Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment

Microbial strains can be optimized using metabolic engineering which implements gene knockout techniques. These techniques manipulate potential genes to increase the yield of metabolites through restructuring metabolic networks. Nowadays, several hybrid optimization algorithms have been proposed to optimize the microbial strains. However, the existing algorithms were unable to obtain optimal strains because the nonessential genes are hardly to be diagnosed and need to be removed due to high complexity of metabolic network. Therefore, the main goal of this study is to overcome the limitation of the existing algorithms by proposing a hybrid of Differential Evolution and Minimization of Metabolic Adjustments (DEMOMA). Differential Evolution (DE) is known as population-based stochastic search algorithm with few tuneable parameter control. Minimization of Metabolic Adjustment (MOMA) is one of the constraint based algorithms which act to simulate the cellular metabolism after perturbation (gene knockout) occurred to the metabolic model. The strength of MOMA is the ability to simulate the strains that have undergone mutation precisely compared to Flux Balance Analysis. The data set used for the production of fumaric acid is S. cerevisiae whereas data set for lycopene production is Y. lipolytica metabolic networks model. Experimental results show that the DEMOMA was able to improve the growth rate for the fumaric acid production rate while for the lycopene production, Biomass Product Coupled Yield (BPCY) and production rate were both able to be optimized

Kinetic Intersections as a Source of Information on Rate Coefficients

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A multiscale hybrid model for pro-angiogenic calcium signals in a vascular endothelial cell

Cytosolic calcium machinery is one of the principal signaling mechanisms by which endothelial cells (ECs) respond to external stimuli during several biological processes, including vascular progression in both physiological and pathological conditions. Low concentrations of angiogenic factors (such as VEGF) activate in fact complex pathways involving, among others, second messengers arachidonic acid (AA) and nitric oxide (NO), which in turn control the activity of plasma membrane calcium channels. The subsequent increase in the intracellular level of the ion regulates fundamental biophysical properties of ECs (such as elasticity, intrinsic motility, and chemical strength), enhancing their migratory capacity. Previously, a number of continuous models have represented cytosolic calcium dynamics, while EC migration in angiogenesis has been separately approached with discrete, lattice-based techniques. These two components are here integrated and interfaced to provide a multiscale and hybrid Cellular Potts Model (CPM), where the phenomenology of a motile EC is realistically mediated by its calcium-dependent subcellular events. The model, based on a realistic 3-D cell morphology with a nuclear and a cytosolic region, is set with known biochemical and electrophysiological data. In particular, the resulting simulations are able to reproduce and describe the polarization process, typical of stimulated vascular cells, in various experimental conditions.Moreover, by analyzing the mutual interactions between multilevel biochemical and biomechanical aspects, our study investigates ways to inhibit cell migration: such strategies have in fact the potential to result in pharmacological interventions useful to disrupt malignant vascular progressio

Mathematical modelling plant signalling networks

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting

A hybrid of differential search algorithm and flux balance analysis to: Identify knockout strategies for in silico optimization of metabolites production

An increasing demand of naturally producing metabolites has gained the attention of researchers to develop better algorithms for predicting the effects of reaction knockouts. With the success of genome sequencing, in silico metabolic engineering has aided the researchers in modifying the genome-scale metabolic network. However, the complexities of the metabolic networks, have led to difficulty in obtaining a set of knockout reactions, which eventually lead to increase in computational time. Hence, many computational algorithms have been developed. Nevertheless, most of these algorithms are hindered by the solution being trapped in the local optima. In this paper, we proposed a hybrid of Differential Search Algorithm (DSA) and Flux Balance Analysis (FBA), to identify knockout reactions for enhancing the production of desired metabolites. Two organisms namely Escherichia coli and Zymomonas mobilis were tested by targeting the production rate of succinic acid, acetic acid, and ethanol. From this experiment, we obtained the list of knockout reactions and production rate. The results show that our proposed hybrid algorithm is capable of identifying knockout reactions with above 70% of production rate from the wild-type

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided

Improved particle swarm optimization and gravitational search algorithm for parameter estimation in aspartate pathways

One of the main issues in biological system is to characterize the dynamic behaviour of the complex biological processes. Usually, metabolic pathway models are used to describe the complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. Therefore, the parameter values are estimated by fitting the model with experimental data. However, the estimation on these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Previously, a computational approach namely optimization algorithms are used to estimate the measurement of the model parameters. Most of these algorithms previously often suffered bad estimation for the biological system models, which resulted in bad fitting (error) the model with the experimental data. This research proposes a parameter estimation algorithm that can reduce the fitting error between the models and the experimental data. The proposed algorithm is an Improved Particle Swarm Optimization and Gravitational Search Algorithm (IPSOGSA) to obtain the near-optimal kinetic parameter values from experimental data. The improvement in this algorithm is a local search, which aims to increase the chances to obtain the global solution. The outcome of this research is that IPSOGSA can outperform other comparison algorithms in terms of root mean squared error (RMSE) and predictive residual error sum of squares (PRESS) for the estimated results. IPSOGSA manages to score the smallest RMSE with 12.2125 and 0.0304 for Ile and HSP metabolite respectively. The predicted results are benefits for the estimation of optimal kinetic parameters to improve the production of desired metabolites

Co-evolution of strain design methods based on flux balance and elementary mode analysis

More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last ten years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications.Novo Nordisk UK Research Foundation(NORTE-07-0124-FEDER-000028