Genome-scale models of microbial cells: evaluating the consequences of constraints

Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.     

Genome-scale models of bacterial metabolism: reconstruction and applications

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities.     

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

ABSTRACT: BACKGROUND: Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. METHODS: A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC794) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allows solutions to be found through standard linear programming. RESULTS: Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetylacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. CONCLUSIONS: FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.     

Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production

In the post-genome era, it is one challenge to understand the cellular metabolism at the systematic levels. Mathematical modeling of microorganisms and subsequent computer simulation are effective tools for systems biology. In this paper, based on the genome-scale Escherichia coli stoichiometric model iJR904, through the GAMS linear programming package, the in silico maximal succinate yield was estimated to be 1.714 mol/mol glucose. When another two constraints were added, the maximal succinate yield dropped to 1.60 mol/mol glucose. Further analysis substantiated the uniqueness of the flux distribution under such constraints. After comparisons with the metabolic flux analysis (MFA) results computed from the wet experimental data of the three kinds of E. coli, three potential improvement target sites, the glucose phosphotransferase transport system, the pyruvate carboxylase, and the glyoxylate shunt, were identified and selected for the genetic modifications. All the three genetic modified strains showed increased succinate yield. The final strain TUQ19/pQZ6 had a high yield of 1.29 mol succinate/mol glucose and high productivity. The success of the above experiments proved that this in silico optimal succinate production pathway is reasonable and practical. This method may also be used as a general strategy to help enhance the yields of other favorable metabolites in E. coli.     

Natural computation meta-heuristics for the in silico optimization of microbial strains

Background  

One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.     

Translational models of tumor angiogenesis: A nexus of in silico and in vitro models

Emerging evidence shows that endothelial cells are not only the building blocks of vascular networks that enable oxygen and nutrient delivery throughout a tissue but also serve as a rich resource of angiocrine factors. Endothelial cells play key roles in determining cancer progression and response to anti-cancer drugs. Furthermore, the endothelium-specific deposition of extracellular matrix is a key modulator of the availability of angiocrine factors to both stromal and cancer cells. Considering tumor vascular network as a decisive factor in cancer pathogenesis and treatment response, these networks need to be an inseparable component of cancer models. Both computational and in vitro experimental models have been extensively developed to model tumor-endothelium interactions. While informative, they have been developed in different communities and do not yet represent a comprehensive platform. In this review, we overview the necessity of incorporating vascular networks for both in vitro and in silico cancer models and discuss recent progresses and challenges of in vitro experimental microfluidic cancer vasculature-on-chip systems and their in silico counterparts. We further highlight how these two approaches can merge together with the aim of presenting a predictive combinatorial platform for studying cancer pathogenesis and testing the efficacy of single or multi-drug therapeutics for cancer treatment.     

GST profile expression study in some selected plants: in silico approach

Glutathione acts as a protein disulfide reductant, which detoxifies herbicides by conjugation, either spontaneously or by the activity of one of a number of glutathione-S-transferases (GSTs), and regulates gene expression in response to environmental stress and pathogen attack. GSTs play role in both normal cellular metabolism as well as in the detoxification of a wide variety of xenobiotic compounds, and they have been intensively studied with regard to herbicide detoxification in plants. A newly discovered plant GST subclass has been implicated in numerous stress responses, including those arising from pathogen attack, oxidative stress, and heavy-metal toxicity. In addition, plant GSTs play a role in the cellular response to auxins and during the normal metabolism of plant secondary products like anthocyanins and cinnamic acid. The present study involves two in silico analytical approaches—general secondary structure prediction studies of the proteins and detailed signature pattern studies of some selected GST classes in Arabdiopsis thaliana, Mustard, Maize, and Bread wheat by standard Bioinformatics tools; structure prediction tools; signature pattern tools; and the evolutionary trends were analyzed by ClustalW. For this purpose, sequences were obtained from standard databases. The study reveals that these proteins are mainly alpha helical in nature with specific signature pattern similar to phosphokinase C, tyrosine kinase, and casein kinase II proteins, which are closely related to plant oxidative stress. This study aims to comprehend the relationship of GST gene family and plant oxidative stress with respect to certain specific conserved motifs, which may help in future studies for screening of biomodulators involved in plant stress metabolism.     

GST profile expression study in some selected plants: in silico approach

Glutathione acts as a protein disulphide reductant, which detoxifies herbicides by conjugation, either spontaneously or by the activity of one of a number of glutathione-S-transferases (GSTs), and regulates gene expression in response to environmental stress and pathogen attack. GSTs play roles in both normal cellular metabolisms as well as in the detoxification of a wide variety of xenobiotic compounds, and they have been intensively studied with regard to herbicide detoxification in plants. A newly discovered plant GST subclass has been implicated in numerous stress responses, including those arising from pathogen attack, oxidative stress and heavy-metal toxicity. In addition, plants GSTs play a role in the cellular response to auxins and during the normal metabolism of plant secondary products like anthocyanins and cinnamic acid. The present work involves two in silico analytical approaches—general secondary structure prediction studies of the proteins and detailed signature pattern studies of some selected GST classes in Arabdiopsis thaliana, mustard, maize and bread wheat by standard Bioinformatics tools; structure prediction tools; signature pattern tools; and the evolutionary trends were analyzed by ClustalW. For this purpose, sequences were obtained from standard databases. The work reveals that these proteins are mainly alpha helical in nature with specific signature pattern similar to phosphokinase C, tyrosine kinase and casein kinase II proteins, which are closely related to plant oxidative stress. This study aims to comprehend the relationship of GST gene family and plant oxidative stress with respect to certain specific conserved motifs, which may help in future studies for screening of biomodulators involved in plant stress metabolism.     

177 T-cell vaccine design for Streptococcus pneumoniae: an in silico approach

Streptococcus pneumoniae (pneumococcus) remains an important cause of meningitis, bacteremia, acute otitis media, community acquired pneumonia associated with significant morbidity, and mortality world wide. Conjugated polysaccharide, glycoconjugated, and capsular polysaccharide based vaccines were existent for pneumococcal disease but are still specific and restricted to serotypes of S. pneumoniae. Proteome of eight serotypes of S. pneumoniae was retrieved and identified in common proteins (Munikumar et al., 2012). 18 membrane proteins were distinguished from 1657 common proteins of eight serotypes of S. pneumoniae. Implementing comparative genomic approach and subtractive genomic approach, three membrane proteins were predicted as essential for bacterial survival and non-homologous to human (Munikumar et al., 2012; Umamaheswari et al., 2011). ProPred server was used to propose four promiscuous T-cell epitopes from three membrane proteins and validated through published positive control, SYFPEITHI and immune epitope database (Munikumar et al., in press). The four epitopes docked into peptide binding region of predominant HLA-DRB alleles with good binding affinity in Maestro v9.2. The T-cell epitope 89-VVYLLPILI-97 and HLA-DRB5^?0101 docking complex was with best XPG score (?13.143?kcal/mol). Further, the stability of the complex was checked through molecular dynamics simulations in Desmond v3.3. The simulation results had revealed that the complex was stable throughout 5000?ps (Munikumar et al., in press). Thus, the epitope would be the ideal candidate for T-cell driven subunit vaccine design against selected serotypes of S. pneumoniae.     

Molecular dynamics simulations of RNA: an in silico single molecule approach

RNA molecules are now known to be involved in the processing of genetic information at all levels, taking on a wide variety of central roles in the cell. Understanding how RNA molecules carry out their biological functions will require an understanding of structure and dynamics at the atomistic level, which can be significantly improved by combining computational simulation with experiment. This review provides a critical survey of the state of molecular dynamics (MD) simulations of RNA, including a discussion of important current limitations of the technique and examples of its successful application. Several types of simulations are discussed in detail, including those of structured RNA molecules and their interactions with the surrounding solvent and ions, catalytic RNAs, and RNA-small molecule and RNA-protein complexes. Increased cooperation between theorists and experimentalists will allow expanded judicious use of MD simulations to complement conceptually related single molecule experiments. Such cooperation will open the door to a fundamental understanding of the structure-function relationships in diverse and complex RNA molecules. .     

New experimental models in microbial ecology

Peculiar features of dysbiosis development in persons under extreme conditions were studied. It was shown that a number of extreme factors participated in formation of dysbiotic disorders in intestinal microflora. Of paramount importance was the neuro-emotional stress. Lability of bifido- and lactoflora was considered as the starting mechanism in dysbacteriosis under the extreme conditions. In the experimental models with rats SPF and Primates during flights of biosatellites of the Kosmos series the role of indigenous++ microflora in maintaining the microecological homeostasis, as well as the need for development of artificial and controlled intestinal microflora promising in prophylaxis of dysbacteriosis under extreme conditions was shown. The theoretical and experimentally grounded necessity of maintaining constant intestine microbiocenosis was confirmed by the practice of using the system of measures for recovery, stabilization and optimization of microflora in persons under extreme conditions.     

An in silico modeling approach to understanding the dynamics of sarcoidosis

Background

Sarcoidosis is a polygenic disease with diverse phenotypic presentations characterized by an abnormal antigen-mediated Th1 type immune response. At present, progress towards understanding sarcoidosis disease mechanisms and the development of novel treatments is limited by constraints attendant to conducting human research in a rare disease in the absence of relevant animal models. We sought to develop a computational model to enhance our understanding of the pathological mechanisms of and predict potential treatments of sarcoidosis.

Methodology/Results

Based upon the literature, we developed a computational model of known interactions between essential immune cells (antigen-presenting macrophages, effector and regulatory T cells) and cytokine mediators (IL-2, TNFα, IFNγ) of granulomatous inflammation during sarcoidosis. The dynamics of these interactions are described by a set of ordinary differential equations. The model predicts bistable switching behavior which is consistent with normal (self-limited) and “sarcoidosis-like” (sustained) activation of the inflammatory components of the system following a single antigen challenge. By perturbing the influence of model components using inhibitors of the cytokine mediators, distinct clinically relevant disease phenotypes were represented. Finally, the model was shown to be useful for pre-clinical testing of therapies based upon molecular targets and dose-effect relationships.

Conclusions/Significance

Our work illustrates a dynamic computer simulation of granulomatous inflammation scenarios that is useful for the investigation of disease mechanisms and for pre-clinical therapeutic testing. In lieu of relevant in vitro or animal surrogates, our model may provide for the screening of potential therapies for specific sarcoidosis disease phenotypes in advance of expensive clinical trials.     

Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors

P-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET properties of drugs and drug candidates. Substrates of P-gp are not only subject to multidrug resistance (MDR) in tumor therapy, they are also associated with poor pharmacokinetic profiles. In contrast, inhibitors of P-gp have been advocated as modulators of MDR. However, due to the polyspecificity of P-gp, knowledge on the molecular basis of ligand-transporter interaction is still poor, which renders the prediction of whether a compound is a P-gp substrate/non-substrate or an inhibitor/non-inhibitor quite challenging. In the present investigation, we used a set of fingerprints representing the presence/absence of various functional groups for machine learning based classification of a set of 484 substrates/non-substrates and a set of 1935 inhibitors/non-inhibitors. Best models were obtained using a combination of a wrapper subset evaluator (WSE) with random forest (RF), kappa nearest neighbor (kNN) and support vector machine (SVM), showing accuracies >70%. Best P-gp substrate models were further validated with three sets of external P-gp substrate sources, which include Drug Bank (n=134), TP Search (n=90) and a set compiled from literature (n=76). Association rule analysis explores the various structural feature requirements for P-gp substrates and inhibitors.     

Novel matrix metalloproteinase inhibitors: generation of lead compounds by the in silico fragment-based approach

Generation of structurally new matrix metalloproteinase inhibitors was successfully carried out using an in silico technique. In order to identify the small fragment interacting with residues in the S1' pocket of MMP-1 through hydrogen bonds, we performed in silico screening using the LUDI program. As a result, acetyl-L-alanyl-(N-methyl)amide (Ac-L-Ala-NHMe) was selected to link with another fragment, hydroxamic acid that interacted with catalytic zinc. By this approach, the L-glutamic acid derivative 2b was discovered to be a new type of matrix metalloproteinase inhibitor. Further transformation to reduce its peptidic nature and improve activity yielded nonpeptidic lead compounds as inhibitors of MMP-1, -2, -3, and -9.     

Exploring the binding properties of agonists interacting with glucocorticoid receptor: an in silico approach

The glucocorticoid receptors (GR) are members of the nuclear receptor superfamily that regulate growth, development, and many of the biological functions, including metabolism and inflammation, in a ligand dependent behavior. Thus, GRs are vital as therapeutic targets with steroid hormones and steroidal analogues, especially including the glucocorticoids. Studying the molecular mechanism of binding between GR and ligands is fundamentally important to develop applications in the pharmacological industry. The present study was carried out via molecular docking and molecular dynamic (MD) simulations of three GR-ligand complexes formed between the ligand binding domain (LBD) of GR with cortisol (a natural steroid), dexamethasone (a well-known synthetic steroid drug), and chonemorphine (a steroid virtually screened from the “Sri Lankan Flora” web-based information system). The investigation was mainly carried out in terms of macroscopic properties of the ligand-protein interactions and conformational fluctuations of the protein. The results indicated greater stability and a similar behavior of the GR protein in the chonemorphine-GR complex, compared to the other two complexes, GR-dexamethasone and GR-cortisol, in an aqueous medium. The integrity of the protein-substrate complexes was preserved by strong hydrogen bonds formed between the amino acid residues of the binding site of the proteins and ligands. The findings revealed that chonemorphine is a promising agonist to GR and may produce a pharmacological effect like that produced by glucocorticoids. Thus, the obtained knowledge could lead to further investigations of the pharmaceutical potential of chonemorphine and biological functions of GR in vivo.