et al.

coli and a gene-deficient strain not exposed to adaptive evolution. It has been shown [21] that while the wild-type fluxes can be approximately described by the optimization of biomass production, the genetically perturbed strain operates sub-optimally.

This correlation indicates that irreversible fluxes tend to be larger in non-optimal metabolic states, consistently with our predictions. Altogether, our results offer an explanation for the temporary activation of latent pathways observed in adaptive evolution experiments after environmental [16] or genetic perturbations [17].

These initially inactive pathways are transiently activated after a perturbation, but subsequently inactivated again after adaptive evolution. We hypothesize that transient suboptimal states are the leading cause of latent pathway activation. Since we predict that a large number of reactions are inactive in optimal states but active in typical non-optimal states, many reactions are expected to show temporary activation if we assume that the state following the perturbation is suboptimal and both the pre-perturbation and post-adaptation states are near optimality.

To demonstrate this computationally for the E. coli model, we consider the idealized scenario where the perturbation to the growth-maximizing wild type is caused by a reaction knockout and the initial response of the metabolic network—before the perturbed strain evolves to a new growth-maximizing state—is well approximated by the method of minimization of metabolic adjustment MOMA [21].

MOMA assumes that the perturbed organisms minimize the square-sum deviation of its flux distribution from the wild-type distribution under the constraints imposed by the perturbation.

Figure 5A shows the distribution of the number of active reactions for single-reaction knockouts that alter the flux distribution but allow positive MOMA-predicted growth. While the distribution is spread around — for the suboptimal states in the initial response, it is sharply peaked around for the optimal states at the endpoint of the evolution, which is consistent with our results on random sampling of the extreme points Figure 4.

We thus predict that the initial number of active reactions for the unperturbed wild-type strain which is , as shown by a dashed vertical line in Figure 5A typically increases to more than following the perturbation, and then decays back to a number close to after adaptive evolution in the given environment, as illustrated schematically in Figure 5B.

A neat implication of our analysis is that the active reactions in the early post-perturbation state includes much more than just a superposition of the reactions that are active in the pre- and post-perturbation optimal states, thus explaining the pronounced burst in gene expression changes observed to accompany media changes and gene knockouts [16] , [17].

For example, for E. coli in glucose minimal medium, temporary activation is predicted for the Entner-Doudoroff pathway after pgi knockout and for the glyoxylate bypass after tpi knockout, in agreement with recent flux measurements in adaptive evolution experiments [17].

A The initial response is predicted by the minimization of metabolic adjustment MOMA and the endpoint of adaptive evolution by the maximization of the growth rate FBA , using the medium defined in Materials and Methods and a commercial optimization software package [79].

We consider all 77 nonlethal single-reaction knockouts that change the flux distribution. B Schematic illustration of the network reaction activity during the adaptive evolution after a small perturbation, indicating the temporary activation of many latent pathways.

Another potential application of our results is to explain previous experimental evidence that antagonistic pleiotropy is important in adaptive evolution [54] , as they indicate that increasing fitness in a single environment requires inactivation of many reactions through regulation and mutation of associated genes, which is likely to cause a decrease of fitness in some other environments [15].

Combining computational and analytical means, we have uncovered the microscopic mechanisms giving rise to the phenomenon of spontaneous reaction silencing in single-cell organisms, in which optimization of a single metabolic objective, whether growth or any other, significantly reduces the number of active reactions to a number that appears to be quite insensitive to the size of the entire network.

Two mechanisms have been identified for the large-scale metabolic inactivation: reaction irreversibility and cascade of inactivity. In particular, the reaction irreversibility inactivates a pathway when the objective function could be enhanced by hypothetically reversing the metabolic flow through that pathway.

We have demonstrated that such pathways can be found among non-equivalent parallel pathways, transverse pathways connecting structures that lead to the synthesis of different biomass components, and pathways leading to metabolite excretion.

Although the irreversibility and cascading mechanisms do not require explicit maximization of efficiency, massive reaction silencing is also expected for organisms optimizing biomass yield or other linear functions of metabolic fluxes normalized by uptake rates [18].

Furthermore, while we have focused on minimal media, we expect the effect to be even more pronounced in richer media. On one hand, a richer medium has fewer absent substrates, which increases the number of active reactions in non-optimal states.

On the other hand, a richer medium allows the organism to utilize more efficient pathways that would not be available in a minimal medium, possibly further reducing the number of active reactions in optimal states. Our study carries implications for both natural and engineered processes.

In the rational design of microbial enhancement, for example, one seeks genetic modifications that can optimize the production of specific metabolic compounds, which is a special case of the optimization problem we consider here and akin to the problem of identifying optimal reaction activity [24] , [25].

The identification of a reduced set of active reactions also provides a new approach for testing the existence of global metabolic objectives and their consistency with hypothesized objective functions [46]. Such an approach is complementary to current approaches based on coefficients of importance [44] , [45] or putative objective reactions [47] and is expected to provide novel insights into goal-seeking dynamic concepts such as cybernetic modeling [55].

Our study may also help model compromises between competing goals, such as growth and robustness against environmental or genetic changes [56].

In particular, our results open a new avenue for addressing the origin of mutational robustness [57] — [62]. Single-gene deletion experiments on E. coli and S. cerevisiae have shown that only a small fraction of their genes are essential for growth under standard laboratory conditions [1] , [5] , [6].

The number of essential genes can be even smaller given that growth defect caused by a gene deletion may be synthetically rescued by compensatory gene deletions [25] , an effect not accounted for in single-gene deletion experiments. Under fixed environmental conditions, large part of this mutational robustness arises from the reactions that are inactive under maximum growth, whose deletion is predicted to have no effect on the growth rate [52].

coli in glucose medium, we predict that out of the reactions can be removed simultaneously while retaining the maximum growth rate Note 4 , which is comparable to computed in a minimal genome study in rich media [11].

But what is the origin of all these non-essential genes? A recent study on S. cerevisiae has shown that the single deletion of almost any non-essential gene leads to a growth defect in at least one stress condition [15] , providing substantive support for the long-standing hypothesis that mutational robustness is a byproduct of environmental robustness [61] at least if we assume that the tested conditions are representative of the natural conditions under which the organisms evolved.

An alternative explanation would be that in variable environments, which is a natural selective pressure likely to be more important than considered in standard laboratory experiments, the apparently dispensable pathways may play a significant role in suboptimal states induced by environmental changes.

This alternative is based on the hypothesis that latent pathways provide intermediate states necessary to facilitate adaptation, therefore conferring competitive advantage even if the pathways are not active in any single fixed environmental condition.

In light of our results, this hypothesis can be tested experimentally in medium-perturbation assays by measuring the change in growth or other phenotype caused by deleting the predicted latent pathways in advance to a medium change.

We conclude by calling attention to a limitation and strength of our results, which have been obtained using steady-state analysis. Such analysis avoids complications introduced by unknown regulatory and kinetic parameters, but admittedly does not account for constraints that could be introduced by the latter.

Nevertheless, we have been able to draw robust conclusions about dynamical behaviors, such as the impact of perturbation and adaptive evolution on reaction activity. Our methodology scales well for genome-wide studies and may prove instrumental for the identification of specific extreme pathways [63] , [64] or elementary modes [65] , [66] governing the optimization of metabolic objectives.

Combined with recent studies on complex networks [67] — [73] and the concept of functional modularity [74] , our results are likely to lead to new understanding of the interplay between network activity and biological function.

For H. For S. The flux through the ATP maintenance reaction was set to 7. aureus and S. Under steady-state conditions, a cellular metabolic state is represented by a solution of a homogeneous linear equation describing the mass balance condition, 6 where S is the m × N stoichiometric matrix and is the vector of metabolic fluxes.

Assuming that the cell's operation is mainly limited by the availability of substrates in the medium, we impose no other constraints on the internal reaction fluxes, except for the ATP maintenance flux for S. cerevisiae see Strains and media section above.

The set of all flux vectors v satisfying the above constraints defines the feasible solution space , representing the capability of the metabolic network as a system. For a given objective function, we numerically determine an optimal flux distribution for this problem using an implementation of the simplex method [43].

In the particular case of growth maximization, the objective vector c is taken to be parallel to the biomass flux, which is modeled as an effective reaction that converts metabolites into biomass. To find a set of reactions from which none can be removed without forcing zero growth, we start with the set of all reactions and recursively reduce it until no further reduction is possible.

At each recursive step, we first compute how much the maximum growth rate would be reduced if each reaction were removed from the set individually. Then, we choose a reaction that causes the least change in the maximum growth rate, and remove it from the set.

We repeat this step until the maximum growth rate becomes zero. The set of reactions we have just before we remove the last reaction is a desired minimal reaction set. Note that, since the algorithm is not exhaustive, the number of reactions in this set is an upper bound and approximation for the minimum number of reactions required to sustain positive growth.

The authors thank Linda J. Broadbelt for valuable discussions and for providing feedback on the manuscript.

The authors also thank Jennifer L. Reed and Adam M. Feist for providing information on their in silico models. Conceived and designed the experiments: AEM.