Minimization of intermediate concentrations as a suggested optimality principle for biochemical networks

The criterion of minimum intermediate concentrations in steady states is suggested to be of essential relevance in the evolution of biochemical reaction networks. This extremum principle is phrased in two different ways, firstly in terms of total osmolarity of intermediates and, secondly, as a multiple criterion problem. The relationships between the two problems are elucidated and a solving method for the latter is then given. It turns out that in each optimal state, the network can be subdivided into a slow and a fast subsystem. The notion of convex conservation relations is introduced and the implications of such relations for the optimization problem are investigated.     

A Petri net approach to the study of persistence in chemical reaction networks

Persistence is the property, for differential equations in R(n), that solutions starting in the positive orthant do not approach the boundary of the orthant. For chemical reactions and population models, this translates into the non-extinction property: provided that every species is present at the start of the reaction, no species will tend to be eliminated in the course of the reaction. This paper provides checkable conditions for persistence of chemical species in reaction networks, using concepts and tools from Petri net theory, and verifies these conditions on various systems which arise in the modeling of cell signaling pathways.     

Splitting the dynamics of large biochemical interaction networks

This article is inscribed in the general motivation of understanding the dynamics on biochemical networks including metabolic and genetic interactions. Our approach is continuous modeling by differential equations. We address the problem of the huge size of those systems. We present a mathematical tool for reducing the size of the model, master-slave synchronization, and fit it to the biochemical context.     

Mathematical descriptions of biochemical networks: stability, stochasticity, evolution

In this paper, we review some fundamental aspects, as well as some new developments, in the emerging field of network biology. The focus of attention is placed on mathematical approaches to conceptual modeling of biomolecular networks with special emphasis on dynamic stability, stochasticity and evolution.     

Computational models of signalling networks for non-linear control

Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.     

On the estimation of robustness and filtering ability of dynamic biochemical networks under process delays, internal parametric perturbations and external disturbances

Inherently, biochemical regulatory networks suffer from process delays, internal parametrical perturbations as well as external disturbances. Robustness is the property to maintain the functions of intracellular biochemical regulatory networks despite these perturbations. In this study, system and signal processing theories are employed for measurement of robust stability and filtering ability of linear and nonlinear time-delay biochemical regulatory networks. First, based on Lyapunov stability theory, the robust stability of biochemical network is measured for the tolerance of additional process delays and additive internal parameter fluctuations. Then the filtering ability of attenuating additive external disturbances is estimated for time-delay biochemical regulatory networks. In order to overcome the difficulty of solving the Hamilton Jacobi inequality (HJI), the global linearization technique is employed to simplify the measurement procedure by a simple linear matrix inequality (LMI) method. Finally, an example is given in silico to illustrate how to measure the robust stability and filtering ability of a nonlinear time-delay perturbative biochemical network. This robust stability and filtering ability measurement for biochemical network has potential application to synthetic biology, gene therapy and drug design.     

Efficient nonlinear predictive control of a biochemical reactor using neural models

This paper describes the application of artificial neural networks to modelling and control of a continuous fermentor. A computationally efficient nonlinear model predictive control (MPC) algorithm with nonlinear prediction and linearisation (MPC-NPL) which needs solving on-line a quadratic programming problem is developed. It is demonstrated that the algorithm results in closed-loop control performance similar to that obtained in nonlinear MPC, which hinges on full on-line non-convex optimisation. The computational complexity of the MPC-NPL algorithm is shown, control accuracy and robustness are also demonstrated in the case of noisy measurements and disturbances affecting the process.     

Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide

Based on an experimental database consisting of 194 daily cases, artificial neural networks were used to model the removal efficiency of a biofilter for treating hydrogen sulphide (H₂S). In this work, the removal efficiency of the reactor was considered as a function of the changes in the air flow and concentration of H₂S entering the biofilter. In order to obtain true representative values, the removal efficiencies (outputs) were measured 24 h after each input was changed. A MLP (multilayer perceptron 2-2-1) model with two input variables (unit flow and concentration of the contaminant fed into the biofilter) rendered good prediction values with a determination coefficient of 0.92 for the removal efficiency within the range studied. This means that the MLP model can explain 92% of the overall variability detected in the biofilter corresponding to a wide range of operating conditions.     

Metabolic engineering of a complex biochemical pathway: The lysine and threonine biosynthesis as an example

The nutritional quality of crop plants is determined by their content in essential amino acids provided in food for humans or in feed for monogastric animals. Amino acid composition of crop–based diets can be improved via manipulation of the properties of key enzymes of amino acid biosynthetic pathways by mutation and transformation. We focused on the aspartate-derived amino acid pathway producing four essential amino acids: lysine, threonine, isoleucine and methionine. Genes encoding aspartate kinase (AK) and dihydrodipicolinate synthase (DHDPS) that operate as key genes of the aspartate pathway have been cloned from Arabidopsis. Genetic and molecular studies revealed that at least five different ak genes are represented. Some of them were characterized in terms of gene and promoter structure, developmental expression and regulatory properties. In the case of dhdps, two quite identical genes have been identified and characterized at expression level. Mutated genes encoding a fully feedback-insensitive form of the DHDPS enzyme were obtained from Nicotiana sylvestris and Arabidopsis. Several chimeric constructs harbouring this mutated allele under the control of constitutive or seed-specific promoters were transferred via Agrobacterium or biolistics in various plant species. In all cases, lines with significant increase of free lysine content were obtained in vegetative organs, but the impact of the transgene in seeds is limited due to the presence of an active catabolic enzyme, lysine ketoreductase. These results show that, although dealing with a complex, highly regulated pathway, the overexpression of a single gene encoding a feedback-insensitive form of the key enzyme DHDPS exerts a significant effect on the carbon flux through the aspartate pathway towards lysine production.     

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.     

Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

Large‐scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature‐derived network of 82 proteins covering the immediate‐early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature‐derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell‐type‐specific models of mammalian signalling from generic protein signalling networks.     

Anhydrous tert-pentanol as a novel media for the efficient enzymatic synthesis of amoxicillin

The efficient enzymatic synthesis of amoxicillin using anhydrous tert-pentanol as a novel media has been demonstrated for the first time. p-OH-Phenylglycine methyl ester (HPGM) was selected as the activated acyl donor due to its good solubility in organic solvents. The screening results of 21 organic solvents showed that solvents with either strong polarity or poor substrate solubility were unfavorable. Remarkable catalytic activity of the immobilized penicillin acylase (IPA) from Escherichia coli was retained in tert-pentanol, and high yield could be obtained. Effects of various parameters such as acyl donor, water content or cosolvents of tert-pentanol, substrate concentration, temperature, etc., on the enzymatic synthesis of amoxicillin in tert-pentanol were investigated systematically. The best reaction medium, the optimal temperature, initial concentration of 6-APA and HPGM and concentration of enzyme were tert-pentanol, 15 °C, 100, 200 mM and 20 IU/mL, respectively. Under the optimal conditions, the yield of amoxicillin was as high as 88% after a reaction time of 20 h.     

In vitro metabolic fate of a novel structural class: evidence for the formation of a reactive intermediate on a benzothiophene moiety

The characterization of the metabolic pathways of new chemical entities with a special emphasis on detecting potentially reactive metabolites is increasingly being performed early in the drug discovery process. In the present study, the preliminary in vitro metabolic routes of a series of novel 2-substituted benzothiophene-containing discovery molecules were determined in fresh and cryopreserved hepatocyte suspensions. The objectives of this investigation were: (1) to use systematic LC/MS and LC/MS/MS analyses to provide a preliminary characterization of the in vitro metabolism of these compounds, with a particular focus on metabolites potentially arising from reactive intermediates, and (2) to identify potential lead molecules not associated with such metabolic pathways. This benzothiophene-containing series of compounds was characterized by the formation of five metabolites, at least two of which (dihydrodiol formation and glutathione adduct of the dihydrohydroxyl) were indicative of the formation of a reactive arene oxide intermediate. Tandem mass spectral analysis of the metabolites formed from a variety of structurally similar compounds demonstrated this reactive arene oxide intermediate to form on the 2-substituted benzothiophene moiety. Substitution of the benzothiophene with other functional groups eliminated these potentially toxic metabolites. The data presented here demonstrate the utility of performing metabolic route screens early in the drug discovery process prior to lengthy and costly radiolabeled studies, and furthermore, implicate a 2-substituted benzothiophene moiety as a substrate for formation of a reactive arene oxide intermediate.     

Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work

Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdöven Dam Reservoir. The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA.     

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.     

碳的氧化态变化与ATP生成的近似计算

本文根据碳的氧化数概念,将其应用到碳水化合物氧化代谢和能量计算的过程中。碳水化合物彻底氧化最终会生成二氧化碳和水,反应前后氢氧元素的氧化数没有发生改变,因此可以将碳的氧化数状态的变化同电子转移的量联系起来,从而根据物质初态和终态碳的氧化数的变化,推导出物质代谢中电子的转移和氧化脱氢过程,进而根据脱氢的次数来整体估算ATP的生成。     

The Transgenic Rabbit as Model for Human Diseases and as a Source of Biologically Active Recombinant Proteins

Until recently, transgenic rabbits were produced exclusively by pronuclear microinjection which results in additive random insertional transgenesis; however, progress in somatic cell cloning based on nuclear transfer will soon make it possible to produce rabbits with modifications to specific genes by the combination of homologous recombination and subsequent prescreening of nuclear donor cells. Transgenic rabbits have been found to be excellent animal models for inherited and acquired human diseases including hypertrophic cardiomyopathy, perturbed lipoprotein metabolism and atherosclerosis. Transgenic rabbits have also proved to be suitable bioreactors for the production of recombinant protein both on an experimental and a commercial scale. This review summarizes recent research based on the transgenic rabbit model.     

Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of <Emphasis Type="Italic">Bacillus megaterium</Emphasis> cellular concentrations

Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.     

GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks

This paper presents GINsim, a Java software suite devoted to the qualitative modelling, analysis and simulation of genetic regulatory networks. Formally, our approach leans on discrete mathematical and graph-theoretical concepts. GINsim encompasses an intuitive graph editor, enabling the definition and the parameterisation of a regulatory graph, as well as a simulation engine to compute the corresponding qualitative dynamical behaviour. Our computational approach is illustrated by a preliminary model analysis of the inter-cellular regulatory network activating Notch at the dorsal-ventral boundary in the wing imaginal disc of Drosophila. We focus on the cross-regulations between five genes (within and between two cells), which implements the dorsal-ventral border in the developing imaginal disc. Our simulations qualitatively reproduce the wild-type developmental pathway, as well as the outcome of various types of experimental perturbations, such as loss-of-function mutations or ectopically induced gene expression.