Understanding the host-microbe interactions using metabolic modeling

The human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health. [MediaObject not available: see fulltext.

A study on the robustness of strain optimization algorithms

5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011), ISBN 978-3-642-19913-4In recent years, there have been considerable advances in the use of genome-scale metabolic models to provide accurate phenotype simulation methods, which in turn enabled the development of efficient strain optimization algorithms for Metabolic Engineering. In this work, we address some of the limitations of previous studies regarding strain optimization algorithms, mainly its use of Flux Balance Analysis in the simulation layer.We perform a thorough analysis of previous results by relying on Flux Variability Analysis and on alternative methods for phenotype simulation, such as ROOM. This last method is also used in the simulation layer, as a basis for optimization, and the results obtained are also the target of thorough analysis and comparison with previous ones.Fundação para a Ciência e a Tecnologia (FCT) - project MIT-PT/BS-BB/0082/200

Time-periodic magnetic field analysis with saturation and hysteresis characteristics by harmonic balance finite element method

In this paper we shall further discuss the harmonic balance finite element method (HBFEM) for the time-periodic magnetic field with saturation and hysteresis characteristics and its applications. The HBFEM enables us to calculate the distribution of harmonic magnetic flux at an AC magnetization, and it dose not need the intricate calculation concerned with the time variation. Comparisons between numerical analysis and experimental results are presented

SBML Level 3 Package: Flux Balance Constraints ('fbc')

Constraint based modeling is a widely accepted methodology used to analyze and study biological networks on both a small and whole organism (genome) scale. Typically these models are underdetermined and constraint based methods (e.g. linear, quadratic optimization) are used to optimize specific model properties. This is assumed to occur under a defined set of constraints (e.g. stoichiometric, metabolic) and bounds (e.g. thermodynamic, experimental and environmental) on the values that the solution fluxes can obtain. Perhaps the most well known (and widely used) analysis method is Flux Balance Analysis (FBA; Orth et al., 2010) which is performed on Genome Scale Reconstructions (GSR’s; Oberhardt et al., 2009). Using FBA a target flux is optimized (e.g. maximizing a flux to biomass or minimizing ATP production) while other fluxes can be bounded to simulate a selected growth environment or specific metabolic state. As constraint based models are generally underdetermined, i.e. few or none of the kinetic rate equations and related parameters are known, it is crucial that a model definition includes the ability to define optimization parameters such as objective functions, flux bounds and constraints. Currently this is not possible in the Systems Biology Markup Language (SBML) Level 2 or Level 3 core specification (Hucka et al., 2011, 2003). The question of how to encode constraint based (also referred to as steady state or FBA) models in SBML is not new. However, advances in the methods used to construct genome scale constraint based models and the wider adoption of constraint based modeling in biotechnological/medical applications have led to a rapid increase in both the number of models being constructed and the tools used to analyze them. Faced with such growth, both in number and diversity, the need for a standardized data format for the definition, exchange and annotation of constraint based models has become critical. As the core model components (e.g. species, reactions, stoichiometry) can already be efficiently described in SBML (with its associated active community, software and tool support) the Flux Balance Constraints package aims to extend SBML Level 3 core by adding the elements necessary to encode current and future constraint based models

Flexible and user friendly tools for the incorporation of fluxomics data into metabolic models

The measurement of fluxes and the understanding of their control are at the core of Metabolic Engineering (ME). In this context, this work presents two integrated open- source software tools that allow to perform tasks of metabolic flux analysis (MFA). Both are platform independent, written in Java, and interact with the OptFlux framework [1], which also facilitates their communication (Figure 1). OptFlux is a modular open-source software that incorporates tools for strain optimization, i.e., the identification of ME targets. It also provides tools to use stoichiometric metabolic models for phenotype simulation of both wild-type and mutant organisms, using methods such as the well known Flux Balance Analysis (FBA). Graphical user interfaces are made available for every operation and to check the results that are obtained. Moreover, a network visualization system is offered, where simulation results can be added to overlap the network graph. The developed tools exploit OptFluxâ??s capabilities in terms of model interaction, simulation methods and visualization features. The first proposed software, named MetabolIc NEtwork Ratio AnaLysis (MiNeRAl) (Figure 1, bottom), aims at analyzing labeling experiments to infer flux constraints that for stoichiometric models. From a set of measurements of a 13C-labelling experiment, mass isotopomer distribution vectors (MDV) are calculated. If aminoacids are measured, the measured fragments, coupled with a carbon transition map provided by the user, are used to determine their precursors, and the corresponding MDVs are calculated. Based on the set of MDVs, the software uses the carbon transitions to determine the flux ratios that produce a given metabolite through the different pathways. These ratios are probabilistic equations that translate how the 13C-labeling pattern is distributed throughout the metabolic network [2]. Since the calculation of the flux ratios is independent of the flux distribution, this software can be used independently of other flux calculation processes, and the ratios can be further exploited to reduce the degrees of freedom of systems obtained in other MFA approaches [3,4]. The main differentiating characteristics of this tool are, besides being usr-friendly, the fact that it is generic for any type of metabolite fragmentation originating from GC-MS techniques and metabolic network topology. Furthermore, the software is also able to investigate what flux ratio constraints are possible to be inferred for a certain experiment beforehand. On the other hand, the second software application here described, jMFA (Figure 1, top), is focused on using different types of experimental flux data to constrain metabolic models and improve their predictions with a variety of tools. It allows users to define constraints associated with measured fluxes and/ or flux ratios, together with environmental conditions (e.g. media) and reaction/ gene knockouts. The application identifies the set of applicable methods based on the constraints defined from user inputs, allowing to select the desired approach, encompassing algebraic and constraint- based simulation methods (such as Flux Balance Analysis and its variants). Anytime a set of constraints is selected, the software calculates the degrees of freedom of the configured system, and updates the admissible methods depending on whether the system is underdetermined, determined or overdetermined, as shown in Figure 1. A method to perform robustness analysis is also implemented. The integration of jMFA within the OptFlux framework allows the use of different model formats and the integration with complementary methods for phenotype simulation and visualization of the results. Moreover, the flux ratio constraints can be obtained from previous calculations in MiNeRAl, or manually defined by the user. The first option provides a straightforward way to integrate both applications in a ME workflow

Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Finite volume schemes for diffusion equations: introduction to and review of modern methods

We present Finite Volume methods for diffusion equations on generic meshes, that received important coverage in the last decade or so. After introducing the main ideas and construction principles of the methods, we review some literature results, focusing on two important properties of schemes (discrete versions of well-known properties of the continuous equation): coercivity and minimum-maximum principles. Coercivity ensures the stability of the method as well as its convergence under assumptions compatible with real-world applications, whereas minimum-maximum principles are crucial in case of strong anisotropy to obtain physically meaningful approximate solutions