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Comparative reduced kidney model evaluation. ( A ) Overlap of gene activity predictions with genes expressing above the significance threshold. Regions of the diagram are approximately proportional to their associated set sizes. The magenta circle represents the set of genes predicted active in the reduced kidney model. The cyan circle represents the set of Recon1-associated genes with expression levels above the significance threshold in the kidney tissue data. The yellow circle represents the set of genes encoding proteins that were detected in normal human
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Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for e...
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The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly...
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... Genome scale metabolic network models (GeMs) are mathematical reconstructions of all metabolic reactions encoded in an organism's genome and can be used to simulate specific growth conditions and analyse the resulting cellular phenotype (Orth, Thiele and Palsson, 2010). Additional layers of mechanistic and regulatory information can be incorporated into such models by complementing the reconstructed network with other -omics data like metabolomics, transcriptomics and proteomics (Chang et al., 2010;Lewis et al., 2010;Bonde et al., 2011). Cellular metabolism simulated in a GeM is assumed to be in a quasi-steady state whereby the total sum of any compound being produced must equal the total sum being consumed, resulting in no net accumulation or depletion of metabolites. ...
Industrial biotechnology is currently synonymous with heterotrophic processes that rely on bacterial, yeast, insect or mammalian cells to biosynthesise products of interest. Microalgae are of substantial biotechnological interest due their polyphyletic nature which grants them access to a wide array of high-value metabolites and their ability to grow under a variety of trophic strategies, including phototrophy. Despite significant process development and optimisation efforts, the full potential of these photosynthetic organisms has yet to be realised. One of the most impactful process parameters when cultivating microalgae is light. It is essential for phototrophic growth and remains highly influential on mixotrophic growth. Indoor cultivations relying on artificial light allow full control of illumination conditions. The advent of LED lights has lowered the costs and improved the flexibility of such installations. Specifically, the spectral composition of LED lights can be accurately and dynamically tailored to the needs of the culture. Spectral composition is known to exert regulatory control over the cell cycle and can affect the cell’s biochemical make up. The effects of illumination strategy on the model microalgae Chlamydomonas reinhardtii were characterised at three different levels (a) growth kinetics, (b) biochemical composition and, (c) transcriptional activity at key carbon nodes. To obtain the transcriptional data, RNA extraction protocols were compared and optimised. Additionally, a suite of candidate reference genes was validated to ensure accurate gene expression normalisation was possible in reverse transcriptase quantitative real-time polymerase chain reaction (RT-qPCR) studies. The growth kinetics and biochemical composition data obtained served as inputs for a previously published genome scale metabolic model. An algorithm was developed to approximate the default biomass composition in the model to experimental data in an effort to increase the fidelity of the simulations. The flux distributions obtained thereafter helped to describe the distinct metabolic fingerprints created under different trophic and illumination strategies.
... For example, in the core E. coli metabolic model, our method is able to correctly reflect important biological features such as the transition from glycolysis to gluconeogenesis, overflow metabolism, and the effects of anoxia. Such context-dependent networks can be used to analyse the properties of metabolism in a wide variety of scenarios; for example, to find metabolic conditions in which a drug or a combination of drugs may be maximally effective [36][37][38][39]. In addition, these networks can be easily adapted to a multi-layer network setting [40] (e.g., modifying equation (12) to obtain an m × m × n adjacency tensor) to study the contribution by each metabolite to the network. ...
Cells adapt their metabolic fluxes in response to changes in the environment. We present a systematic flux-based framework for the construction of graphs to represent organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via links that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes as probabilities, or tailored to different environmental conditions by incorporating flux distributions computed from constraint-based modelling such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and study the derived graphs under various growth conditions. The results reveal drastic changes in the topological and community structure of the metabolic graphs, which capture the re-routing of metabolic fluxes under each growth condition. By integrating constraint-based models and tools from network science, our framework allows for the interrogation of environment-specific metabolic responses beyond fixed, standard pathway descriptions.
... Others have explored host/pathogen interactions [9], cocultures and microbial communities10111213, ecology [14], and chemotaxis [15]. Numerous recent developments have broadened the predictive scope of genome-scale models by incorporating other sources of biological data, such as protein structural data, into reconstructions [7, 16, 17]. The complementarity of molecular-level and systemslevel data types has led to the integration of protein structurally-derived data into genome-scale models. ...
... Such a multi-scale approach acts as bridge between systems biology and structural biology, two scientific disciplines that, when combined, become the emerging field of structural systems biology1819202122. This union has brought about exciting advances, which would have otherwise been out of reach: the evolution of fold families in metabolism [7] , identification of causal off target actions of drugs [16], identification of protein-protein interactions [23, 24] , and determination of causal mutations for disease susceptibility [24, 25]. In recent years, the number of publicly available biological macromolecule structures has grown to more than 110,000 entries, and continues to increase yearly by roughly 10 % [26]. ...
Background:
The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.
Results:
Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/).
Conclusions:
Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.
... 26 Just as docking can be used to discover new ligands binding to a given target and to predict binding of substrates and products to enzymes in a biochemical pathway, docking approaches can also be used to predict off-target binding of drug candidates. Following the pioneering work of other groups in this field, [27][28][29][30] we are developing ensemble-based docking as a predictive technology platform to red-flag drug candidates for their potential to bind off-target to proteins known to be involved in most clinical failures. This approach is different from cheminformatics/ligand-based predictions in that it requires no parameters other than those used in the parametrization docking scoring functions, and is hence amenable to use in the case of novel molecules for which little chemical similarity to known drugs is available. ...
This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.
... Others have explored host/pathogen interactions [9], cocultures and microbial communities10111213, ecology [14], and chemotaxis [15]. Numerous recent developments have broadened the predictive scope of genome-scale models by incorporating other sources of biological data, such as protein structural data, into reconstructions [7, 16, 17]. The complementarity of molecular-level and systemslevel data types has led to the integration of protein structurally-derived data into genome-scale models. ...
... Such a multi-scale approach acts as bridge between systems biology and structural biology, two scientific disciplines that, when combined, become the emerging field of structural systems biology1819202122. This union has brought about exciting advances, which would have otherwise been out of reach: the evolution of fold families in metabolism [7] , identification of causal off target actions of drugs [16], identification of protein-protein interactions [23, 24] , and determination of causal mutations for disease susceptibility [24, 25]. In recent years, the number of publicly available biological macromolecule structures has grown to more than 110,000 entries, and continues to increase yearly by roughly 10 % [26]. ...
... Hence, numerous studies have been dedicated to generating tissue specific or cell specific models of metabolism [20, 21]. These tissue-specific reconstructions can be built by piecing together the model based on previously established biological evidence obtained by reviewing the literature2223242526, through the integration of omics data and computational methods in order to tailor generic, published human reconstructions [5, 9,272829 to the desired cell type [15, 16,30313233, or through a combination of computational algorithms and manual curation [27, 28,343536. Automated tissue-specific reconstruction algorithms developed to date can be broadly categorized into two groups [20] : " flux-dependent " and " pruning " methods. ...
Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.
... 1 7 inhibitors regulate ROS-generating enzymes remains unclear, but it may be possible that they have binding sites that target oxidases, as suggested by computational analysis (Chang et al., 2010). In addition to influencing ROS production, CETP inhibitors affected activation of STAT3, a transcription factor involved in VSMC activation, since we found that the MLC phosphorylation induced by CETP inhibitors was reduced by STAT3 inhibition. ...
Elevated blood pressure was an unexpected outcome in some cholesteryl ester transfer protein (CETP) inhibitor (CETPI) trials, possibly due to vascular effects of these drugs. We investigated whether CETPIs (torcetrapib, dalcetrapib, anacetrapib) influence vascular function and explored putative underlying molecular mechanisms. Resistance arteries and vascular smooth muscle cells (VSMC) from rats, which lack the CETP gene, were studied. Contractile responses to phenylephrine were augmented by torcetrapib, dalcetrapib, and anacetrap. Only torcetrapib reduced endothelium-dependent vasorelaxation. CETPI effects were ameliorated by N-acetylcysteine (NAC) [reactive oxygen species (ROS) scavenger] and S3I-201 (STAT3 inhibitor). CETPI increased phosphorylation of vascular myosin light chain (pMLC) and myosin phosphatase target subunit 1 (p-MYPT1) (pro-contractile proteins) and stimulated ROS production. CETPI increased phosphorylation of STAT3, transcription factor important in cell activation. Activation of MLC was reduced by NAC, GKT137831 (Nox1/4 inhibitor), and S3I-201. Phosphorylation of STAT3 was unaffected by NAC and GKT137831. CETPI did not influence activation of mitogen-activated proteins kinases (MAPK) or c-Src. Our data demonstrate that CETP inhibitors influence vascular function and contraction through redox-sensitive and STAT3-dependent processes. These phenomena are CETP-independent, since CETP gene is absent in rats. Findings from our study indicate that CETPI have vasoactive properties, which may contribute to adverse cardiovascular effects of these drugs, such as hypertension.
... Finally, few virulence-related proteins have characterized ligands, even though their structures are readily available [7]. Previously, we developed a structural systems pharmacology approach, to identify drug-target interactions on a proteome scale by integrating proteome-wide ligand binding site comparison [11, 12], protein-ligand docking [13], and Molecular Dynamics (MD) simulation with systems biology modeling [7, 11,1415161718192021. Here, we apply this proven successful strategy to reveal FDA-approved and experimental drugs with the potential to inhibit the replication and virulence of Ebola. ...
... The structural systems pharmacology approach has been successfully applied to the prediction of side effect [15, 37], drug repurposing [10, 14, 38] , polypharmacology drug design161718 39], and other applications [12, 20, 40, 41]. Here we used the strategy to determine effective drugs which target Ebola virus. ...
The recent outbreak of Ebola has been cited as the largest in history. Despite this global health crisis, few drugs are available to efficiently treat Ebola infections. Drug repurposing provides a potentially efficient solution to accelerating the development of therapeutic approaches in response to Ebola outbreak. To identify such candidates, we use an integrated structural systems pharmacology pipeline which combines proteome-scale ligand binding site comparison, protein-ligand docking, and Molecular Dynamics (MD) simulation.
One thousand seven hundred and sixty-six FDA-approved drugs and 259 experimental drugs were screened to identify those with the potential to inhibit the replication and virulence of Ebola, and to determine the binding modes with their respective targets. Initial screening has identified a number of promising hits. Notably, Indinavir; an HIV protease inhibitor, may be effective in reducing the virulence of Ebola. Additionally, an antifungal (Sinefungin) and several anti-viral drugs (e.g. Maraviroc, Abacavir, Telbivudine, and Cidofovir) may inhibit Ebola RNA-directed RNA polymerase through targeting the MTase domain.
Identification of safe drug candidates is a crucial first step toward the determination of timely and effective therapeutic approaches to address and mitigate the impact of the Ebola global crisis and future outbreaks of pathogenic diseases. Further in vitro and in vivo testing to evaluate the anti-Ebola activity of these drugs is warranted.
... This validation step requires knowledge of the stoichiometry of the reactions and the ability to translate the network structure into a stoichiometric matrix to allow for flux calculation. Finally, the total inhibition of particular reactions can be tested and compared to experimentally known effects (Chang et al., 2010;Shlomi et al., 2009;Shlomi et al., 2008). ...
This chapter discusses the modelling of human metabolism using metabolic networks and how such models can be employed in the analysis of metabolomics data. The chapter first lists all potential human metabolic reactions and incorporates this list into a genome-scale network. A brief overview of available network repositories is presented. The chapter then shows how a network can be translated into a mathematical object called a graph. This structure constitutes a relevant framework for the analysis of untargeted metabolomics data. In fact, it can be used to interpret the metabolic relationships connecting a set of biomarkers. The application of graphs for the assessment of diet-based metabolic impacts is also demonstrated.
... Each cycle improves the accuracy, predictive ability, and therefore utility of metabolic models. Metabolic models have already been used to aid strain design for metabolic engineering (Lee et al., 2005;Perez Pulido et al., 2005;Park et al., 2011;Licona-Cassani et al., 2012), to analyze network properties (Almaas et al., 2005;Nam et al., 2012), and to provide context for the analysis of highthroughput omics data (Chandrasekaran and Price, 2010;Chang et al., 2010;van Berlo et al., 2011), and their role in such projects is anticipated to grow as the models are refined and simulate a larger number of biological conditions more accurately. ...
Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100-200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.
