Towards a biologically relevant description of phenotypes based on pathway analysis

In metabolic systems, the cellular network of reactions together with constraints on reversibility of enzymes determine the space of all possible steady-state phenotypes. In actuality, the cell does not invoke the large majority of those in given conditions. We propose a method in two steps to obtain a more precise description of cellular phenotypes through pathway analysis. The first step is based on a modified version of the concept of control effective flux (CEF) [1] and only requires the stoichiometric network. The second step is based on thermodynamic feasibility of reactions and requires measurements of concentrations and thermodynamic properties of the metabolites. CEFs represent the importance of each reaction for efficient and flexible operation of the entire metabolic network. We modified the concept to take into account the reaction directionality within the modes by splitting up the reversible reactions. We observed that directionality of the largest CEF -forward reaction at least two times larger than backward or vice versa- matches well with the measured reaction directions for growth on glucose, glycerol, and acetate as the sole carbon source. We also found that the modified CEFs are good predictors of intra-cellular fluxes for the central carbon metabolism of Escherichia coli and Sacharomyces cerevisiae. The proposed method allows a reduction of up to 51% out of 2706 modes for E. coli and up to 81% out of 191,083 modes for S. cerevisiae, so that only pathways are contained that carry flux matching the measured directions. An alternative reduction can be obtained by assigning reaction directionalities on thermodynamic grounds using anNET [2] and removing the pathways that contain infeasible reactions. The feasibility of the remaining pathways was checked by taking into account irreversibility of the pathways. Depending on the available measurements and its uncertainties, a reduction of up to 31% in the computed pathways was obtained for particular conditions, though no further reduction compared to the CEFs method. Overall, the largest reduction in the number of pathways was obtained using the stiochiometric network as the only input, thus without the requirement for measurements, towards a biologically relevant description of phenotypes

Selection of thermodynamically feasible and active pathways

Although the network of metabolic reactions, together with constraints of (ir)reversibility of enzymes, determines the space of all potentially possible phenotypes, in actuality the cell does not invoke the large majority of those in given conditions. We propose a method in two steps to obtain a more accurate description of cellular phenotypes through pathway analysis. The first step requires measurements of the concentrations and thermodynamic properties of the metabolites; the second step measured exchange rates. The importance of this lays in the fact that the internal fluxes are not independently distributed but strictly constrained by external fluxes through the pathways. The case study considers the central carbon metabolism of Escherichia coli using five datasets from literature. As the pathway modelling method, we chose generating vectors (GVs), the smallest subset of pathways, instead of elementary modes, the largest set, because any steady state flux pattern can be expressed as a nonnegative linear combination of GVs and for its associated lower computational intensity [1]. First, we applied thermodynamics analysis [2] to check for thermodynamic inconsistencies in the dataset and model. We removed the blacklisted metabolites before further analysis. Then, we assigned reaction directionalities based on thermodynamic feasibilities (in addition to (ir)reversibilities of the enzymes) in given environmental conditions and recomputed the pathways (which is necessary since the set of GVs is not unique). Finally, feasibility of these pathways was tested taking into account irreversibility of the pathways as well. With this approach, a reduction of up to 61% in the computed pathways was obtained for particular phenotypes. Second, we used a controlled random search algorithm to select an active subset of feasible pathways that describes a particular phenotype based on exchange rates (oxygen uptake rate, carbon evolution rate, glucose uptake rate, specific growth rate, and acetate formation or consumption rate) [1]. The algorithm is based on an iterative search procedure and was run several times to find the active pathways. The original model containing 295 GVs could be reduced to a system of one to three pathways giving a good correlation with the measured datasets

Selection of elementary modes for bioprocess control

The lack of model-based information in bioreactor monitoring and control can have a profound impact on biological systems. We therefore aim to develop a model using elementary modes (EMs) that represents the observed phenotype in given environmental conditions suited for bioprocess control. Challenges in the model development were the high number of possible phenotypes of stoichiometric models and the high computational intensity. Two methods were compared to reduce the number of EMs to match the observed cellular phenotype. The first method is based on ranking modes and the second is a controlled random search (CRS) algorithm. Since we wish to obtain a biologically realistic subset of EMs, the objective function to be minimized is a trade-off between the error, efficiency of the modes, and model size. The case study considered the central carbon metabolism of Escherichia coli. The original model containing 2706 modes for case 1 and 11718 for case 2 was reduced to a system of one for case 1 and three modes for case 2 giving a good correlation with the measured data. Furthermore, considering also intracellular besides extracellular metabolites, results in a better fit of the measured rates. Finally, the CRS outperformed the ranking algorithm.(undefined

Systems biology, bioinformatics and metabolic engineering

This research line covers the topics of genome-scale metabolic model re-construction, biological text mining and metabolic engineering with the ultimate aims of designing improved cell factories for the application in industrial biotechnology processes and of improving our understanding of important human pathogens. In Metabolic Engineering problems, it is often difficult to predict the effects of genetic modifications on the resulting phenotype, owing to the complexity of metabolic networks. Consequently, the task of identifying the modifications that will lead to an improved microbial phenotype is a quite complex one, requiring robust mathematical and computational tools. Part of our effort is therefore dedicated to the generation of better mathematical models of microbial metabolism, applying Bioinformatics tools like Data Mining and Biological Text Mining. We have also developed algorithms for identifying gene knockouts that can improve productivities in strains using stoichiometric metabolic models and have been focusing our attention on the possibility of indicating other genetic modifications such as gene additions and over-expressions. Furthermore, we have developed an open-source software tool, called OptFlux, aiming at being the reference metabolic engineering platform. The tasks of model re-construction and the interpretation of the results obtained by in silico metabolic engineering approaches are difficult if not impossible to achieve without a proper contextualization with available literature information. Our main text mining software tool, called @Note, offers as major functional contributions the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a text mining module supporting dataset preparation and algorithm evaluation

Identification of metabolic engineering targets through pathway analysis

Given the complexity of metabolic networks, identification of optimal metabolic intervention strategies for redirecting fluxes towards desired products is a challenging task. Several algorithms based on linear programming and pathway analysis have been proposed. However, there is still a lack of an algorithmic framework that exploits the range of optimal and suboptimal routes and the structural/regulatory properties thereof. To this end, we are using a modified version of the concept “control effective flux (CEF)” [1] towards a novel algorithm for in silico metabolic engineering. CEFs represent the importance of each reaction for efficient and flexible operation of the entire metabolic network. We propose four modifications on the CEFs. First, as the absolute values are not comparable across networks, we apply normalization. Second, we use “minimal generating sets” to facilitate the use of large-scale networks. Third, we take into account the reaction directionality within the modes. Fourth, we only take into account biologically relevant modes. We show that CEFs are good predictors of intra-cellular fluxes in Escherichia coli and Sacharomyces cerevisiae. Next, we introduce our metabolic engineering algorithm where the objective is to identify deletion targets that increase the CEF of the desired flux. Our formulation leads to solutions that couple growth with product formation while considering optimal as well as sub-optimal routes and their efficiency

Online automatic tuning and control for fed-batch cultivation

Performance of controllers applied in biotechnological production is often below expectation. Online automatic tuning has the capability to improve control performance by adjusting control parameters. This work presents automatic tuning approaches for model reference specific growth rate control during fed-batch cultivation. The approaches are direct methods that use the error between observed specific growth rate and its set point; systematic perturbations of the cultivation are not necessary. Two automatic tuning methods proved to be efficient, in which the adaptation rate is based on a combination of the error, squared error and integral error. These methods are relatively simple and robust against disturbances, parameter uncertainties, and initialization errors. Application of the specific growth rate controller yields a stable system. The controller and automatic tuning methods are qualified by simulations and laboratory experiments with Bordetella pertussis

Random sampling of elementary flux modes in large-scale metabolic networks

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks

Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript