The small world of metabolism

The energetics and evolution of oxidoreductases in deep time

The core metabolic reactions of life drive electrons through a class of redox protein enzymes, the oxidoreductases. The energetics of electron flow is determined by the redox potentials of organic and inorganic cofactors as tuned by the protein environment. Understanding how protein structure affects oxidation-reduction energetics is crucial for studying metabolism, creating bioelectronic systems, and tracing the history of biological energy utilization on Earth. We constructed ProtReDox (https://protein-redox-potential.web.app), a manually curated database of experimentally determined redox potentials. With over 500 measurements, we can begin to identify how proteins modulate oxidation-reduction energetics across the tree of life. By mapping redox potentials onto networks of oxidoreductase fold evolution, we can infer the evolution of electron transfer energetics over deep time. ProtReDox is designed to include user-contributed submissions with the intention of making it a valuable resource for researchers in this field.

Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications

This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and low-performance loss. To address this issue, this research study compares the performance, behavior, and spike generation of multiple SNN models using consistent inputs and neurons. The findings of the study provide valuable insights into the benefits and challenges of SNNs and their models, emphasizing the significance of comparing multiple models to identify the most effective one. Moreover, the study quantifies the number of spiking operations required by each model to process the same inputs and produce equivalent outputs, enabling a thorough assessment of computational efficiency. The findings provide valuable insights into the benefits and limitations of SNNs and their models. The research underscores the significance of comparing different models to make informed decisions in practical applications. Additionally, the results reveal essential variations in biological plausibility and computational efficiency among the models, further emphasizing the importance of selecting the most suitable model for a given task. Overall, this study contributes to a deeper understanding of SNNs and offers practical guidelines for using their potential in real-world scenarios.

Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Panthera tigris) and wildebeest (Connochaetes taurinus)

Fecal microbiota variability and individuality are well studied in humans and also in farm animals (related to diet- or disease-specific influences), but very little is known for exotic zoo-housed animals. This includes a wide range of species that differ greatly in microbiota composition and variation. For example, herbivorous species show a very similar and constant fecal microbiota over time, whereas carnivorous species appear to be highly variable in fecal microbial diversity and composition. Our objective was to determine whether species-specific and individual-specific clustering patterns were observed in the fecal microbiota of wildebeest (Connochaetes taurinus) and tigers (Panthera tigris). We collected 95 fecal samples of 11 animal individuals that were each sampled over eight consecutive days and analyzed those with Illumina MiSeq sequencing of the V3-V4 region of the 16SrRNA gene. In order to identify species or individual clusters, we applied two different agglomerative hierarchical clustering algorithms - a community detection algorithm and Ward's linkage. Our results showed that both, species-specific and individual-specific clustering is possible, but more reliable results were achieved when applying dynamic time warping which finds the optimal alignment between different time series. Furthermore, the bacterial families that distinguish individuals from each other in both species included daily occurring core bacteria (e.g., Acidaminococcaceae in wildebeests or Clostridiaceae in tigers) as well as individual dependent and more fluctuating bacterial families. Our results suggest that while it is necessary to consider multiple consecutive samples per individual, it is then possible to characterize individual abundance patterns in fecal microbiota in both herbivorous and carnivorous species. This would allow establishing individual microbiota profiles of animals housed in zoos, which is a basic prerequisite to quickly detect deviations and use microbiome analysis as a non-invasive and cost-effective tool in animal welfare.

Challenges and Limitations of Biological Network Analysis

High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein-Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein-protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Semi-rational design of L-amino acid deaminase for production of pyruvate and D-alanine by Escherichia coli whole-cell biocatalyst

In our previous study, one-step pyruvate and D-alanine production from D,L-alanine by a whole-cell biocatalyst Escherichia coli expressing L-amino acid deaminase (Pm1) derived from Proteus mirabilis was investigated. However, due to the low catalytic efficiency of Pm1, the pyruvate titer was relatively low. Here, semi-rational design based on site-directed saturation mutagenesis was carried out to improve the catalytic efficiency of Pm1. A novel high-throughput screening (HTS) method for pyruvate based on 2,4-dinitrophenylhydrazine indicator was then established. The catalytic efficiency (k_cat/K_m) of the mutant V437I screened out by this method was 1.88 times higher than wild type. Next, to improve the growth of the engineered strain BLK07, the genes encoding for Xpk and Fbp were integrated into its genome to construct non-oxidative glycolysis (NOG) pathway. Finally, the CRISPR/Cas9 system was used to integrate the N6-pm1-V437I gene into the genome of BLK07. Pyruvic acid titer of the plasmid-free strain reached 42.20 g/L with an L-alanine conversion rate of 77.62% and a D-alanine resolution of 82.4%. This work would accelerate the industrial production of pyruvate and D-alanine by biocatalysis, and the HTS method established here could be used to screen other Pm1 mutants with high pyruvate titers.

Neural and social correlates of attitudinal brokerage: using the complete social networks of two entire villages

To avoid polarization and maintain small-worldness in society, people who act as attitudinal brokers are critical. These people maintain social ties with people who have dissimilar and even incompatible attitudes. Based on resting-state functional magnetic resonance imaging (n = 139) and the complete social networks from two Korean villages (n = 1508), we investigated the individual-level neural capacity and social-level structural opportunity for attitudinal brokerage regarding gender role attitudes. First, using a connectome-based predictive model, we successfully identified the brain functional connectivity that predicts attitudinal diversity of respondents' social network members. Brain regions that contributed most to the prediction included mentalizing regions known to be recruited in reading and understanding others’ belief states. This result was corroborated by leave-one-out cross-validation, fivefold cross-validation and external validation where the brain connectivity identified in one village was used to predict the attitudinal diversity in another independent village. Second, the association between functional connectivity and attitudinal diversity of social network members was contingent on a specific position in a social network, namely, the structural brokerage position where people have ties with two people who are not otherwise connected.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

Water and Life: The Medium is the Message

Water, the most abundant compound on the surface of the Earth and probably in the universe, is the medium of biology, but is much more than that. Water is the most frequent actor in the chemistry of metabolism. Our quantitation here reveals that water accounts for 99.4% of metabolites in Escherichia coli by molar concentration. Between a third and a half of known biochemical reactions involve consumption or production of water. We calculated the chemical flux of water and observed that in the life of a cell, a given water molecule frequently and repeatedly serves as a reaction substrate, intermediate, cofactor, and product. Our results show that as an E. coli cell replicates in the presence of molecular oxygen, an average in vivo water molecule is chemically transformed or is mechanistically involved in catalysis ~ 3.7 times. We conclude that, for biological water, there is no distinction between medium and chemical participant. Chemical transformations of water provide a basis for understanding not only extant biochemistry, but the origins of life. Because the chemistry of water dominates metabolism and also drives biological synthesis and degradation, it seems likely that metabolism co-evolved with biopolymers, which helps to reconcile polymer-first versus metabolism-first theories for the origins of life.

New insights on key genes involved in drought stress response of barley: gene networks reconstruction, hub, and promoter analysis

Background

Barley (Hordeum vulgare L.) is one of the most important cereals worldwide. Although this crop is drought-tolerant, water deficiency negatively affects its growth and production. To detect key genes involved in drought tolerance in barley, a reconstruction of the related gene network and discovery of the hub genes would help. Here, drought-responsive genes in barley were collected through analysis of the available microarray datasets (− 5 ≥ Fold change ≥ 5, adjusted p value ≤ 0.05). Protein-protein interaction (PPI) networks were reconstructed.

Results

The hub genes were identified by Cytoscape software using three Cyto-hubba algorithms (Degree, Closeness, and MNC), leading to the identification of 17 and 16 non-redundant genes at vegetative and reproductive stages, respectively. These genes consist of some transcription factors such as HvVp1, HvERF4, HvFUS3, HvCBF6, DRF1.3, HvNAC6, HvCO5, and HvWRKY42, which belong to AP2, NAC, Zinc-finger, and WRKY families. In addition, the expression pattern of four hub genes was compared between the two studied cultivars, i.e., “Yousef” (drought-tolerant) and “Morocco” (susceptible). The results of real-time PCR revealed that the expression patterns corresponded well with those determined by the microarray. Also, promoter analysis revealed that some TF families, including AP2, NAC, Trihelix, MYB, and one modular (composed of two HD-ZIP TFs), had a binding site in 85% of promoters of the drought-responsive genes and of the hub genes in barley.

Conclusions

The identified hub genes, especially those from AP2 and NAC families, might be among key TFs that regulate drought-stress response in barley and are suggested as promising candidate genes for further functional analysis.

Biotransformation and chiral resolution of d,l-alanine into pyruvate and d-alanine with a whole-cell biocatalyst expressing l-amino acid deaminase

Pyruvate is an important pharmaceutical intermediate and is widely used in food, nutraceuticals, and pharmaceuticals. However, high environmental pollution caused by chemical synthesis or complex separation process of microbial fermentation methods constrain the supply of pyruvate. Here, one-step pyruvate and d-alanine production from d,l-alanine by whole-cell biocatalysis was investigated. First, l-amino acid deaminase (Pm1) from Proteus mirabilis was expressed in Escherichia coli, resulting in pyruvate titer of 12.01 g/L. Then, N-terminal coding sequences were introduced to the 5'-end of the pm1 gene to enhance the expression of Pm1 and the pyruvate titer increased to 15.13 g/L. Next, product utilization by the biocatalyst was prevented by knocking out the pyruvate uptake transporters (cstA, btsT) and the pyruvate metabolic pathway genes pps, poxB, pflB, ldhA, and aceEF using CRISPR/Cas9, yielding 30.88 g/L pyruvate titer. Finally, by optimizing the reaction conditions, the pyruvate titer was further enhanced to 43.50 g/L in 8 H with a 79.99% l-alanine conversion rate; meanwhile, the resolution of d-alanine reached 84.0%. This work developed a whole-cell biocatalyst E. coli strain for high-yield, high-efficiency, and low-pollution pyruvate and d-alanine production, which has great potential for the commercial application in the future.

Small protein folds at the root of an ancient metabolic network

Life on Earth is driven by electron transfer reactions catalyzed by a suite of enzymes that comprise the superfamily of oxidoreductases (Enzyme Classification EC1). Most modern oxidoreductases are complex in their structure and chemistry and must have evolved from a small set of ancient folds. Ancient oxidoreductases from the Archean Eon between ca. 3.5 and 2.5 billion years ago have been long extinct, making it challenging to retrace evolution by sequence-based phylogeny or ancestral sequence reconstruction. However, three-dimensional topologies of proteins change more slowly than sequences. Using comparative structure and sequence profile-profile alignments, we quantify the similarity between proximal cofactor-binding folds and show that they are derived from a common ancestor. We discovered that two recurring folds were central to the origin of metabolism: ferredoxin and Rossmann-like folds. In turn, these two folds likely shared a common ancestor that, through duplication, recruitment, and diversification, evolved to facilitate electron transfer and catalysis at a very early stage in the origin of metabolism.

Landscape of the complete RNA chemical modifications in the human 80S ribosome

During ribosome biogenesis, ribosomal RNAs acquire various chemical modifications that ensure the fidelity of translation, and dysregulation of the modification processes can cause proteome changes as observed in cancer and inherited human disorders. Here, we report the complete chemical modifications of all RNAs of the human 80S ribosome as determined with quantitative mass spectrometry. We assigned 228 sites with 14 different post-transcriptional modifications, most of which are located in functional regions of the ribosome. All modifications detected are typical of eukaryotic ribosomal RNAs, and no human-specific modifications were observed, in contrast to a recently reported cryo-electron microscopy analysis. While human ribosomal RNAs appeared to have little polymorphism regarding the post-transcriptional modifications, we found that pseudouridylation at two specific sites in 28S ribosomal RNA are significantly reduced in ribosomes of patients with familial dyskeratosis congenita, a genetic disease caused by a point mutation in the pseudouridine synthase gene DKC1. The landscape of the entire epitranscriptomic ribosomal RNA modifications provides a firm basis for understanding ribosome function and dysfunction associated with human disease.

MATria: a unified centrality algorithm

Background

Computing centrality is a foundational concept in social networking that involves finding the most “central” or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm.

Results

We instead generalize the results of any k centrality algorithms through our iterative algorithm MATRIA, producing a single ranked and unified set of central nodes. Through tests on three biological networks, we demonstrate evident and balanced correlations with the results of these k algorithms. We also improve its speed through GPU parallelism.

Conclusions

Our results show iteration to be a powerful technique that can eliminate spatial bias among central nodes, increasing the level of agreement between algorithms with various importance definitions. GPU parallelism improves speed and makes iteration a tractable problem for larger networks.

Measuring the importance of vertices in the weighted human disease network

Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network (WHDN) in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in WHDN, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases.

Identification of Disease-miRNA Networks Across Different Cancer Types Using SWIM

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).

Universal scaling across biochemical networks on Earth

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

The scale-free nature of protein sequence space

The sequence space of five protein superfamilies was investigated by constructing sequence networks. The nodes represent individual sequences, and two nodes are connected by an edge if the global sequence identity of two sequences exceeds a threshold. The networks were characterized by their degree distribution (number of nodes with a given number of neighbors) and by their fractal network dimension. Although the five protein families differed in sequence length, fold, and domain arrangement, their network properties were similar. The fractal network dimension Df was distance-dependent: a high dimension for single and double mutants (Df = 4.0), which dropped to Df = 0.7-1.0 at 90% sequence identity, and increased to Df = 3.5-4.5 below 70% sequence identity. The distance dependency of the network dimension is consistent with evolutionary constraints for functional proteins. While random single and double mutations often result in a functional protein, the accumulation of more than ten mutations is dominated by epistasis. The networks of the five protein families were highly inhomogeneous with few highly connected communities ("hub sequences") and a large number of smaller and less connected communities. The degree distributions followed a power-law distribution with similar scaling exponents close to 1. Because the hub sequences have a large number of functional neighbors, they are expected to be robust toward possible deleterious effects of mutations. Because of their robustness, hub sequences have the potential of high innovability, with additional mutations readily inducing new functions. Therefore, they form hotspots of evolution and are promising candidates as starting points for directed evolution experiments in biotechnology.

Network Architecture and Mutational Sensitivity of the C. elegans Metabolome

A fundamental issue in evolutionary systems biology is understanding the relationship between the topological architecture of a biological network, such as a metabolic network, and the evolution of the network. The rate at which an element in a metabolic network accumulates genetic variation via new mutations depends on both the size of the mutational target it presents and its robustness to mutational perturbation. Quantifying the relationship between topological properties of network elements and the mutability of those elements will facilitate understanding the variation in and evolution of networks at the level of populations and higher taxa. We report an investigation into the relationship between two topological properties of 29 metabolites in the C. elegans metabolic network and the sensitivity of those metabolites to the cumulative effects of spontaneous mutation. The correlations between measures of network centrality and mutability are not statistically significant, but several trends point toward a weak positive association between network centrality and mutational sensitivity. There is a small but significant negative association between the mutational correlation of a pair of metabolites (r_M) and the shortest path length between those metabolites. Positive association between the centrality of a metabolite and its mutational heritability is consistent with centrally-positioned metabolites presenting a larger mutational target than peripheral ones, and is inconsistent with centrality conferring mutational robustness, at least in toto. The weakness of the correlation between r_M and the shortest path length between pairs of metabolites suggests that network locality is an important but not overwhelming factor governing mutational pleiotropy. These findings provide necessary background against which the effects of other evolutionary forces, most importantly natural selection, can be interpreted.

Identification of reaction organization patterns that naturally cluster enzymatic transformations

BACKGROUND

Metabolic reactions are chemical transformations commonly catalyzed by enzymes. In recent years, the explosion of genomic data and individual experimental characterizations have contributed to the construction of databases and methodologies for the analysis of metabolic networks. Some methodologies based on graph theory organize compound networks into metabolic functional categories without preserving biochemical pathways. Other methods based on chemical group exchange and atom flow trace the conversion of substrates into products in detail, which is useful for inferring metabolic pathways.

METHODS

Here, we present a novel rule-based approach incorporating both methods that decomposes each reaction into architectures of compound pairs and loner compounds that can be organized into tree structures. We compared the tree structure-compound pairs to those reported in the KEGG-RPAIR dataset and obtained a match precision of 81%. The generated tree structures naturally clustered all reactions into general reaction patterns of compounds with similar chemical transformations. The match precision of each cluster was calculated and used to suggest reactant-pairs for which manual curation can be avoided because this is the main goal of the method. We evaluated catalytic processes in the clusters based on Enzyme Commission categories that revealed preferential use of enzyme classes.

CONCLUSIONS

We demonstrate that the application of simple rules can enable the identification of reaction patterns reflecting metabolic reactions that transform substrates into products and the types of catalysis involved in these transformations. Our rule-based approach can be incorporated as the input in pathfinders or as a tool for the construction of reaction classifiers, indicating its usefulness for predicting enzyme catalysis.

Community detection in dynamic networks via adaptive label propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.

Identification of metabolic pathways using pathfinding approaches: a systematic review

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.

Detecting the Significant Flux Backbone of Escherichia coli metabolism

The heterogeneity of computationally predicted reaction fluxes in metabolic networks within a single flux state can be exploited to detect their significant flux backbone. Here, we disclose the backbone of Escherichia coli, and compare it with the backbones of other bacteria. We find that, in general, the core of the backbones is mainly composed of reactions in energy metabolism corresponding to ancient pathways. In E. coli, the synthesis of nucleotides and the metabolism of lipids form smaller cores which rely critically on energy metabolism. Moreover, the consideration of different media leads to the identification of pathways sensitive to environmental changes. The metabolic backbone of an organism is thus useful to trace simultaneously both its evolution and adaptation fingerprints.

Joint Estimation of Multiple High-dimensional Precision Matrices

Motivated by analysis of gene expression data measured in different tissues or disease states, we consider joint estimation of multiple precision matrices to effectively utilize the partially shared graphical structures of the corresponding graphs. The procedure is based on a weighted constrained ℓ∞/ℓ1 minimization, which can be effectively implemented by a second-order cone programming. Compared to separate estimation methods, the proposed joint estimation method leads to estimators converging to the true precision matrices faster. Under certain regularity conditions, the proposed procedure leads to an exact graph structure recovery with a probability tending to 1. Simulation studies show that the proposed joint estimation methods outperform other methods in graph structure recovery. The method is illustrated through an analysis of an ovarian cancer gene expression data. The results indicate that the patients with poor prognostic subtype lack some important links among the genes in the apoptosis pathway.

Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm

Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis.

Computational methods to identify metabolic sub-networks based on metabolomic profiles

Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

Transporter engineering and enzyme evolution for pyruvate production from d/l-alanine with a whole-cell biocatalyst expressing l-amino acid deaminase from Proteus mirabilis

Pyruvate, which has been widely used in the food, pharmaceutical, and agrochemical industries, can be produced by “one-step pyruvate production” method from d/l-alanine with a whole-cell E. coli biocatalyst expressing l-amino acid deaminase (pm1) from Proteus mirabilis.

Biconnectivity of the cellular metabolism: A cross-species study and its implication for human diseases

The maintenance of stability during perturbations is essential for living organisms, and cellular networks organize multiple pathways to enable elements to remain connected and communicate, even when some pathways are broken. Here, we evaluated the biconnectivity of the metabolic networks of 506 species in terms of the clustering coefficients and the largest biconnected components (LBCs), wherein a biconnected component (BC) indicates a set of nodes in which every pair is connected by more than one path. Via comparison with the rewired networks, we illustrated how biconnectivity in cellular metabolism is achieved on small and large scales. Defining the biconnectivity of individual metabolic compounds by counting the number of species in which the compound belonged to the LBC, we demonstrated that biconnectivity is significantly correlated with the evolutionary age and functional importance of a compound. The prevalence of diseases associated with each metabolic compound quantifies the compounds vulnerability, i.e., the likelihood that it will cause a metabolic disorder. Moreover, the vulnerability depends on both the biconnectivity and the lethality of the compound. This fact can be used in drug discovery and medical treatments.

The transfer and transformation of collective network information in gene-matched networks

Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type.

K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores

Background

Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals.

Results

The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains.

Conclusion

Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.

Electronic supplementary material

The online version of this article (doi:10.1186/s13336-015-0016-6) contains supplementary material, which is available to authorized users.

Application of centrality measures in the identification of critical genes in diabetes mellitus

The connectivity of a protein and its structure is related to its functional properties. Many experimental approaches have been employed for the identification of Diabetes Mellitus (DM) associated candidate genes. Therefore, it is of interest to use var ious graph centrality measures integrated with the genes associated with the human Diabetes Mellitus network for the identification of potential targets. We used 2728 genes known to cause Diabetes Mellitus from Jensenlab (Novo Nordisk Foundation Center for Protein Research, Denmark) for this analysis. A protein-protein interaction network was further constructed using a tool Centralities in Biological Networks (CentiBiN) with 1020 nodes after eliminating the duplicates, parallel edges, self -loop edges and unknown Human Protein Reference Database (HPRD) IDS. We used fourteen centralities measures which are useful in identifying the structural characteristic of individuals in the network. The results of the centrality measures are highly correlated. Thus, we identified genes that are critically associated with DM. We further report the top ten genes of all fourteen centrality measures for further consideration as targets for DM.

Quantitative analyses of the plant cytoskeleton reveal underlying organizational principles

The actin and microtubule (MT) cytoskeletons are vital structures for cell growth and development across all species. While individual molecular mechanisms underpinning actin and MT dynamics have been intensively studied, principles that govern the cytoskeleton organization remain largely unexplored. Here, we captured biologically relevant characteristics of the plant cytoskeleton through a network-driven imaging-based approach allowing us to quantitatively assess dynamic features of the cytoskeleton. By introducing suitable null models, we demonstrate that the plant cytoskeletal networks exhibit properties required for efficient transport, namely, short average path lengths and high robustness. We further show that these advantageous features are maintained during temporal cytoskeletal rearrangements. Interestingly, man-made transportation networks exhibit similar properties, suggesting general laws of network organization supporting diverse transport processes. The proposed network-driven analysis can be readily used to identify organizational principles of cytoskeletons in other organisms.

Controlling networks of nonlinearly-coupled nodes using response surfaces

Control of complex processes is a major goal of network analyses. Most approaches to control nonlinearly coupled systems require the network topology and/or network dynamics. Unfortunately, neither the full set of participating nodes nor the network topology is known for many important systems. On the other hand, system responses to perturbations are often easily measured. We show how the collection of such responses –a response surface– can be used for network control. Analyses of model systems show that response surfaces are smooth and hence can be approximated using low order polynomials. Importantly, these approximations are largely insensitive to stochastic fluctuations in data or measurement errors. They can be used to compute how a small set of nodes need to be altered in order to direct the network close to a pre-specified target state. These ideas, illustrated on a nonlinear electrical circuit, can prove useful in many contexts including in reprogramming cellular states.

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.

Identifying all moiety conservation laws in genome-scale metabolic networks

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses

Constraint-based approaches have been used for integrating data in large-scale metabolic networks to obtain insights into metabolism of various organisms. Due to the underlying steady-state assumption, these approaches are usually not suited for making predictions about metabolite levels. Here, we ask whether we can make inferences about the variability of metabolite levels from a constraint-based analysis based on the integration of transcriptomics data. To this end, we analyze time-resolved transcriptomics and metabolomics data from Arabidopsis thaliana under a set of eight different light and temperature conditions. In a previous study, the gene expression data have already been integrated in a genome-scale metabolic network to predict pathways, termed modulators and sustainers, which are differentially regulated with respect to a biochemically meaningful data-driven null model. Here, we present a follow-up analysis which bridges the gap between flux- and metabolite-centric methods. One of our main findings demonstrates that under certain environmental conditions, the levels of metabolites acting as substrates in modulators or sustainers show significantly lower temporal variations with respect to the remaining measured metabolites. This observation is discussed within the context of a systems-view of plasticity and robustness of metabolite contents and pathway fluxes. Our study paves the way for investigating the existence of similar principles in other species for which both genome-scale networks and high-throughput metabolomics data of high quality are becoming increasingly available.

Analysis of Unweighted Amino Acids Network

The analysis of amino acids network is very important to studying the various physicochemical properties of amino acids. In this paper we consider the amino acid network based on mutation of the codons. To analyze the relative importance of the amino acids we have discussed different measures of centrality. The measure of centrality is a powerful tool of graph theory for ranking the vertices and analysis of biological network. We have also investigated the correlation coefficients between various measures of centrality. Also we have discussed clustering coefficient as well as average clustering coefficient of the network. Finally we have discussed the degree of distribution as well as skewness.

Metabolic network reconstruction and their topological analysis

This chapter focuses on the way to build a metabolic network and how to analyze its structure. The first part of this chapter describes the methods of the network model reconstruction from biochemical data found in specialized databases and/or literature. The second part deals with metabolic pathway analysis as a useful tool for better understanding the complex architecture of intracellular metabolism. The graph analysis and the stoichiometric network analysis are important approaches for understanding the network topology and consequently the function of metabolic networks. Among the methods presented, the Elementary Flux Modes analysis will be more detailed. Finally, we illustrate in this chapter an example of network reconstruction from heterotrophic plant cells metabolism and its topological analysis leading to a huge number of Elementary Flux Modes.

Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective

We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now.

New approaches to the problem of generating coherent, reproducible phenotypes

Fundamental, unresolved questions in biology include how a bacterium generates coherent phenotypes, how a population of bacteria generates a coherent set of such phenotypes, how the cell cycle is regulated and how life arose. To try to help answer these questions, we have developed the concepts of hyperstructures, competitive coherence and life on the scales of equilibria. Hyperstructures are large assemblies of macromolecules that perform functions. Competitive coherence describes the way in which organisations such as cells select a subset of their constituents to be active in determining their behaviour; this selection results from a competition between a process that is responsible for a historical coherence and another process responsible for coherence with the current environment. Life on the scales of equilibria describes how bacteria depend on the cell cycle to negotiate phenotype space and, in particular, to satisfy the conflicting constraints of having to grow in favourable conditions so as to reproduce yet not grow in hostile conditions so as to survive. Both competitive coherence and life on the scales deal with the problem of reconciling conflicting constraints. Here, we bring together these concepts in the common framework of hyperstructures and make predictions that may be tested using a learning program, Coco, and secondary ion mass spectrometry.

COMPARATIVE ANALYSIS OF METABOLIC NETWORKS IN MESOPHILIC AND THERMOPHILIC ARCHAEA METHANOGENS BASED ON MODULARITY

Metabolic networks are useful representations of the metabolic capabilities of cells. A comparison of metabolic networks across species is essential to better understand how evolutionary pressures shape these networks. By comparing the set of reactions that are expected to occur in an organism with the set of reactions in reference metabolic pathways, it is possible to infer the main metabolic functions of an organism. In this paper, the metabolic networks of the mesophilic archaeon Methanosarcina acetivorans and the thermophilic archaeon Methanopyrus kandleri have been reconstructed based on the KEGG LIGAND database, followed by four topological statistical analyses of the nodes in the two networks to compare their metabolic networks. The values of average degree and characteristic path length are very small but clustering coefficient is relatively large. The results show that the complete metabolic networks of M. acetivorans and M. kandleri possessed "small-world" network properties. Then we used Girvan–Newman modular algorithm to identify hub modules and compared hub modules with non-hub modules, respectively. The results show that M. kandleri metabolic network has a better modular organization than the M. acetivorans network. M. acetivorans includes 39 modules, 25 modules of them are independent, and 15 modules are functionally pure. On the other hand, M. kandleri includes 30 modules. Among them, there are 20 independent modules, and 14 of them are functionally pure. These results further indicated that the present approach for identifying modules yields modules that have biologically significant functions. We also identified hub modules of the metabolic networks and found that these hub modules are carbohydrate metabolism and amino acid metabolism. The conclusions obtained from such studies provide a broad overview of the similarities and differences between organism's metabolic networks. These will be very helpful for further research on thermostability of methanogens.

Small world network strategies for studying protein structures and binding

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

Minimal cut sets and the use of failure modes in metabolic networks

A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding ways of producing industrially relevant compounds from renewable resources, not only for economical, but also for sustainability, reasons.