Incorporation of enzyme concentrations into FBA and identification of optimal metabolic pathways

De, Rajat K.; Das, Mouli; Mukhopadhyay, Soumik

doi:10.1186/1752-0509-2-65

Cited by 14 publications

(13 citation statements)

References 41 publications

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“…Glycerate-3P $ Glycerate-2P $ Phosphoenolpyruvate. This is the same path found for T. pallidum [7]. This can have several explanations.…”

Section: Metabolic Engineering Of Glycolysis/ Gluconeogenesis Pathwaysupporting

confidence: 57%

“…This problem is similar to a special case of the enzyme target identification problem considered in this paper. De et al, for example, consider the extreme pathway analysis problem [7]. In order to reduce the yield of a compound in a pathway, De et al use FBA to compute the optimal pathway so that the yield of the target metabolite is minimum.…”

Section: Limitations Of Randomized Methodsmentioning

confidence: 99%

“…De et al [7] studied the path that maximizes the production of this compound. T.pallidum, however, has a simple pathway that contains only four enzymes.…”

Section: Metabolic Engineering Of Glycolysis/ Gluconeogenesis Pathwaymentioning

confidence: 99%

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Manipulating the Steady State of Metabolic Pathways

Song

Büyüktahtakın

Ranka

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Metabolic pathways show the complex interactions among enzymes that transform chemical compounds. The state of a metabolic pathway can be expressed as a vector, which denotes the yield of the compounds or the flux in that pathway at a given time. The steady state is a state that remains unchanged over time. Altering the state of the metabolism is very important for many applications such as biomedicine, biofuels, food industry, and cosmetics. The goal of the enzymatic target identification problem is to identify the set of enzymes whose knockouts lead the metabolism to a state that is close to a given goal state. Given that the size of the search space is exponential in the number of enzymes, the target identification problem is very computationally intensive. We develop efficient algorithms to solve the enzymatic target identification problem in this paper. Unlike existing algorithms, our method works for a broad set of metabolic network models. We measure the effect of the knockouts of a set of enzymes as a function of the deviation of the steady state of the pathway after their knockouts from the goal state. We develop two algorithms to find the enzyme set with minimal deviation from the goal state. The first one is a traversal approach that explores possible solutions in a systematic way using a branch and bound method. The second one uses genetic algorithms to derive good solutions from a set of alternative solutions iteratively. Unlike the former one, this one can run for very large pathways. Our experiments show that our algorithms' results follow those obtained in vitro in the literature from a number of applications. They also show that the traversal method is a good approximation of the exhaustive search algorithm and it is up to 11 times faster than the exhaustive one. This algorithm runs efficiently for pathways with up to 30 enzymes. For large pathways, our genetic algorithm can find good solutions in less than 10 minutes.

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“…Glycerate-3P $ Glycerate-2P $ Phosphoenolpyruvate. This is the same path found for T. pallidum [7]. This can have several explanations.…”

Section: Metabolic Engineering Of Glycolysis/ Gluconeogenesis Pathwaysupporting

confidence: 57%

Section: Limitations Of Randomized Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Manipulating the Steady State of Metabolic Pathways

Song

Büyüktahtakın

Ranka

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…It constitutes a set of enzyme catalyzed biochemical reactions through which a substrate (starting metabolite A) gets converted to a target metabolite B. An enzyme acts as a catalyst for a reaction, i.e., presence of an enzyme c 1 to a higher level, corresponding to an edge R 1 , represents the inclusion of the edge in the optimal pathway [17]. This has been explained below in this section.…”

Section: Methodsmentioning

confidence: 98%

“…This method uses the second-order derivative in addition to the gradient to determine the next updating direction and step size [21]. The c k 's can also be modified by using the GD optimization technique which largely depends on the value of the learning parameter as mentioned in the earlier work in [17]. In contrast to GD, the novelty of this new learning rule is that it is independent of the parameter that indicates the rate of modification.…”

Section: Estimation Of Weighting Coefficients C K Through Second-ordementioning

confidence: 97%

An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways

Das

Murthy

2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.

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Modeling host–pathogen interactions: H. sapiens as a host and C. difficile as a pathogen

Tomar

2012

J of Molecular Recognition

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Many complex mechanisms in immunological studies cannot be measured by experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of host-pathogen system interactions can be explored and clinical treatment schedules can be optimized to lower the microbial toxin burden and side effects in the host system. In this study, we use a computational modeling technique that aims to explain the host-pathogen interactions and suggests how the host system tries to survive from the pathogen attack. The method generates data on reaction fluxes in a pathway at steady state. A set of constraints is incorporated and an objective function for the minimization of toxin expression, with respect to some parameters such as concentration of signaling molecules, is formulated. We have integrated the toxin expression regulatory pathway in Clostridium difficile, apoptosis and mitogen-activated protein kinase pathways in an infected host (Homo sapiens). We have found that due to the minimization of the toxin expression, the signal flow values for most of the survival genes are at the higher side, whereas it is the reverse for most of the proapoptotic genes. We have observed increased signal flow values of the molecules for extracellular regulated kinase as compared with the molecules present in c-Jun NH2-terminal kinase/p38 pathways. In light of these observations, we can hypothesize that lower toxin level in a pathogen implies higher chance of host survival.

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Incorporation of enzyme concentrations into FBA and identification of optimal metabolic pathways

Cited by 14 publications

References 41 publications

Manipulating the Steady State of Metabolic Pathways

Manipulating the Steady State of Metabolic Pathways

An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways

Modeling host–pathogen interactions: H. sapiens as a host and C. difficile as a pathogen

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