Neuro-fuzzy prediction of uricase production

Recent biotechnology requires implementation of new modelling methods based on knowledge principles and learning structures, comprised in fuzzy knowledge-based systems (FKBS), neural networks (NN) and different hybrid methods. The intelligent modelling approaches solve sufficiently a very important problem - processing of scarce, uncertainty and incomplete numerical and linguistic information about multivariate non-linear and non-stationary systems as well as biotechnological processes. The paper deals with prediction of an enzyme oxidizing uric acid to alantoin - the uricase, produced by Candida utilis 90-12 employing neuro-fuzzy knowledge-based approach. The implemented predictive technique exploits the fact that the fuzzy model can be seen as a network structure, similar to artificial NN, which on computational level assure a high model accuracy. The predictors implemented are four different by nature variables. The developed predictive model shows that best predictors of uricase production are biomass and limiting substrate concentrations.     

A neural network estimator for total biomass of filamentous fungi growing on two dimensional solid substrate

A neural network dynamic model is proposed for the on-line estimation of total biomass during filamentous fungi cultures on two dimensional solid substrate. The neural network provides an accurate and robust estimation of biomass from macroscopic measurements of the colony radius evolution. Experiments were performed on Gibberella fujikuroi growing on Petri dishes under different conditions of temperature and water activity. © Rapid Science Ltd. 1998     

Functional nodes in dynamic neural networks for bioprocess modelling

This contribution presents a novel method for the direct integration of a-priori knowledge in a neural network and its application for the online determination of a secondary metabolite during industrial yeast fermentation. Hereby, existing system knowledge is integrated in an artificial neural network (ANN) by means of 'functional nodes'. A generalized backpropagation algorithm is presented. For illustration, a set of ordinary differential equations describing the diacetyl formation and degradation during the cultivation is incorporated in a functional node and integrated in a dynamic feedforward neural network in a hybrid manner. The results show that a hybrid modelling approach exploiting available a-priori knowledge and experimental data can considerably outperform a pure data-based modelling approach with respect to robustness, generalization and necessary amount of training data. The number of training sets were decreased by 50%, obtaining the same accuracy as in a conventional approach. All incorrect decisions, according to defined cost criteria obtained with the conventional ANN, were avoided.     

Travelling Waves in Hybrid Chemotaxis Models

Hybrid models of chemotaxis combine agent-based models of cells with partial differential equation models of extracellular chemical signals. In this paper, travelling wave properties of hybrid models of bacterial chemotaxis are investigated. Bacteria are modelled using an agent-based (individual-based) approach with internal dynamics describing signal transduction. In addition to the chemotactic behaviour of the bacteria, the individual-based model also includes cell proliferation and death. Cells consume the extracellular nutrient field (chemoattractant), which is modelled using a partial differential equation. Mesoscopic and macroscopic equations representing the behaviour of the hybrid model are derived and the existence of travelling wave solutions for these models is established. It is shown that cell proliferation is necessary for the existence of non-transient (stationary) travelling waves in hybrid models. Additionally, a numerical comparison between the wave speeds of the continuum models and the hybrid models shows good agreement in the case of weak chemotaxis and qualitative agreement for the strong chemotaxis case. In the case of slow cell adaptation, we detect oscillating behaviour of the wave, which cannot be explained by mean-field approximations.     

A hybrid agent-based approach for modeling microbiological systems

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10³ cells and 1.2×10⁶ molecules. The model produces cell migration patterns that are comparable to laboratory observations.     

Growth mechanisms and growth kinetics of filamentous microorganisms

Filamentous microorganisms are of major biotechnological importance, being responsible for production of the majority of secondary metabolites, particularly antibiotics. Two main groups are involved, filamentous fungi and filamentous actinomycetes, particularly the streptomycetes. In terms of cellular growth mechanisms, these groups differ greatly. Eukaryotic fungi possess subcellular organelles and cytoskeletal structures directing growth while prokaryotic streptomycetes have no such cellular organization. Despite these fundamental differences, both groups exhibit similar morphologies, growth patterns, growth forms, and hyphal and mycelial growth kinetics on solid media and in liquid culture both grow as dispersed mycelia and pellets. The article therefore discusses the relationship between cellular growth mechanisms and vegetative growth in both filamentous fungi and actinomycetes, the conceptual and theoretical models applicable to both groups, and the significance of such models in industrial fermentation processes.     

An X-ray microtomography-based method for detailed analysis of the three-dimensional morphology of fungal pellets

Filamentous fungi are widely used in the production of biotechnological compounds. Since their morphology is strongly linked to productivity, it is a key parameter in industrial biotechnology. However, identifying the morphological properties of filamentous fungi is challenging. Owing to a lack of appropriate methods, the detailed three-dimensional morphology of filamentous pellets remains unexplored. In the present study, we used state-of-the-art X-ray microtomography (µCT) to develop a new method for detailed characterization of fungal pellets. µCT measurements were performed using freeze-dried pellets obtained from submerged cultivations. Three-dimensional images were generated and analyzed to locate and quantify hyphal material, tips, and branches. As a result, morphological properties including hyphal length, tip number, branch number, hyphal growth unit, porosity, and hyphal average diameter were ascertained. To validate the potential of the new method, two fungal pellets were studied—one from Aspergillus niger and the other from Penicillium chrysogenum. We show here that µCT analysis is a promising tool to study the three-dimensional structure of pellet-forming filamentous microorganisms in utmost detail. The knowledge gained can be used to understand and thus optimize pellet structures by means of appropriate process or genetic control in biotechnological applications.     

Improving the amylolytic activity of Saccharomyces cerevisiae glucoamylase by the addition of a starch binding domain

Glucoamylase produced by amylolytic strains of Saccharomyces cerevisiae (var. diastaticus) lacks a starch binding domain that is present in homologous glucoamylases from Aspergillus niger and other filamentous fungi. The absence of the binding domain makes the enzyme inefficient against raw starch and hence unsuitable for most biotechnological applications. We have constructed a hybrid glucoamylase-encoding gene by in-frame fusion of the S. cerevisiae STA1 gene and DNA fragment that encodes the starch binding domain of A. niger glucoamylase. The hybrid enzyme resulting from expression of the chimeric gene in S. cerevisiae has substrate binding capability and hydrolyses insoluble starch, properties not present in the original yeast enzyme.     

Dynamic Causal Modeling and subspace identification methods

The main contribution of the paper is in formulating the problem of detection of brain regions structure within the framework of dynamic system theory. The motivation is to see if the mature domain of experimental identification of dynamic systems can provide a methodology alternative to Dynamic Causal Modeling (DCM) which is currently used as an exclusive tool to estimate the structure of interconnections among a given set of brain regions using the measured data from functional magnetic resonance imaging (fMRI). The key tool proposed for modeling the structure of brain interconnections in this paper is subspace identification methods which produce linear state-space model, thus neglecting the bilinear term from DCM. The procedure is illustrated using a simple two-region model with maximally simplified linearized hemodynamics. We assume that the underlying system can be modeled by a set of linear differential equations, and identify the parameters (in terms of state space matrices), without any a priori constraints. We then transform the hidden states so that the implicit state matrix has a form or structure that is consistent with the generation of (region-specific) hemodynamic signals by coupled neuronal states.     

小麦赤霉病流行动态的控制论模型

对小麦赤霉病流行资料进行分析,给出了流行状态微分方程的预测方法．根据不同的状态微分方程,以极大值原理建立小麦赤霉病流行动态的控制论模型,得到流行动态的最优控制轨线．结果表明,气象条件有利,品种感病,药剂防效低,则防治时间应提早．要求消灭病害彻底,防治时间也应提前．药剂防效高,小麦产量高,品种感病,则防治后的净收益就大．以小偃6号为例,给出了Malthus增长型下的病害最优控制轨线和最大净收益．     

Nonlinear EEG analysis based on a neural mass model

The well-known neural mass model described by Lopes da Silva et al. (1976) and Zetterberg et al. (1978) is fitted to actual EEG data. This is achieved by reformulating the original set of integral equations as a continuous-discrete state space model. The local linearization approach is then used to discretize the state equation and to construct a nonlinear Kalman filter. On this basis, a maximum likelihood procedure is used for estimating the model parameters for several EEG recordings. The analysis of the noise-free differential equations of the estimated models suggests that there are two different types of alpha rhythms: those with a point attractor and others with a limit cycle attractor. These attractors are also found by means of a nonlinear time series analysis of the EEG recordings. We conclude that the Hopf bifurcation described by Zetterberg et al. (1978) is present in actual brain dynamics. Received: 11 August 1997 / Accepted in revised form: 20 April 1999     

A Finite Element Approach for Skeletal Muscle using a Distributed Moment Model of Contraction

The present paper describes a geometrically and physically nonlinear continuum model to study the mechanical behaviour of passive and active skeletal muscle. The contraction is described with a Huxley type model. A Distributed Moments approach is used to convert the Huxley partial differential equation in a set of ordinary differential equations. An isoparametric brick element is developed to solve the field equations numerically. Special arrangements are made to deal with the combination of highly nonlinear effects and the nearly incompressible behaviour of the muscle. For this a Natural Penalty Method (NPM) and an Enhanced Stiffness Method (ESM) are tested. Finally an example of an analysis of a contracting tibialis anterior muscle of a rat is given. The DM-method proved to be an efficient tool in the numerical solution process. The ESM showed the best performance in describing the incompressible behaviour.     

A Finite Element Approach for Skeletal Muscle using a Distributed Moment Model of Contraction

Abstract

The present paper describes a geometrically and physically nonlinear continuum model to study the mechanical behaviour of passive and active skeletal muscle. The contraction is described with a Huxley type model. A Distributed Moments approach is used to convert the Huxley partial differential equation in a set of ordinary differential equations. An isoparametric brick element is developed to solve the field equations numerically. Special arrangements are made to deal with the combination of highly nonlinear effects and the nearly incompressible behaviour of the muscle. For this a Natural Penalty Method (NPM) and an Enhanced Stiffness Method (ESM) are tested. Finally an example of an analysis of a contracting tibialis anterior muscle of a rat is given. The DM-method proved to be an efficient tool in the numerical solution process. The ESM showed the best performance in describing the incompressible behaviour.     

Hybrid modeling of xanthan gum bioproduction in batch bioreactor

This work is focused on hybrid modeling of xanthan gum bioproduction process by Xanthomonas campestris pv. mangiferaeindicae. Experiments were carried out to evaluate the effects of stirred speed and superficial gas velocity on the kinetics of cell growth, lactose consumption and xanthan gum production in a batch bioreactor using cheese whey as substrate. A hybrid model was employed to simulate the bio-process making use of an artificial neural network (ANN) as a kinetic parameter estimator for the phenomenological model. The hybrid modeling of the process provided a satisfactory fitting quality of the experimental data, since this approach makes possible the incorporation of the effects of operational variables on model parameters. The applicability of the validated model was investigated, using the model as a process simulator to evaluate the effects of initial cell and lactose concentration in the xanthan gum production.     

A multi-phase optimal control technique for the simulation of a human vertical jump

A multi-phase optimal control technique is presented that can be used to solve dynamic optimization problems involving musculoskeletal systems. The biomechanical model consists of a set of differential equations describing the dynamics of the multi-body system and the generation of the dynamic forces of the human muscles. Within the optimization technique, subintervals can be defined in which the differential equations are continuous. At the boundaries the dimension of the state- and control vector as well as the dimension of the right-hand side may change. The problem is solved by a multiple shooting approach which converts the problem into a non-linear program. The method is applied to simulate a human jump movement.     

Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network

This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.     

A bioenergetic model for simulating athletic performance of intermediate duration

Simulating factors affecting human athletic performance, including fatigue, requires a dynamic model of the bioenergetic capabilities of the athlete. To address general cases, the model needs inputs, outputs, and states with a set of differential equations describing how the inputs affect the states and outputs as functions of time. We improve an existing phenomenological muscle model, removing unnecessarily fast dynamic behavior, adding force–velocity dependence, and generalizing it to task level activities. This makes it more suitable for simulating and calculating optimal strategies of athletic events of medium duration (longer than a sprint but shorter than a marathon). To examine the validity and limitations of the model, parameters have been identified from numerical fits to published experimental data.     

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.     

Studying and approximating spatio-temporal models for epidemic spread and control

A class of simple spatio-temporal stochastic models for the spread and control of plant disease is investigated. We consider a lattice-based susceptible-infected model in which the infection of a host occurs through two distinct processes: a background infective challenge representing primary infection from external sources, and a short-range interaction representing the secondary infection of susceptibles by infectives within the population. Recent data-modelling studies have suggested that the above model may describe the spread of aphid-borne virus diseases in orchards. In addition, we extend the model to represent the effects of different control strategies involving replantation (or recovery). The Contact Process is a particular case of this model. The behaviour of the model has been studied using Cellular-Automata simulations. An alternative approach is to formulate a set of deterministic differential equations that captures the essential dynamics of the stochastic system. Approximate solutions to this set of equations, describing the time evolution over the whole parameter range, have been obtained using the pairwise approximation (PA) as well as the most commonly used mean-field approximation (MF). Comparison with simulation results shows that PA is significantly superior to MF, predicting accurately both transient and long-run, stationary behaviour over relevant parts of the parameter space. The conditions for the validity of the approximations to the present model and extensions thereof are discussed.