Kinetic parameters estimation for increasing the efficiency of nutrient absorption in fruit trees

The selection of rootstocks presenting traits of interest, such as resistance to pests and diseases, and compatibility with scion capable of producing fruits attractive to the market, has revolutionized fruit crops worldwide. However, the enormous potential of plants to be selected based on their nutrient uptake efficiency is yet to be properly explored; it can be done based on knowledge about kinetic parameters such as Influx, Maximum uptake velocity, Constant ion affinity with the carrier, and Minimum concentration. The adaptation of a methodology elaborated for annual crops to set kinetic parameters, as well as the Brazilian development of the Influx software, enabled selecting not only peach rootstocks with different nutrient uptake capacities, but also observing the influence the grafting of a particular cultivar crown has on rootstock’s ability to uptake a given nutrient from the soil. Thus, fruit tree rootstocks and cultivars can always be selected based on traits desired by fruit growers and consumers, with emphasis on yield and quality of fruits. They can also be more efficient in taking up nutrients from low natural-fertility soils that require lower nutrient amounts, a fact that increases fertilizer use rationalization and reduces environmental contamination processes resulting from excessive nutrient applications.

An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.