A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

BACKGROUND: Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. RESULTS: We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. CONCLUSION: Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production

Towards a UTP semantics for modelica

We describe our work on a UTP semantics for the dynamic systems modelling language Modelica. This is a language for modelling a system’s continuous behaviour using a combination of differential algebraic equations and an event-handling system. We develop a novel UTP theory of hybrid relations, inspired by Hybrid CSP and Duration Calculus, that is purely relational and provides uniform handling of continuous and discrete variables. This theory is mechanised in our Isabelle implementation of the UTP, Isabelle/UTP, with which we verify some algebraic properties. Finally, we show how a subset of Modelica models can be given semantics using our theory. When combined with the wealth of existing UTP theories for discrete system modelling, our work enables a sound approach to heterogeneous semantics for Cyber-Physical systems by leveraging the theory linking facilities of the UTP

Using the Functional Mockup Interface as an Intermediate Format in AUTOSAR Software Component Development

Abstract This paper shows how the recently developed Functional Mockup Interface (FMI) standard for model exchange can be utilized in the context of AUTO-SAR software component (SW-C) development. Automatic transformations between the XML schemas of the two standards are utilized to convert FMI models to AUTOSAR. An application example is demonstrated, where a Modelica controller is exported through FMI, converted to an AUTOSAR SW-C and then imported into an AUTOSAR tool. The presented approach, with FMI as an intermediate format, should be an attractive alternative to providing full-fledged AUTOSAR SW-C export

A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1

<p>Abstract</p> <p>Background</p> <p>Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms.</p> <p>Results</p> <p>We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.</p> <p>We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated.</p> <p>Conclusions</p> <p>We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (<it>Mus Musculus</it>, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening.</p

The DLR Robots library - Using replaceable packages to simulate various serial robots

In order to simulate different kinds of serial robots, the implementation of functionalities such as the calculation of their direct and inverse kinematics, visualization, collision behavior, etc. is necessary. However, providing these functionalities in robot specific models leads to additional modeling overhead in cases where one would like to switch between several different robot models. The DLR Robots library demonstrates an implementation of all robot specific components as replaceable Modelica packages, allowing for an user-friendly way to exchange robot models without modifying the general structure of the overlying model. The simulation of robotic systems is a great example for the multi-domain versatility of Modelica, combining multi-body mechanics with controllers, electric drives and algorithms, e.g. path-planning. A multitude of scientific works use Modelica to simulate a specific robot, providing models for the mechanics and all other components exclusively for the simulated robot. Other approaches use parameter sets to simulate more than one robot model within a given structure. However, both approaches lack flexibility regarding switching between different robot types in a Modelica model, e.g. if the number of axes is changing. In this case, instead of changing a parameter value, the complete model structure has to be altered and adapted for the new robot model. In this paper, a new approach to model robots in Modelica is presented. By separating the robot functionalities (e.g. visualization, dynamics, path-planning, etc.) from the model-based description of the robot itself, and wrapping the latter in a replaceable package, it becomes possible to switch between entirely different robot models without the need for changing the structure of the main model. This approach is inspired by the Modelica Media library, where different media provide their own functions and models, also wrapped in a replaceable package, enabling the user to switch easily between different media in a model. Utilizing the scripting language LUA, it becomes possible to control the simulated robots with a virtual robot controller. This enables the user to design complex robot programs and execute them in a Modelica simulation. Several applications of the library are shown, from feasibility studies of robotic systems to real-time path-planning algorithms for real robot arms

What is flux balance analysis?

matrix of stoichiometries-that consumes precursor metabolites at stoichiometries that simulate biomass production. The biomass reaction is based on experimental measurements of biomass components. This reaction is scaled so that the flux through it is equal to the exponential growth rate (µ) of the organism. Now that biomass is represented in the model, predicting the maximum growth rate can be accomplished by calculating the conditions that result in the maximum flux through the biomass reaction. In other cases, more than one reaction may contribute to the phenotype of interest. Mathematically, an &apos;objective function&apos; is used to quantitatively define how much each reaction contributes to the phenotype. Taken together, the mathematical representations of the metabolic reactions and of the objective define a system of linear equations. In flux balance analysis, these equations are solved using linear programming Suppose we want to calculate the maximum aerobic growth of E. coli under the assumption that uptake of glucose, and not oxygen, is the limiting constraint on growth. This calculation can be performed using a published model of E. coli metabolism 12 . In addition to metabolic reactions and the biomass reaction discussed above, this model also includes reactions that represent glucose and oxygen uptake into the cell. The assumptions are mathematically represented by setting the maximum rate of glucose uptake to a physiologically realistic level (18.5 mmol The core feature of this representation is a tabulation, in the form of a numerical matrix, of the stoichiometric coefficients of each reaction Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system. The matrix of stoichiometries imposes flux (that is, mass) balance constraints on the system, ensuring that the total amount of any compound being produced must be equal to the total amount being consumed at steady state From constraints to optimizing a phenotype The next step in FBA is to define a phenotype in the form of a biological objective that is relevant to the problem being studied In this primer, we illustrate the principles behind FBA by applying it to predict the maximum growth rate of Escherichia coli in the presence and absence of oxygen. The principles outlined can be applied in many other contexts to analyze the phenotypes and capabilities of organisms with different environmental and genetic perturbations (a Supplementary Tutorial provides ten additional worked examples with figures and computer code). Flux balance analysis is based on constraints The first step in FBA is to mathematically represent metabolic reactions What is flux balance analysis? Jeffrey D Orth, Ines Thiele &amp; Bernhard Ø Palsson Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations