Data-driven integration of genome-scale regulatory and metabolic network models

Pooled CRISPR Interference Screening Identifies Crucial Transcription Factors in Gas-Fermenting Clostridium ljungdahlii

Gas-fermenting Clostridium species hold tremendous promise for one-carbon biomanufacturing. To unlock their full potential, it is crucial to unravel and optimize the intricate regulatory networks that govern these organisms; however, this aspect is currently underexplored. In this study, we employed pooled CRISPR interference (CRISPRi) screening to uncover a wide range of functional transcription factors (TFs) in Clostridium ljungdahlii, a representative species of gas-fermenting Clostridium, with a special focus on TFs associated with the utilization of carbon resources. Among the 425 TF candidates, we identified 75 and 68 TF genes affecting the heterotrophic and autotrophic growth of C. ljungdahlii, respectively. We focused our attention on two of the screened TFs, NrdR and DeoR, and revealed their pivotal roles in the regulation of deoxyribonucleoside triphosphates (dNTPs) supply, carbon fixation, and product synthesis in C. ljungdahlii, thereby influencing the strain performance in gas fermentation. Based on this, we proceeded to optimize the expression of deoR in C. ljungdahlii by adjusting its promoter strength, leading to an improved growth rate and ethanol synthesis of C. ljungdahlii when utilizing syngas. This study highlights the effectiveness of pooled CRISPRi screening in gas-fermenting Clostridium species, expanding the horizons for functional genomic research in these industrially important bacteria.

Genetic epidemiology of resistance to M. tuberculosis Infection: importance of study design and recent findings

Resistance to M. tuberculosis, often referred to as "RSTR" in the literature, is being increasingly studied because of its potential relevance as a clinical outcome in vaccine studies. This review starts by addressing the importance of epidemiological characterization of this phenotype, and ongoing challenges in that characterization. Then, this review summarizes the extant genetic and genomic studies of this phenotype, including heritability studies, candidate gene studies, and genome-wide association studies, as well as whole transcriptome studies. Findings from recent studies that used longitudinal characterization of the RSTR phenotype are compared to those using a cross-sectional definition, and the challenges of using tuberculin skin test and interferon-gamma release assay are discussed. Finally, future directions are proposed. Since this is a rapidly evolving area of public health significance, this review will help frame future research questions and study designs.

Exploring the expressiveness of abstract metabolic networks

Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an abstract metabolic network, is a graph in which metabolic pathways are nodes and there is an edge between two nodes if their corresponding pathways share one or more compounds. The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms' metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

MetNet: A two-level approach to reconstructing and comparing metabolic networks

Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Perspective: Dimensions of the scientific method

The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining-inspired, and "allochthonous" knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

Metabolic modelling of chronic wound microbiota predicts mutualistic interactions that drive community composition

AIMS

To identify putative mutualistic interactions driving community composition in polymicrobial chronic wound infections using metabolic modelling.

METHODS AND RESULTS

We developed a 12 species metabolic model that covered 74% of 16S rDNA pyrosequencing reads of dominant genera from 2963 chronic wound patients. The community model was used to predict species abundances averaged across this large patient population. We found that substantially improved predictions were obtained when the model was constrained with genera prevalence data and predicted abundances were averaged over 5000 ensemble simulations with community participants randomly determined according to the experimentally determined prevalences. Staphylococcus and Pseudomonas were predicted to exhibit a strong mutualistic relationship that resulted in community growth rate and diversity simultaneously increasing, suggesting that these two common chronic wound pathogens establish dominance by cooperating with less harmful commensal species. In communities lacking one or both dominant pathogens, other mutualistic relationship including Staphylococcus/Acinetobacter, Pseudomonas/Serratia and Streptococcus/Enterococcus were predicted consistent with published experimental data.

CONCLUSIONS

Mutualistic interactions were predicted to be driven by crossfeeding of organic acids, alcohols and amino acids that could potentially be disrupted to slow chronic wound disease progression.

SIGNIFICANCE AND IMPACT OF THE STUDY

Approximately 2% of the US population suffers from nonhealing chronic wounds infected by a combination of commensal and pathogenic bacteria. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich wound environment and species interactions that promote community stability and robustness. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy.

Distribution of fitness effects of mutations obtained from a simple genetic regulatory network model

Beneficial and deleterious mutations change an organism's fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.

Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

MOTIVATION

Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging.

RESULTS

We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.

AVAILABILITY AND IMPLEMENTATION

The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models

Genome-scale metabolic network models have been widely used over the last decade and have been shown to successfully predict the metabolic behavior of many organisms. Yet the complexity of metabolic regulation often limits the accuracy of these models. Integrative modeling approaches have recently been developed that combine metabolic and regulatory networks, thereby expanding the capabilities and accuracy of genome-scale modeling. This chapter provides a guide to reconstruct and curate such integrated network models. Specifically, this protocol describes the PROM (Probabilistic Regulation of Metabolism) and GEMINI (Gene Expression and Metabolism Integrated for Network Inference) approaches. PROM is an automated method for the construction of integrated metabolic and transcriptional regulatory network models, while the GEMINI approach curates the integrated network models using transcriptomics and phenomics data. GEMINI represents the first attempt at applying well-established curation tools that exist for metabolic networks to be applied for curating regulatory networks. The integrated network models generated by these approaches enable the mechanistic integration of diverse biological data and can identify novel strategies to engineer cellular metabolism.

A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli

Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.

Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

Background:

Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference.

Objective:

To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution.

Method:

In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list.

Results:

Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source.

Conclusion:

In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

The Genome-Scale Integrated Networks in Microorganisms

The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

Co-expression network analysis and cis-regulatory element enrichment determine putative functions and regulatory mechanisms of grapevine ATL E3 ubiquitin ligases

Arabidopsis thaliana Toxicos en Levadura (ATL) proteins are a subclass of the RING-H2 zinc finger binding E3 ubiquitin ligases. The grapevine (Vitis vinifera) ATL family was recently characterized, revealing 96 members that are likely to be involved in several physiological processes through protein ubiquitination. However, the final targets and biological functions of most ATL E3 ligases are still unknown. We analyzed the co-expression networks among grapevine ATL genes across a set of transcriptomic data related to defense and abiotic stress, combined with a condition-independent dataset. This revealed strong correlations between ATL proteins and diverse signal transduction components and transcriptional regulators, in particular those involved in immunity. An enrichment analysis of cis-regulatory elements in ATL gene promoters and related co-expressed genes highlighted the importance of hormones in the regulation of ATL gene expression. Our work identified several ATL proteins as candidates for further studies aiming to decipher specific grapevine resistance mechanisms activated in response to pathogens.

Synthetic Biology to Improve the Production of Lipases and Esterases (Review)

Synthetic biology is an emergent field of research whose aim is to make biology an engineering discipline, thus permitting to design, control, and standardize biological processes. Synthetic biology is therefore expected to boost the development of biotechnological processes such as protein production and enzyme engineering, which can be significantly relevant for lipases and esterases.

Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives

Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.

Genome-Wide Mapping of Transcriptional Regulation and Metabolism Describes Information-Processing Units in Escherichia coli

In the face of changes in their environment, bacteria adjust gene expression levels and produce appropriate responses. The individual layers of this process have been widely studied: the transcriptional regulatory network describes the regulatory interactions that produce changes in the metabolic network, both of which are coordinated by the signaling network, but the interplay between them has never been described in a systematic fashion. Here, we formalize the process of detection and processing of environmental information mediated by individual transcription factors (TFs), utilizing a concept termed genetic sensory response units (GENSOR units), which are composed of four components: (1) a signal, (2) signal transduction, (3) genetic switch, and (4) a response. We used experimentally validated data sets from two databases to assemble a GENSOR unit for each of the 189 local TFs of Escherichia coli K-12 contained in the RegulonDB database. Further analysis suggested that feedback is a common occurrence in signal processing, and there is a gradient of functional complexity in the response mediated by each TF, as opposed to a one regulator/one pathway rule. Finally, we provide examples of other GENSOR unit applications, such as hypothesis generation, detailed description of cellular decision making, and elucidation of indirect regulatory mechanisms.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.

SBMLmod: a Python-based web application and web service for efficient data integration and model simulation

Background

Systems Biology Markup Language (SBML) is the standard model representation and description language in systems biology. Enriching and analysing systems biology models by integrating the multitude of available data, increases the predictive power of these models. This may be a daunting task, which commonly requires bioinformatic competence and scripting.

Results

We present SBMLmod, a Python-based web application and service, that automates integration of high throughput data into SBML models. Subsequent steady state analysis is readily accessible via the web service COPASIWS. We illustrate the utility of SBMLmod by integrating gene expression data from different healthy tissues as well as from a cancer dataset into a previously published model of mammalian tryptophan metabolism.

Conclusion

SBMLmod is a user-friendly platform for model modification and simulation. The web application is available at http://sbmlmod.uit.no, whereas the WSDL definition file for the web service is accessible via http://sbmlmod.uit.no/SBMLmod.wsdl. Furthermore, the entire package can be downloaded from https://github.com/MolecularBioinformatics/sbml-mod-ws. We envision that SBMLmod will make automated model modification and simulation available to a broader research community.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1722-9) contains supplementary material, which is available to authorized users.

Inhibition of expression of the circadian clock gene Period causes metabolic abnormalities including repression of glycometabolism in Bombyx mori cells

Abnormalities in the circadian clock system are known to affect the body’s metabolic functions, though the molecular mechanisms responsible remain uncertain. In this study, we achieved continuous knockdown of B. mori Period (BmPer) gene expression in the B. mori ovary cell line (BmN), and generated a Per-KD B. mori model with developmental disorders including small individual cells and slow growth. We conducted cell metabolomics assays by gas chromatography/liquid chromatography-mass spectrometry and showed that knockdown of BmPer gene expression resulted in significant inhibition of glycometabolism. Amino acids that used glucose metabolites as a source were also down-regulated, while lipid metabolism and nucleotide metabolism were significantly up-regulated. Metabolite correlation analysis showed that pyruvate and lactate were closely related to glycometabolism, as well as to metabolites such as aspartate, alanine, and xanthine in other pathways. Further validation experiments showed that the activities of the key enzymes of glucose metabolism, hexokinase, phosphofructokinase, and citrate synthase, were significantly decreased and transcription of their encoding genes, as well as that of pyruvate kinase, were also significantly down-regulated. We concluded that inhibition of the circadian clock gene BmPer repressed glycometabolism, and may be associated with changes in cellular amino acid metabolism, and in cell growth and development.

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference.

BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals

MOTIVATION

Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models.

RESULTS

We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets.

AVAILABILITY AND IMPLEMENTATION

MATLAB code is available at https://github.com/kms1041/BeReTa (github).

CONTACT

byungkim@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks.

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements

Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.62 in log scale (p < 10(-10)), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between kmax(vivo) and kcat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a high-throughput method for extracting enzyme kinetic constants from omics data.

Heterologous biosynthesis of artemisinic acid in Saccharomyces cerevisiae

Artemisinic acid is a precursor of antimalarial compound artemisinin. The titre of biosynthesis of artemisinic acid using Saccharomyces cerevisiae platform has been achieved up to 25 g l(-1) ; however, the performance of platform cells is still industrial unsatisfied. Many strategies have been proposed to improve the titre of artemisinic acid. The traditional strategies mainly focused on partial target sites, simple up-regulation key genes or repression competing pathways in the total synthesis route. However, this may result in unbalance of carbon fluxes and dysfunction of metabolism. In this review, the recent advances on the promising methods in silico and in vivo for biosynthesis of artemisinic acid have been discussed. The bioinformatics and omics techniques have brought a great prospect for improving production of artemisinin and other pharmacal compounds in heterologous platform.

Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers

We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system.

Providing data science support for systems pharmacology and its implications to drug discovery

INTRODUCTION

The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology.

AREAS COVERED

This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction.

EXPERT OPINION

Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

Systems Biology Approaches to Understand Natural Products Biosynthesis

Actinomycetes populate soils and aquatic sediments that impose biotic and abiotic challenges for their survival. As a result, actinomycetes metabolism and genomes have evolved to produce an overwhelming diversity of specialized molecules. Polyketides, non-ribosomal peptides, post-translationally modified peptides, lactams, and terpenes are well-known bioactive natural products with enormous industrial potential. Accessing such biological diversity has proven difficult due to the complex regulation of cellular metabolism in actinomycetes and to the sparse knowledge of their physiology. The past decade, however, has seen the development of omics technologies that have significantly contributed to our better understanding of their biology. Key observations have contributed toward a shift in the exploitation of actinomycete’s biology, such as using their full genomic potential, activating entire pathways through key metabolic elicitors and pathway engineering to improve biosynthesis. Here, we review recent efforts devoted to achieving enhanced discovery, activation, and manipulation of natural product biosynthetic pathways in model actinomycetes using genome-scale biological datasets.