Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty

Background

Biological networks describes the mechanisms which govern cellular functions. Temporal networks show how these networks evolve over time. Studying the temporal progression of network topologies is of utmost importance since it uncovers how a network evolves and how it resists to external stimuli and internal variations. Two temporal networks have co-evolving subnetworks if the evolving topologies of these subnetworks remain similar to each other as the network topology evolves over a period of time. In this paper, we consider the problem of identifying co-evolving subnetworks given a pair of temporal networks, which aim to capture the evolution of molecules and their interactions over time. Although this problem shares some characteristics of the well-known network alignment problems, it differs from existing network alignment formulations as it seeks a mapping of the two network topologies that is invariant to temporal evolution of the given networks. This is a computationally challenging problem as it requires capturing not only similar topologies between two networks but also their similar evolution patterns.

Results

We present an efficient algorithm, Tempo, for solving identifying co-evolving subnetworks with two given temporal networks. We formally prove the correctness of our method. We experimentally demonstrate that Tempo scales efficiently with the size of network as well as the number of time points, and generates statistically significant alignments—even when evolution rates of given networks are high. Our results on a human aging dataset demonstrate that Tempo identifies novel genes contributing to the progression of Alzheimer’s, Huntington’s and Type II diabetes, while existing methods fail to do so.

Conclusions

Studying temporal networks in general and human aging specifically using Tempo enables us to identify age related genes from non age related genes successfully. More importantly, Tempo takes the network alignment problem one huge step forward by moving beyond the classical static network models.

     

Damped oscillations in the adaptive response of the iron homeostasis network of E. coli

Living organisms often have to adapt to sudden environmental changes and reach homeostasis. To achieve adaptation, cells deploy motifs such as feedback in their genetic networks, endowing the cellular response with desirable properties. We studied the iron homeostasis network of E. coli, which employs feedback loops to regulate iron usage and uptake, while maintaining intracellular iron at non‐toxic levels. Using fluorescence reporters for iron‐dependent promoters in bulk and microfluidics‐based, single‐cell experiments, we show that E. coli cells exhibit damped oscillations in gene expression, following sudden reductions in external iron levels. The oscillations, lasting for several generations, are independent of position along the cell cycle. Experiments with mutants in network components demonstrate the involvement of iron uptake in the oscillations. Our findings suggest that the response is driven by intracellular iron oscillations large enough to induce nearly full network activation/deactivation. We propose a mathematical model based on a negative feedback loop closed by rapid iron uptake, and including iron usage and storage, which captures the main features of the observed behaviour. Taken together, our results shed light on the control of iron metabolism in bacteria and suggest that the oscillations represent a compromise between the requirements of stability and speed of response.     

Catalysis in Reaction Networks

We define catalytic networks as chemical reaction networks with an essentially catalytic reaction pathway: one which is “on” in the presence of certain catalysts and “off” in their absence. We show that examples of catalytic networks include synthetic DNA molecular circuits that have been shown to perform signal amplification and molecular logic. Recall that a critical siphon is a subset of the species in a chemical reaction network whose absence is forward invariant and stoichiometrically compatible with a positive point. Our main theorem is that all weakly-reversible networks with critical siphons are catalytic. Consequently, we obtain new proofs for the persistence of atomic event-systems of Adleman et al., and normal networks of Gnacadja. We define autocatalytic networks, and conjecture that a weakly-reversible reaction network has critical siphons if and only if it is autocatalytic.     

Tree-based networks: characterisations,metrics, and support trees

Phylogenetic networks generalise phylogenetic trees and allow for the accurate representation of the evolutionary history of a set of present-day species whose past includes reticulate events such as hybridisation and lateral gene transfer. One way to obtain such a network is by starting with a (rooted) phylogenetic tree T, called a base tree, and adding arcs between arcs of T. The class of phylogenetic networks that can be obtained in this way is called tree-based networks and includes the prominent classes of tree-child and reticulation-visible networks. Initially defined for binary phylogenetic networks, tree-based networks naturally extend to arbitrary phylogenetic networks. In this paper, we generalise recent tree-based characterisations and associated proximity measures for binary phylogenetic networks to arbitrary phylogenetic networks. These characterisations are in terms of matchings in bipartite graphs, path partitions, and antichains. Some of the generalisations are straightforward to establish using the original approach, while others require a very different approach. Furthermore, for an arbitrary tree-based network N, we characterise the support trees of N, that is, the tree-based embeddings of N. We use this characterisation to give an explicit formula for the number of support trees of N when N is binary. This formula is written in terms of the components of a bipartite graph.

     

Dynamic network rewiring determines temporal regulatory functions in Drosophila melanogaster development processes

The identification of network motifs has been widely considered as a significant step towards uncovering the design principles of biomolecular regulatory networks. To date, time‐invariant networks have been considered. However, such approaches cannot be used to reveal time‐specific biological traits due to the dynamic nature of biological systems, and hence may not be applicable to development, where temporal regulation of gene expression is an indispensable characteristic. We propose a concept of a “temporal sequence of network motifs”, a sequence of network motifs in active sub‐networks constructed over time, and investigate significant network motifs in the active temporal sub‐networks of Drosophila melanogaster . Based on this concept, we find a temporal sequence of network motifs which changes according to developmental stages and thereby cannot be identified from the whole static network. Moreover, we show that the temporal sequence of network motifs corresponding to each developmental stage can be used to describe pivotal developmental events.     

The frugivory network properties of a simplified ecosystem: Birds and plants in a Neotropical periurban park

Frugivory networks exhibit a set of properties characterized by a number of network theory‐derived metrics. Their structures often form deterministic patterns that can be explained by the functional roles of interacting species. Although we know lots about how these networks are organized when ecosystems are in a complete, functional condition, we know much less about how incomplete and simplified networks (such as those found in urban and periurban parks) are organized, which features are maintained, which ones are not, and why. In this paper, we examine the properties of a network between frugivorous birds and plants in a small Neotropical periurban park. We found a frugivory network composed of 29 species of birds and 23 of plants. The main roles in this network are played by four species of generalist birds (three resident, one migratory: Myiozetetes similis, Turdus grayi, Chlorospingus flavopectus, and Dumetella carolinensis) and three species of plants (one exotic, two early successional: Phoenix canariensis, Phoradendron sp., and Witheringia stramoniifolia). When compared to reference data from other locations in the Neotropics, species richness is low, one important network‐level metric is maintained (modularity) whereas another one is not (nestedness). Nestedness, a metric associated with network specialists, is a feature this network lacks. Species‐level metrics such as degree, species strength, and module roles, are not maintained. Our work supports modularity as the most pervasive network‐level metric of altered habitats. From a successional point of view, our results suggest that properties revealed by species‐level indices may be developed at a later time, lagging the acquisition of structural elements.     

Persistence in metabolic nets

In an attempt to improve the understanding of complex metabolic dynamic phenomena, we have analysed several ‘metabolic networks’, dynamical systems which, under a single formulation, take into account the activity of several catalytic dissipative structures, interconnected by substrate fluxes and regulatory signals. These metabolic networks exhibit a rich variety of self-organized dynamic patterns, with e.g., phase transitions emerging in the whole activity of each network. We apply Hurst’s R/S analysis to several time series generated by these metabolic networks, and measure Hurst exponents H < 0.5 in most cases. This value of H, indicative of antipersistent processes, is detected at very high significance levels, estimated with detailed Monte Carlo simulations. These results show clearly the considered type of metabolic networks exhibit long-term memory phenomena.     

Species traits and abundance influence the organization of liana–tree antagonistic interaction

The interaction among species can be influenced by neutral processes, in which more abundant species have high effect on the structure of interaction, or can be influenced by trait matching. Despite both variables (abundance and species traits) influencing the interaction of species in mutualistic networks, few studies showed their importance in antagonistic networks. Here, we posed the question: what are the main predictors of the liana–tree interactions: species abundance, biological traits or both? In a savanna woodland fragment in south‐eastern Brazil, we sampled lianas and trees in 1 ha, where we recorded the abundance, maximum height and bark roughness of tree species, as well as abundance, maximum diameter and climbing system of liana species. For each species, we calculated their contribution to nestedness (n_i), which is a measure of network structure, and performed simple linear regressions between n_i and abundance and species traits. Abundant species contribute more to n_i than rare species, indicating that neutral processes affect interactions between lianas and trees, probably because lianas are opportunistic and climb trees in their neighbourhood. The only trait related to n_i was tree height, which can indicate that light availability can have a considerable role on network structure between both growth forms. Therefore, the importance of species abundance and tree height can be related to opportunism of lianas on climbing the most suitable tree in their neighbourhood.     

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

     

Arabidopsis mlo3 mutant plants exhibit spontaneous callose deposition and signs of early leaf senescence

Key message

Arabidopsis thaliana mlo3 mutant plants are not affected in pathogen infection phenotypes but—reminiscent of mlo2 mutant plants—exhibit spontaneous callose deposition and signs of early leaf senescence.

Abstract

The family of Mildew resistance Locus O (MLO) proteins is best known for its profound effect on the outcome of powdery mildew infections: when the appropriate MLO protein is absent, the plant is fully resistant to otherwise virulent powdery mildew fungi. However, most members of the MLO protein family remain functionally unexplored. Here, we investigate Arabidopsis thaliana MLO3, the closest relative of AtMLO2, AtMLO6 and AtMLO12, which are the Arabidopsis MLO genes implicated in the powdery mildew interaction. The co-expression network of AtMLO3 suggests association of the gene with plant defense-related processes such as salicylic acid homeostasis. Our extensive analysis shows that mlo3 mutants are unaffected regarding their infection phenotype upon challenge with the powdery mildew fungi Golovinomyces orontii and Erysiphe pisi, the oomycete Hyaloperonospora arabidopsidis, and the bacterial pathogen Pseudomonas syringae (the latter both in terms of basal and systemic acquired resistance), indicating that the protein does not play a major role in the response to any of these pathogens. However, mlo3 genotypes display spontaneous callose deposition as well as signs of early senescence in 6- or 7-week-old rosette leaves in the absence of any pathogen challenge, a phenotype that is reminiscent of mlo2 mutant plants. We hypothesize that de-regulated callose deposition in mlo3 genotypes might be the result of a subtle transient aberration of salicylic acid-jasmonic acid homeostasis during development.

     

Nested Canalyzing Depth and Network Stability

We introduce the nested canalyzing depth of a function, which measures the extent to which it retains a nested canalyzing structure. We characterize the structure of functions with a given depth and compute the expected activities and sensitivities of the variables. This analysis quantifies how canalyzation leads to higher stability in Boolean networks. It generalizes the notion of nested canalyzing functions (NCFs), which are precisely the functions with maximum depth. NCFs have been proposed as gene regulatory network models, but their structure is frequently too restrictive and they are extremely sparse. We find that functions become decreasingly sensitive to input perturbations as the canalyzing depth increases, but exhibit rapidly diminishing returns in stability. Additionally, we show that as depth increases, the dynamics of networks using these functions quickly approach the critical regime, suggesting that real networks exhibit some degree of canalyzing depth, and that NCFs are not significantly better than functions of sufficient depth for many applications of the modeling and reverse engineering of biological networks.     

Design patterns of biological cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.     

Measurement of the Girth of Coniferous Trees at Braemar in 1894

ABSTRACT

Background: Plant communities are usually characterised by species composition and abundance, but also underlie a multitude of complex interactions that we have only recently started unveiling. Yet, we are still far from understanding ecological and evolutionary processes shaping the network-level organisation of plant diversity, and to what extent these processes are specific to certain spatial scales or environments.

Aims: Understanding the systemic mechanisms of plant–plant network assembly and their consequences for diversity patterns.

Methods: We review recent methods and results of plant–plant networks.

Results: We synthetize how plant–plant networks can help us to: (a) assess how competition and facilitation may balance each other through the network; (b) analyse the role of plant–plant interactions beyond pairwise competition in structuring plant communities, and (c) forecast the ecological implications of complex species dependencies. We discuss pros and cons, assumptions and limitations of different approaches used for inferring plant–plant networks.

Conclusions: We propose novel opportunities for advancing plant ecology by using ecological networks that encompass different ecological levels and spatio-temporal scales, and incorporate more biological information. Embracing networks of interactions among plants can shed new light on mechanisms driving evolution and ecosystem functioning, helping us to mitigate diversity loss.     

A novel algorithm for alignment of multiple PPI networks based on simulated annealing

Background

To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem.

Results

In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR.

Conclusions

We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

     

Photoresponsivity and motility in the planarian Schmidtea mediterranea vary diurnally

ABSTRACT

The planarian flatworm has become one of the leading animal model systems for studying stem cell behavior and tissue regeneration. Recent studies have shown that components of the circadian clockwork have important roles in tissue homeostasis and repair. However, it remains unknown whether planarians exhibit circadian or diurnal rhythms in physiology or behavior. Here, we developed a behavioral assay to evaluate diurnal activity in planarians based upon their well-established propensity to swim away from light (negative phototaxis). We show evidence that the planarian Schmidtea mediterranea has diurnal variability in negative phototaxis as a function of daily variation in motility. We also demonstrate that variation in planarian motility over 48 h occurs with 24-h periodicity. Our data suggest that S. mediterranea may be a useful model for studying the interplay between the circadian system and tissue regeneration.     

Trafficking coordinate description of intracellular transport control of signaling networks

Many cellular networks rely on the regulated transport of their components to transduce extracellular information into precise intracellular signals. The dynamics of these networks is typically described in terms of compartmentalized chemical reactions. There are many important situations, however, in which the properties of the compartments change continuously in a way that cannot naturally be described by chemical reactions. Here, we develop an approach based on transport along a trafficking coordinate to precisely describe these processes and we apply it explicitly to the TGF-β signal transduction network, which plays a fundamental role in many diseases and cellular processes. The results of this newly introduced approach accurately capture the distinct TGF-β signaling dynamics of cells with and without cancerous backgrounds and provide an avenue to predict the effects of chemical perturbations in a way that closely recapitulates the observed cellular behavior.     

Genomic insights into the iron uptake mechanisms of the biomining microorganism Acidithiobacillus ferrooxidans

Commercial bioleaching of copper and the biooxidation of gold is a cost-effective and environmentally friendly process for metal recovery. A partial genome sequence of the acidophilic, bioleaching bacterium Acidithiobacillus ferrooxidans is available from two public sources. This information has been used to build preliminary models that describe how this microorganism confronts unusually high iron loads in the extremely acidic conditions (pH 2) found in natural environments and in bioleaching operations. A. ferrooxidans contains candidate genes for iron uptake, sensing, storage, and regulation of iron homeostasis. Predicted proteins exhibit significant amino acid similarity with known proteins from neutrophilic organisms, including conservation of functional motifs, permitting their identification by bioinformatics tools and allowing the recognition of common themes in iron transport across distantly related species. However, significant differences in amino acid sequence were detected in pertinent domains that suggest ways in which the periplasmic and outer membrane proteins of A. ferrooxidans maintain structural integrity and relevant protein-protein contacts at low pH. Unexpectedly, the microorganism also contains candidate genes, organized in operon-like structures that potentially encode at least 11 siderophore systems for the uptake of Fe(III), although it does not exhibit genes that could encode the biosynthesis of the siderophores themselves. The presence of multiple Fe(III) uptake systems suggests that A. ferrooxidans can inhabit aerobic environments where iron is scarce and where siderophore producers are present. It may also help to explain why it cannot tolerate high Fe(III) concentrations in bioleaching operations where it is out-competed by Leptospirillum species.

     

Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two Blattabacterium cuenoti strains,primary endosymbionts of cockroaches

Background

Cockroaches are terrestrial insects that strikingly eliminate waste nitrogen as ammonia instead of uric acid. Blattabacterium cuenoti (Mercier 1906) strains Bge and Pam are the obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively. The genomes of both bacterial endosymbionts have recently been sequenced, making possible a genome-scale constraint-based reconstruction of their metabolic networks. The mathematical expression of a metabolic network and the subsequent quantitative studies of phenotypic features by Flux Balance Analysis (FBA) represent an efficient functional approach to these uncultivable bacteria.

Results

We report the metabolic models of Blattabacterium strains Bge (iCG238) and Pam (iCG230), comprising 296 and 289 biochemical reactions, associated with 238 and 230 genes, and 364 and 358 metabolites, respectively. Both models reflect both the striking similarities and the singularities of these microorganisms. FBA was used to analyze the properties, potential and limits of the models, assuming some environmental constraints such as aerobic conditions and the net production of ammonia from these bacterial systems, as has been experimentally observed. In addition, in silico simulations with the iCG238 model have enabled a set of carbon and nitrogen sources to be defined, which would also support a viable phenotype in terms of biomass production in the strain Pam, which lacks the first three steps of the tricarboxylic acid cycle. FBA reveals a metabolic condition that renders these enzymatic steps dispensable, thus offering a possible evolutionary explanation for their elimination. We also confirm, by computational simulations, the fragility of the metabolic networks and their host dependence.

Conclusions

The minimized Blattabacterium metabolic networks are surprisingly similar in strains Bge and Pam, after 140 million years of evolution of these endosymbionts in separate cockroach lineages. FBA performed on the reconstructed networks from the two bacteria helps to refine the functional analysis of the genomes enabling us to postulate how slightly different host metabolic contexts drove their parallel evolution.