Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data

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

Comprehensive review of models and methods for inferences in bio-chemical reaction networks

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed

Improved scatter search algorithm based on meerkat clan algorithm to solve NP-hard problems

A modified Scatter Search (SS) algorithm based on Meerkat Clan Algorithm (MCA) has been presented in this paper. SS is one of the important metaheuristic algorithms, while the MCA is one of the recent swarm intelligence algorithms. The modified SS algorithm, including the main steps of MCA, through it the diversity and exploration of SS-MCA's solutions, have improved. The proposed algorithm has been applied to two important NP-Hard problems (Travelling Salesman Problem (TSP) and Flexible Job Shop Scheduling Problem (FJSSP)) to verify the performance of SS-MCA. The experimental results show that the performance of SS-MCA is better than both SS and MCA, respectively

Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

[Abstract] Many key problems in computational systems biology and bioinformatics can be formulated and solved using a global optimization framework. The complexity of the underlying mathematical models require the use of efficient solvers in order to obtain satisfactory results in reasonable computation times. Metaheuristics are gaining recognition in this context, with Differential Evolution (DE) as one of the most popular methods. However, for most realistic applications, like those considering parameter estimation in dynamic models, DE still requires excessive computation times. Here we consider this latter class of problems and present several enhancements to DE based on the introduction of additional algorithmic steps and the exploitation of parallelism. In particular, we propose an asynchronous parallel implementation of DE which has been extended with improved heuristics to exploit the specific structure of parameter estimation problems in computational systems biology. The proposed method is evaluated with different types of benchmarks problems: (i) black-box global optimization problems and (ii) calibration of non-linear dynamic models of biological systems, obtaining excellent results both in terms of quality of the solution and regarding speedup and scalability.Ministerio de Economía y Competitividad; DPI2011-28112-C04-03Consejo Superior de Investigaciones Científicas; PIE-201170E018Ministerio de Ciencia e Innovación; TIN2013-42148-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05

Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso

The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight ((Formula presented.)) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Optimisation of microfluidic experiments for model calibration of a synthetic promoter in S. cerevisiae

This thesis explores, implements, and examines the methods to improve the efficiency of model calibration experiments for synthetic biological circuits in three aspects: experimental technique, optimal experimental design (OED), and automatic experiment abnormality screening (AEAS). Moreover, to obtain a specific benchmark that provides clear-cut evidence of the utility, an integrated synthetic orthogonal promoter in yeast (S. cerevisiae) and a corresponded model is selected as the experiment object. This work first focuses on the “wet-lab” part of the experiment. It verifies the theoretical benefit of adopting microfluidic technique by carrying out a series of in-vivo experiments on a developed automatic microfluidic experimental platform. Statistical analysis shows that compared to the models calibrated with flow-cytometry data (a representative traditional experimental technique), the models based on microfluidic data of the same experiment time give significantly more accurate behaviour predictions of never-encountered stimuli patterns. In other words, compare to flow-cytometry experiments, microfluidics can obtain models of the required prediction accuracy within less experiment time. The next aspect is to optimise the “dry-lab” part, i.e., the design of experiments and data processing. Previous works have proven that the informativeness of experiments can be improved by optimising the input design (OID). However, the amount of work and the time cost of the current OID approach rise dramatically with large and complex synthetic networks and mathematical models. To address this problem, this thesis introduces the parameter clustering analysis and visualisation (PCAV) to speed up the OID by narrowing down the parameters of interest. For the first time, this thesis proposes a parameter clustering algorithm based on the Fisher information matrix (FIMPC). Practices with in-silico experiments on the benchmarking promoter show that PCAV reduces the complexity of OID and provides a new way to explore the connections between parameters. Moreover, the analysis shows that experiments with FIMPC-based OID lead to significantly more accurate parameter estimations than the current OID approach. Automatic abnormality screening is the third aspect. For microfluidic experiments, the current identification of invalid microfluidic experiments is carried out by visual checks of the microscope images by experts after the experiments. To improve the automation level and robustness of this quality control process, this work develops an automatic experiment abnormality screening (AEAS) system supported by convolutional neural networks (CNNs). The system learns the features of six abnormal experiment conditions from images taken in actual microfluidic experiments and achieves identification within seconds in the application. The training and validation of six representative CNNs of different network depths and design strategies show that some shallow CNNs can already diagnose abnormal conditions with the desired accuracy. Moreover, to improve the training convergence of deep CNNs with small data sets, this thesis proposes a levelled-training method and improves the chance of convergence from 30% to 90%. With a benchmark of a synthetic promoter model in yeast, this thesis optimises model calibration experiments in three aspects to achieve a more efficient procedure: experimental technique, optimal experimental design (OED), and automatic experiment abnormality screening (AEAS). In this study, the efficiency of model calibration experiments for the benchmarking model can be improved by: adopting microfluidics technology, applying CAVP parameter analysis and FIMPC-based OID, and setting up an AEAS system supported by CNN. These contributions have the potential to be exploited for designing more efficient in-vivo experiments for model calibration in similar studies

Biological investigation and predictive modelling of foaming in anaerobic digester

Anaerobic digestion (AD) of waste has been identified as a leading technology for greener renewable energy generation as an alternative to fossil fuel. AD will reduce waste through biochemical processes, converting it to biogas which could be used as a source of renewable energy and the residue bio-solids utilised in enriching the soil. A problem with AD though is with its foaming and the associated biogas loss. Tackling this problem effectively requires identifying and effectively controlling factors that trigger and promote foaming. In this research, laboratory experiments were initially carried out to differentiate foaming causal and exacerbating factors. Then the impact of the identified causal factors (organic loading rate-OLR and volatile fatty acid-VFA) on foaming occurrence were monitored and recorded. Further analysis of foaming and nonfoaming sludge samples by metabolomics techniques confirmed that the OLR and VFA are the prime causes of foaming occurrence in AD. In addition, the metagenomics analysis showed that the phylum bacteroidetes and proteobacteria were found to be predominant with a higher relative abundance of 30% and 29% respectively while the phylum actinobacteria representing the most prominent filamentous foam causing bacteria such as Norcadia amarae and Microthrix Parvicella had a very low and consistent relative abundance of 0.9% indicating that the foaming occurrence in the AD studied was not triggered by the presence of filamentous bacteria. Consequently, data driven models to predict foam formation were developed based on experimental data with inputs (OLR and VFA in the feed) and output (foaming occurrence). The models were extensively validated and assessed based on the mean squared error (MSE), root mean squared error (RMSE), R2 and mean absolute error (MAE). Levenberg Marquadt neural network model proved to be the best model for foaming prediction in AD, with RMSE = 5.49, MSE = 30.19 and R2 = 0.9435. The significance of this study is the development of a parsimonious and effective modelling tool that enable AD operators to proactively avert foaming occurrence, as the two model input variables (OLR and VFA) can be easily adjustable through simple programmable logic controller