Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model

The development of a large-scale metabolic model of Escherichia coli (E. coli) is very crucial to identify the potential solution of industrially viable productions. However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. Thus, this research aimed to estimate large-scale kinetic parameters of the main metabolic pathway of the E. coli model. In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. Initially, PSO algorithm was adapted to find the globally optimal result based on unorganized particle movement in the search space toward the optimal solution. This development then introduces the Se-PSO algorithm in which the particles are segmented to find a local optimal solution at the beginning and later sought by the PSO algorithm. Additionally, the study proposed an Enhance Se-PSO algorithm to improve the linear value of inertia weigh

Synthetic Biology: Tools to Design, Build, and Optimize Cellular Processes

The general central dogma frames the emergent properties of life, which make biology both necessary and difficult to engineer. In a process engineering paradigm, each biological process stream and process unit is heavily influenced by regulatory interactions and interactions with the surrounding environment. Synthetic biology is developing the tools and methods that will increase control over these interactions, eventually resulting in an integrative synthetic biology that will allow ground-up cellular optimization. In this review, we attempt to contextualize the areas of synthetic biology into three tiers: (1) the process units and associated streams of the central dogma, (2) the intrinsic regulatory mechanisms, and (3) the extrinsic physical and chemical environment. Efforts at each of these three tiers attempt to control cellular systems and take advantage of emerging tools and approaches. Ultimately, it will be possible to integrate these approaches and realize the vision of integrative synthetic biology when cells are completely rewired for biotechnological goals. This review will highlight progress towards this goal as well as areas requiring further research

Visualizing regulatory interdependencies and parameter sensitivities in biochemical network models

For the evaluation of data from stimulus response experiments dynamic metabolic network models are generated. With an increase of reaction steps and regulatory interdependencies the amount of the simulation data becomes hard to handle. In this paper, we present the application and extension of methods combining visualization and animation of dynamic models to facilitate the analysis of the complex system behaviour.The dynamic changes of metabolite pools and fluxes are simultaneous visualized within the network structure. Depending on the scaling used, different focuses can be set, e.g. to observe local dynamics or global concentration balances. For the visualization of the present inhibition and activation state of certain reaction steps of a metabolic network model a novel quantification method is proposed.The sensitivity analysis of dynamic metabolic network models leads to high-dimensional sensitivity matrices that vary over time. To process the enormous amount of data we use a colour scale transformation and the reorderable matrix method for the visual exploration of the time-varying matrices.The benefits of our methods are illustrated with the help of a metabolic network model of the central carbon metabolism in Escherichia coli. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved