In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling

There is a wide diversity of unexplored metabolism encoded in the genomes of microorganisms that have an important environmental role. Genome-scale metabolic modelling enables the individual reactions that are encoded in annotated genomes to be organized into a coherent whole, which can then be used to predict metabolic fluxes that will optimize cell function under a range of conditions. In this Review, we summarize a series of studies in which genome-scale metabolic modelling of Geobacter spp. has resulted in an in-depth understanding of their central metabolism and ecology. A similar iterative modelling and experimental approach could accelerate elucidation of the physiology and ecology of other microorganisms inhabiting a diversity of environments, and could guide optimization of the practical applications of these species.     

Integrative modelling of gene expression and cell metabolism

The goal of modelling biochemical networks is to understand the system's behaviour (dynamics and control) of these networks in terms of the properties of the individual molecules. Most modelling approaches have dealt with either gene expression or cell metabolism. In light of the widespread use of robotic technologies in laboratories and improved computational power, it is now time to incorporate information from all biochemical levels--gene expression, protein interactions and metabolism--into integrated models. Here, we review the literature on modelling gene expression with cell metabolism. At the end we describe some analytic methods to deal with these systems.     

A dynamical model of environmental effects on allocation to carbon-based secondary compounds in juvenile trees

BACKGROUND AND AIMS: Patterns and variations in concentration of carbon-based secondary compounds in plant tissues have been explained by means of different complementary and, in some cases, contradictory plant defence hypotheses for more than 20 years. These hypotheses are conceptual models which consider environmental impacts on plant internal demands. In the present study, a mathematical model is presented, which converts and integrates the concepts of the 'Growth-Differentiation Balance' hypothesis and the 'Protein Competition' model into a dynamic plant growth model, that was tested with concentration data of polyphenols in leaves of juvenile apple, beech and spruce trees. The modelling approach is part of the plant growth model PLATHO that considers simultaneously different environmental impacts on the most important physiological processes of plants. METHODS: The modelling approach for plant internal resource allocation is based on a priority scheme assuming that growth processes have priority over allocation to secondary compounds and that growth-related metabolism is more strongly affected by nitrogen deficiency than defence-related secondary metabolism. KEY RESULTS: It is shown that the model can reproduce the effect of nitrogen fertilization on allocation patterns in apple trees and the effects of elevated CO(2) and competition in juvenile beech and spruce trees. The analysis of model behaviour reveals that large fluctuations in plant internal availability of carbon and nitrogen are possible within a single vegetation period. Furthermore, the model displays a non-linear allocation behaviour to carbon-based secondary compounds. CONCLUSIONS: The simulation results corroborate the underlying assumptions of the presented modelling approach for resource partitioning between growth-related primary metabolism and defence-related secondary metabolism. Thus, the dynamical modelling approach, which considers variable source and sink strengths of plant internal resources within different phenological growth stages, presents a successful translation of existing concepts into a dynamic mathematical model.     

Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber

MOTIVATION: Because of the complexity of metabolic networks and their regulation, formal modelling is a useful method to improve the understanding of these systems. An essential step in network modelling is to validate the network model. Petri net theory provides algorithms and methods, which can be applied directly to metabolic network modelling and analysis in order to validate the model. The metabolism between sucrose and starch in the potato tuber is of great research interest. Even if the metabolism is one of the best studied in sink organs, it is not yet fully understood. RESULTS: We provide an approach for model validation of metabolic networks using Petri net theory, which we demonstrate for the sucrose breakdown pathway in the potato tuber. We start with hierarchical modelling of the metabolic network as a Petri net and continue with the analysis of qualitative properties of the network. The results characterize the net structure and give insights into the complex net behaviour.     

Topological analysis of metabolic networks based on Petri net theory

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.     

A systems biology approach for the analysis of carbohydrate dynamics during acclimation to low temperature in Arabidopsis thaliana

Low temperature is an important environmental factor affecting the performance and distribution of plants. During the so-called process of cold acclimation, many plants are able to develop low-temperature tolerance, associated with the reprogramming of a large part of their metabolism. In this study, we present a systems biology approach based on mathematical modelling to determine interactions between the reprogramming of central carbohydrate metabolism and the development of freezing tolerance in two accessions of Arabidopsis thaliana. Different regulation strategies were observed for (a) photosynthesis, (b) soluble carbohydrate metabolism and (c) enzyme activities of central metabolite interconversions. Metabolism of the storage compound starch was found to be independent of accession-specific reprogramming of soluble sugar metabolism in the cold. Mathematical modelling and simulation of cold-induced metabolic reprogramming indicated major differences in the rates of interconversion between the pools of hexoses and sucrose, as well as the rate of assimilate export to sink organs. A comprehensive overview of interconversion rates is presented, from which accession-specific regulation strategies during exposure to low temperature can be derived. We propose this concept as a tool for predicting metabolic engineering strategies to optimize plant freezing tolerance. We confirm that a significant improvement in freezing tolerance in plants involves multiple regulatory instances in sucrose metabolism, and provide evidence for a pivotal role of sucrose-hexose interconversion in increasing the cold acclimation output.     

Systems biotechnology for strain improvement

Various high-throughput experimental techniques are routinely used for generating large amounts of omics data. In parallel, in silico modelling and simulation approaches are being developed for quantitatively analyzing cellular metabolism at the systems level. Thus informative high-throughput analysis and predictive computational modelling or simulation can be combined to generate new knowledge through iterative modification of an in silico model and experimental design. On the basis of such global cellular information we can design cells that have improved metabolic properties for industrial applications. This article highlights the recent developments in these systems approaches, which we call systems biotechnology, and discusses future prospects.     

Modelling dynamic plant cells

A major challenge in plant biology is to understand how functions in plant cells emerge from interactions between molecular components. Computational and mathematical modelling can encapsulate the relationships between molecular components and reveal how biological functions emerge. We review recent progress in modelling in metabolism, growth, signalling and circadian rhythms in plant cells. We discuss challenges and opportunities for future directions.     

CFD simulation of flow through heart: a perspective review

The heart is an organ which pumps blood around the body by contraction of muscular wall. There is a coupled system in the heart containing the motion of wall and the motion of blood fluid; both motions must be computed simultaneously, which make biological computational fluid dynamics (CFD) difficult. The wall of the heart is not rigid and hence proper boundary conditions are essential for CFD modelling. Fluid-wall interaction is very important for real CFD modelling. There are many assumptions for CFD simulation of the heart that make it far from a real model. A realistic fluid-structure interaction modelling the structure by the finite element method and the fluid flow by CFD use more realistic coupling algorithms. This type of method is very powerful to solve the complex properties of the cardiac structure and the sensitive interaction of fluid and structure. The final goal of heart modelling is to simulate the total heart function by integrating cardiac anatomy, electrical activation, mechanics, metabolism and fluid mechanics together, as in the computational framework.     

Current status and challenges in connecting models of erythrocyte metabolism to experimental reality

Detailed kinetic models of human erythrocyte metabolism have served to summarize the vast literature and to predict outcomes from laboratory and “Nature's” experiments on this simple cell. Mathematical methods for handling the large array of nonlinear ordinary differential equations that describe the time dependence of this system are well developed, but experimental methods that can guide the evolution of the models are in short supply. NMR spectroscopy is one method that is non-selective with respect to analyte detection but is highly specific with respect to their identification and quantification. Thus time courses of metabolism are readily recorded for easily changed experimental conditions. While the data can be simulated, the systems of equations are too complex to allow solutions of the inverse problem, namely parameter-value estimation for the large number of enzyme and membrane-transport reactions operating in situ as opposed to in vitro. Other complications with the modelling include the dependence of cell volume on time, and the rates of membrane transport processes are often dependent on the membrane potential. These matters are discussed in the light of new modelling strategies.     

Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.     

系统生物技术在工业(药用)微生物菌种选育与高通量筛选中的应用

系统生物学时代,各种高通量组学技术产生了大量数据。一些旨在挖掘数据和整合信息的计算机建模技术也逐渐用于系统水平定量分析细胞代谢。模型有助于指导实验设计,实验结果反过来检验和优化模型,虚实结合,有利于在系统层面认识复杂的代谢过程。根据这些信息,可以设计、优化工业微生物代谢特征,高表达目标代谢物。本文综述了系统生物技术在工业(药用)微生物育种和高通量筛选中的最新应用进展。     

Molecular modelling of human CYP1B1 substrate interactions and investigation of allelic variant effects on metabolism

Molecular modelling of human CYP1B1 based on homology with the mammalian P450, CYP2C5, of known three-dimensional structure is reported. The enzyme model has been used to investigate the likely mode of binding for selected CYP1B1 substrates, particularly with regard to the possible effects of allelic variants of CYP1B1 on metabolism. In general, it appears that the CYP1B1 model is consistent with known substrate selectivity for the enzyme, and the sites of metabolism can be rationalized in terms of specific contacts with key amino acid residues within the CYP1B1 heme locus. Furthermore, a mode of binding interaction for the inhibitor, alpha-naphthoflavone, is presented which accords with currently available information. The current paper shows that a combination of molecular modelling and experimental determinations on the substrate metabolism for CYP1B1 allelic variants can aid in the understanding of structure-function relationships within P450 enzymes.     

A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress

Background  

In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.     

Construction and Analysis of the Model of Energy Metabolism in E. coli

Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli’s energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values) and v (flux values) was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that (free energy rate input from the environment) can meet the demand of (free energy rate dissipated by chemical process) and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.     

A Multiscale Approach to Modelling Drug Metabolism by Membrane-Bound Cytochrome P450 Enzymes

Cytochrome P450 enzymes are found in all life forms. P450s play an important role in drug metabolism, and have potential uses as biocatalysts. Human P450s are membrane-bound proteins. However, the interactions between P450s and their membrane environment are not well-understood. To date, all P450 crystal structures have been obtained from engineered proteins, from which the transmembrane helix was absent. A significant number of computational studies have been performed on P450s, but the majority of these have been performed on the solubilised forms of P450s. Here we present a multiscale approach for modelling P450s, spanning from coarse-grained and atomistic molecular dynamics simulations to reaction modelling using hybrid quantum mechanics/molecular mechanics (QM/MM) methods. To our knowledge, this is the first application of such an integrated multiscale approach to modelling of a membrane-bound enzyme. We have applied this protocol to a key human P450 involved in drug metabolism: CYP3A4. A biologically realistic model of CYP3A4, complete with its transmembrane helix and a membrane, has been constructed and characterised. The dynamics of this complex have been studied, and the oxidation of the anticoagulant R-warfarin has been modelled in the active site. Calculations have also been performed on the soluble form of the enzyme in aqueous solution. Important differences are observed between the membrane and solution systems, most notably for the gating residues and channels that control access to the active site. The protocol that we describe here is applicable to other membrane-bound enzymes.     

Something from nothing: bridging the gap between constraint-based and kinetic modelling

Two divergent modelling methodologies have been adopted to increase our understanding of metabolism and its regulation. Constraint-based modelling highlights the optimal path through a stoichiometric network within certain physicochemical constraints. Such an approach requires minimal biological data to make quantitative inferences about network behaviour; however, constraint-based modelling is unable to give an insight into cellular substrate concentrations. In contrast, kinetic modelling aims to characterize fully the mechanics of each enzymatic reaction. This approach suffers because parameterizing mechanistic models is both costly and time-consuming. In this paper, we outline a method for developing a kinetic model for a metabolic network, based solely on the knowledge of reaction stoichiometries. Fluxes through the system, estimated by flux balance analysis, are allowed to vary dynamically according to linlog kinetics. Elasticities are estimated from stoichiometric considerations. When compared to a popular branched model of yeast glycolysis, we observe an excellent agreement between the real and approximate models, despite the absence of (and indeed the requirement for) experimental data for kinetic constants. Moreover, using this particular methodology affords us analytical forms for steady state determination, stability analyses and studies of dynamical behaviour.     

Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall

The present study gives a critique of the mechanisms involved with the formation of volatile fatty acid (VFA) formed in the lumen of the reticulo-rumen, the absorption of VFA across the reticulo-rumen wall, and the intra-epithelial metabolism of VFA by reticulo-rumen epithelium. In contrast to the empirical treatment of these aspects in previous rumen modelling studies, a mechanistic model was developed which represents each of these aspects separately. Because tissues of the reticulo-rumen may strongly adapt to changing nutritional conditions, this adaptive response was included in the model. The model enabled an evaluation of the implications of VFA yield on the development of the rumen wall, on the transport of VFA, on the extent of intra-epithelial metabolism of VFA, and on the consequences for the supply of VFA to the ruminant. The current modelling effort allowed the integration of existing knowledge on each of these aspects and the model reproduced some essential characteristics of experimental observations on VFA absorption and metabolism. Although further development is still needed, the model appears helpful to distinguish elements that require specific consideration when evaluating rates of net portal appearance of VFA, or when testing hypothesis on the interaction between formation, absorption and intra-epithelial metabolism of VFA under various experimental conditions.     

In vitro methods in the prediction of kinetics of drugs: focus on drug metabolism

The absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of a candidate drug influence its final clinical success. These properties have traditionally been evaluated by using various in vivo animal approaches, but recently, a number of in vitro and in silico methods have been introduced to determine key ADMET features. Basic events, such as absorption through the gut wall, binding to plasma proteins, active and passive transfer through the blood-brain barrier, and various metabolic parameters, can now be screened with rapid in vitro and computer modelling methods. The focus in this short review is on the basic in vitro and in silico methods that are used for studying the metabolism properties of new drug molecules.