Heuristics for simulated annealing search of active sub-networks in bio-molecular interaction networks

Different kinds of ‘omics’ data for several organisms and bio-molecular interaction networks (e.g. reconstructed networks of biochemical reactions and protein-protein physical interactions) are becoming very common nowadays. These bio-molecular networks are being used as a platform to integrate genome-scale ‘omics’ datasets. Identification of sub-networks in these large networks that show maximum collective response to a perturbation is one the interesting problems to solve by using an integrative analysis. Sub-networks can be hypothesized to represent significant collective biological activity due to the underlying interactions between the bio-molecules. The biological activity can be estimated in several ways- for example coordinated change in the expression level (e.g. mRNA). Identifying these regions reduce complexity of the network to be analyzed in greater detail by revealing the regions that are perturbed by a conditionremoving the interactions that are potentially false-positive and not related to the response under study. As the simulated annealing does not guarantee to find the global optimum and may lead to an incomplete picture of the biological phenomenon, we report a method to estimate the theoretical optimal score curve. The simulated annealing algorithm (SA) used in this study is a slightly modified version of the algorithm by Ideker et al.. Each node in the graph is associated with a binary variable turning the node visible or invisible and therefore inducing several sub-graphs. In the standard formulation, the initial solution is obtained by randomly attributing 0 or 1 to the nodes of the graph. Based in concepts described above, we propose an alternative initialization method to improve the performance of the simulated annealing algorithm.Systems Biology as a Driver for Industrial Biotechnology (SYSINBIO

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

Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes

One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype

Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes

If we tried to list every known chemical reaction within an organismhuman, plant or even bacteriawe would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular speciesprokaryotesto understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design.:This work was supported by grants from: the Fundac ¸ão para a Ciência e a Tecnologia (http:// www.fct.pt) with award number UID/BIO/04469/2013, the European Regional Development Fund (http://www.norte2020.pt) with award number NORTE-01-0145-FEDER-000004 (https://www. ceb.uminho.pt/Projects/Details/6040), Horizon 2020 (https://ec.europa.eu/programmes/ horizon2020) with award number 686070 (http:// dd-decaf.eu/) and COMPETE2020 with award number POCI-01-0145-FEDER-006684 to JCX and IR and the Fundação para a Ciência e a Tecnologia (http://www.fct.pt) with award number SFRH/BD/81626/2011 to JCX. The funders had no role in study design, data collectionand analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Evolutionary programming as a platform for in silico metabolic engineering.

BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems

Standardization and comparison of the biomass objective functions of manually curated genome-scale metabolic models

The biomass objective function (BOF) is an abstractive equation used in genome-scale constraint-based modelling (GS-CBM) to predict growth phenotypes. The BOF represents all the growth requirements upon cell division, which stoichiometric representation is ideally based on experimental measurements for cells growing in log phase (1). For growth rate calculations it is sufficient to know the macromolecular content of the cell, its detailed composition (amino acids, nucleotides and fatty acids.) and energetic costs of growth (2). However, to examine network essentiality another level of detail is required, which includes cofactors and ions and the analysis of which are the minimally essential biomass components (2) often called the core biomass (3, 4). There is no defined strategy in the literature for choosing which components are to be parts of a detailed BOF and the core BOF. In order to obtain a universal core prokaryotic BOF, we integrated BOFs of 71 genome-scale manually curated prokaryotic models, the ModelSEED framework for biomass composition (5) and data from the literature. We used a semi-automatic process to standardize the nomenclature of metabolites in the 71 BOFs, as there is still not a norm for the terminology of metabolites in GS- CBM. We found that the clustering of these 71 models based on their BOFs fails to represent the phylogenetic relationship of the modelled prokaryotes. No cofactor was present in all the BOFs analysed, including the important redox cofactors nicotinamide adenine dinucleotide (NAD) or NADPhosphate. Both the ModelSEED framework and other literature indicate some cofactors and many ions as universally essential. We conclude that not only the redox cofactors but also others as coenzyme A, flavins and thiamin might need to be added to the BOFs for improving future essentiality studies. We present a proposal of a set of cofactors for a universal core prokaryotic BOF

Towards a mechanistic understanding of reciprocal drug-microbiome interactions.

Broad-spectrum antibiotics target multiple gram-positive and gram-negative bacteria, and can collaterally damage the gut microbiota. Yet, our knowledge of the extent of damage, the antibiotic activity spectra, and the resistance mechanisms of gut microbes is sparse. This limits our ability to mitigate microbiome-facilitated spread of antibiotic resistance. In addition to antibiotics, non-antibiotic drugs affect the human microbiome, as shown by metagenomics as well as in vitro studies. Microbiome-drug interactions are bidirectional, as microbes can also modulate drugs. Chemical modifications of antibiotics mostly function as antimicrobial resistance mechanisms, while metabolism of non-antibiotics can also change the drugs' pharmacodynamic, pharmacokinetic, and toxic properties. Recent studies have started to unravel the extensive capacity of gut microbes to metabolize drugs, the mechanisms, and the relevance of such events for drug treatment. These findings raise the question whether and to which degree these reciprocal drug-microbiome interactions will differ across individuals, and how to take them into account in drug discovery and precision medicine. This review describes recent developments in the field and discusses future study areas that will benefit from systems biology approaches to better understand the mechanistic role of the human gut microbiota in drug actions