Network Reconstruction Research Articles

Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data.

Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproteomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.

Strong, healable materials with bio‐like ordered architectures and versatile functionality

AbstractThe capacity of biological tissues to undergo self‐healing is crucial for the performance of functions and the continuation of life. Conventional intrinsic self‐healing materials demonstrate analogous functionality depending on the dissociation‐recombination of reversible bonds with no need of extra repair agents. However, the trade‐off relationship between mechanical strength and self‐healing kinetics in intrinsic self‐healing systems, coupled with the lack of additional functionality, restricts their service life and practical applications. Diversified highly ordered structures in organisms significantly affect the energy dissipation mechanism, signal transmission efficiency, and molecular network reconstruction capability due to their multi‐dimensional differentiated macroscopic composite constructions, microscopic orientation textures, and topologies/bonding types at molecular level. These architectures exhibit distinctive strengthening mechanisms and functionalities, which provide valuable references. This review aims at providing the current status of advanced intrinsic self‐healing materials with biomimetic highly ordered internal micro/nanostructures. Through highlighting specific examples, the classifications, design inspirations, and fabrication strategies of these newly developed materials based on integrating dynamic interactions with ordered nano/microstructures are outlined. Furthermore, the strengthening and self‐healing balance mechanisms, structure–functionalization relationships, and potential application values are discussed. The review concludes with a perspective on the challenges, opportunities, and prospects for the development, application, and promotion of self‐healable materials with bio‐like ordered architectures.

Comparative meta-analysis of transcriptomic studies in spinal muscular atrophy: comparison between tissues and mouse models.

Spinal Muscular Atrophy (SMA), a neuromuscular disorder that leads to weakness in the muscles due to degeneration of motor neurons. Mutations in the survival motor neuron 1 (SMN1) gene leads to the deficiency of SMN protein that causes SMA. The molecular alterations associated with SMA extends across the transcriptome and proteome. Although several studies have examined the transcriptomic profile of SMA, the difference in experimental settings across these studies highlight the need for a comparative meta-analysis to better understand these differences. We conducted a systematic comparative meta-analysis of publicly available gene expression data from six selected studies to elucidate variations in the transcriptomic landscape across different experimental conditions, including tissue types and mouse models. We used both microarray and RNA-seq datasets, retrieved from Gene Expression Omnibus (GEO) and ArrayExpress (AE). Methods included normalization, differential expression analysis, gene-set enrichment analysis (GSEA), network reconstruction and co-expression analysis. Differential expression analysis revealed varying numbers of differentially expressed genes ranging between zero and 1,655 across the selected studies. Notably, the Metallothionein gene Mt2 was common in several of the eight comparisons. This highlights its role in oxidative stress and detoxification. Additionally, genes such as Hspb1, St14 and Sult1a1 were among the top ten differentially expressed genes in more than one comparison. The Snrpa1 gene, involved in pre-mRNA splicing, was upregulated in the spinal cord and has a strong correlation with other differentially expressed genes from other comparisons in our network reconstruction analysis. Gene-set enrichment analysis identified significant GO terms such as contractile fibers and myosin complexes in more than one comparison which highlights its significant role in SMA. Our comparative meta-analysis identified only few genes and pathways that were consistently dysregulated in SMA across different tissues and experimental settings. Conversely, many genes and pathways appeared to play a tissue-specific role in SMA. In comparison with the original studies, reproducibility was rather weak.

Three-Dimensional Semiconductor Network as Regulators of Energy Metabolism Drives Angiogenesis in Bone Regeneration.

Insufficient vascularization is a primary cause of bone implantation failure. The management of energy metabolism is crucial for the achievement of vascularized osseointegration. In light of the bone semiconductor property and the electric property of semiconductor heterojunctions, a three-dimensional semiconductor heterojunction network (3D-NTBH) implant has been devised with the objective of regulating cellular energy metabolism, thereby driving angiogenesis for bone regeneration. The three-dimensional heterojunction interfaces facilitate electron transfer and establish internal electric fields at the nanoscale interfaces. The 3D-NTBH was found to noticeably accelerate glycolysis in endothelial cells, thereby rapidly providing energy to support cellular metabolic activities and ultimately driving angiogenesis within the bone tissue. Molecular dynamic simulations have demonstrated that the 3D-NTBH facilitates the exposure of fibronectin's Arg-Gly-Asp peptide binding site, thereby regulating the glycolysis of endothelial cells. Further evidence suggests that 3D-NTBH promotes early vascular network reconstruction and bone regeneration in vivo. The findings of this research offer a promising research perspective for the design of vascularizing implants.

Physics-inspired machine learning detects “unknown unknowns” in networks: discovering network boundaries from observable dynamics

Abstract Dynamics on networks is often only partially observable in experiment, with many nodes being inaccessible or indeed the existence and properties of a larger unobserved network being unknown. This limits our ability to reconstruct the topology of the network and the strength of the interactions among even the observed nodes. Here, we show how machine learning inspired by physics can be utilized on noisy time series of such partially observed networks to determine which nodes of the observed part of a network form its boundary, i.e. have significant interactions with the unobserved part. This opens a route to reliable network reconstruction. We develop the method for arbitrary network dynamics and topologies and demonstrate it on a broad range of dynamics including non- linear coupled oscillators and chaotic attractors. Beyond these we focus in particular on biochemical reaction networks, where we apply the approach to the dynamics of the Epidermal Growth Factor Receptor (EGFR) network and show that it works even for substantial noise levels.

Research on deep unfolding network reconstruction method based on scalable sampling of transient signals.

In order to solve the problems of long reconstruction time and low reconstruction accuracy of compressed sensing reconstruction algorithm in the measurement of transient signals, a deep unfolding network reconstruction method based on scalable sampling is proposed to achieve fast and high-quality reconstruction of transient signal under low number of measurements. Firstly, the measurement process of compressed sensing is embedded into the neural network to realize automatic design and optimization of the observation matrix, which can reduce the number of measurements. Secondly, scalable sampling is introduced into the measurement process of compressed sensing, which can realize the training of data with different sampling ratios in the same model. Finally, a deep unfolding network model is designed to reconstruct the transient signal, which not only realizes the interpretability of the reconstructed network, but also achieves fast and high-quality reconstruction of the transient signal under the low number of measurements. Experimental results show that compared with the traditional compressed sensing reconstruction algorithms, the proposed method can obtain high-quality reconstruction accuracy with lower measurement times, and the reconstruction time is greatly reduced. The algorithm in this paper also obtains good reconstruction results under different sampling ratios, which shows that the method in this paper has good adaptability and effectiveness.

An Optimized Method for Reconstruction of Transcriptional Regulatory Networks in Bacteria Using ChIP-exo and RNA-seq Datasets.

Transcriptional regulatory networks (TRNs) in bacteria are crucial for elucidating the mechanisms that regulate gene expression and cellular responses to environmental stimuli. These networks delineate the interactions between transcription factors (TFs) and their target genes, thereby uncovering the regulatory processes that modulate gene expression under varying environmental conditions. Analyzing TRNs offers valuable insights into bacterial adaptation, stress responses, and metabolic optimization from an evolutionary standpoint. Additionally, understanding TRNs can drive the development of novel antimicrobial therapies and the engineering of microbial strains for biofuel and bioproduct production. This protocol integrates advanced data analysis pipelines, including ChEAP, DEOCSU, and DESeq2, to analyze omics datasets that encompass genome-wide TF binding sites and transcriptome profiles derived from ChIP-exo and RNA-seq experiments. This approach minimizes both the time required and the risk of bias, making it accessible to non-expert users. Key steps in the protocol include preprocessing and peak calling from ChIP-exo data, differential expression analysis of RNA-seq data, and motif and regulon analysis. This method offers a comprehensive and efficient framework for TRN reconstruction across various bacterial strains, enhancing both the accuracy and reliability of the analysis while providing valuable insights for basic and applied research.

Biologically inspired adaptive crack network reconstruction based on slime mould algorithm.

The dynamic crack propagation trajectories play a crucial role in enhancing our understanding of spatial mechanisms involved in crack expansion. However, visualization of internal cracks under complex crack conditions has always been a challenge. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. This study applied the slime mould algorithm to improve the accuracy of internal crack localization in rocks and employed Minimum spanning tree and Gaussian mixture model to construct the crack propagation trajectories. By introducing the concept of bond length, the evolution characteristics of crack levels were effectively characterized. Research results showed that this approach effectively preserves essential crack localization information while mitigating the influence of interfering parameters, providing crack characterization results that exhibit high consistency with actual fracture patterns. The curves of cumulative bond length and relative bond length over time conform to the trend of a Growth/Sigmoidal curve. The strength of the bond was correlated with the temporal process of crack propagation. This result could be helpful for analyzing crack trajectories and predicting rock stability.

AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium.

Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains.

Transcriptome-wide splicing network reveals specialized regulatory functions of the core spliceosome.

The spliceosome is the complex molecular machinery that sequentially assembles on eukaryotic messenger RNA precursors to remove introns (pre-mRNA splicing), a physiologically regulated process altered in numerous pathologies. We report transcriptome-wide analyses upon systematic knock down of 305 spliceosome components and regulators in human cancer cells and the reconstruction of functional splicing factor networks that govern different classes of alternative splicing decisions. The results disentangle intricate circuits of splicing factor cross-regulation, reveal that the precise architecture of late-assembling U4/U6.U5 tri-small nuclear ribonucleoprotein (snRNP) complexes regulates splice site pairing, and discover an unprecedented division of labor among protein components of U1 snRNP for regulating exon definition and alternative 5' splice site selection. Thus, we provide a resource to explore physiological and pathological mechanisms of splicing regulation.

In Vitro vs. In Vivo Transcriptomic Approach Revealed Core Pathways of Nitrogen Deficiency Response in Tea Plant (Camellia sinensis (L.) Kuntze).

For the first time, we used an in vitro vs. in vivo experimental design to reveal core pathways under nitrogen deficiency (ND) in an evergreen tree crop. These pathways were related to lignin biosynthesis, cell redox homeostasis, the defense response to fungus, the response to Karrikin, amino acid transmembrane transport, the extracellular region, the cellular protein catabolic process, and aspartic-type endopeptidase activity. In addition, the mitogen-activated protein kinase pathway and ATP synthase (ATP)-binding cassette transporters were significantly upregulated under nitrogen deficiency in vitro and in vivo. Most of the MAPK downstream genes were related to calcium signaling (818 genes) rather than hormone signaling (157 genes). Moreover, the hormone signaling pathway predominantly contained auxin- and abscisic acid-related genes, indicating the crucial role of these hormones in ND response. Overall, 45 transcription factors were upregulated in both experiments, 5 WRKYs, 3 NACs, 2 MYBs, 2 ERFs, HD-Zip, RLP12, bHLH25, RADIALIS-like, and others, suggesting their ND regulation is independent from the presence of a root system. Gene network reconstruction displayed that these transcription factors participate in response to fungus/chitin, suggesting that nitrogen response and pathogen response have common regulation. The upregulation of lignin biosynthesis genes, cytochrome genes, and strigalactone response genes was much more pronounced under in vitro ND as compared to in vivo ND. Several cell wall-related genes were closely associated with cytochromes, indicating their important role in flavanols biosynthesis in tea plant. These results clarify the signaling mechanisms and regulation of the response to nitrogen deficiency in evergreen tree crops.

Revealing host genome–microbiome networks underlying feed efficiency in dairy cows

Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1–6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli (μ(z)) and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: ΛCDM and Chevallier–Linder–Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with ΛCDM and CPL.

Mapping Health Pathways: A Network Analysis for Improved Illness Prediction

ABSTRACTComplex networks and network reconstruction have now become powerful tools for exploring relationships and interactions within systems across various fields to identify patterns and increase predictive accuracy. It provides a structured framework to investigate the relations or connections among individual components or entities within a system. Recently, the importance of the healthcare prediction model in life‐saving efforts has increased. A model is proposed for health prediction that uses network reconstruction methods to build a network from available information, representing features as nodes and the relationships between them as edges. Subsequently, a method is introduced to calculate the value of the decision parameter () for predicting an individual's health status. The proposed model shows substantial improvement over the current state of the prediction model. The first aim of the proposed model is to classify an individual into their appropriate class properly. Another contribution of the proposed model is to measure the factors that are responsible for classifying an individual into a class that shows its significance and impact over the existing state‐of‐the‐art. It provides a new dimension to the prediction mode, emphasizing the importance of identifying critical features and their interdependencies for personalized health diagnostics.

Synaptotagmin 4 Supports Spontaneous Axon Sprouting after Spinal Cord Injury.

Injuries to the central nervous system (CNS) can cause severe neurological deficits. Axonal regrowth is a fundamental process for the reconstruction of compensatory neuronal networks after injury; however, it is extremely limited in the adult mammalian CNS. In this study, we conducted a loss-of-function genetic screen in cortical neurons, combined with a Web resource-based phenotypic screen, and identified synaptotagmin 4 (Syt4) as a novel regulator of axon elongation. Silencing Syt4 in primary cultured cortical neurons inhibits neurite elongation, with changes in gene expression involved in signaling pathways related to neuronal development. In a spinal cord injury model, inhibition of Syt4 expression in cortical neurons prevented axonal sprouting of the corticospinal tract, as well as neurological recovery after injury. These results provide a novel therapeutic approach to CNS injury by modulating Syt4 function.

Multilingual memory albums in Wisconsin and the Duchy of Schleswig

Memory albums, sometimes known as poetry albums or autograph books (German Stammbuch; Danish stambog), have existed in some form for centuries, and by the 19th century, the practice was common among many members of society and were often kept by school-aged children or young adults. Such albums are considered to have been kept for the purposes of group affiliation and for collecting personal memories for the future. Because of this, they are a useful source in historical sociolinguistic research, particularly regarding the reconstruction of social networks and understanding community of practice. By focusing on memory albums created during the same time period in two distinct locations by bi- or multilingual social groups, this chapter aims to shed light on differences and similarities of the communities of practice taking part in memory album creation; one in Wisconsin, the other in the Duchy of Schleswig. Results from this chapter show that for each location, a dominant language is apparent, and similarities arise according to the sex of the album owner.

Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data.

Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproetiomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.

Platform Ecosystem: The Logic of Digital Business Model Innovation

This paper examines the impact of big data capability and value network reconstruction on digital business platforms ecosystem cocreation value. Furthermore, it presents a mechanism model, with business model innovation as the mediating variable. The findings of this study are of considerable importance for digital business platforms seeking to enhance their platform ecosystem co-creation value, particularly in light of their big data capability and value network reconstruction.

Concerted transcriptional regulation of the morphogenesis of hypothalamic neurons by ONECUT3

Acquisition of specialized cellular features is controlled by the ordered expression of transcription factors (TFs) along differentiation trajectories. Here, we find a member of the Onecut TF family, ONECUT3, expressed in postmitotic neurons that leave their Ascl1+/Onecut1/2+ proliferative domain in the vertebrate hypothalamus to instruct neuronal differentiation. We combined single-cell RNA-seq and gain-of-function experiments for gene network reconstruction to show that ONECUT3 affects the polarization and morphogenesis of both hypothalamic GABA-derived dopamine and thyrotropin-releasing hormone (TRH)+ glutamate neurons through neuron navigator-2 (NAV2). In vivo, siRNA-mediated knockdown of ONECUT3 in neonatal mice reduced NAV2 mRNA, as well as neurite complexity in Onecut3-containing neurons, while genetic deletion of Onecut3/ceh-48 in C. elegans impaired neurocircuit wiring, and sensory discrimination-based behaviors. Thus, ONECUT3, conserved across neuronal subtypes and many species, underpins the polarization and morphological plasticity of phenotypically distinct neurons that descend from a common pool of Ascl1+ progenitors in the hypothalamus.

Extinction cascades, community collapse, and recovery across a Mesozoic hyperthermal event

Mass extinctions are considered to be quintessential examples of Court Jester drivers of macroevolution, whereby abiotic pressures drive a suite of extinctions leading to huge ecosystem changes across geological timescales. Most research on mass extinctions ignores species interactions and community structure, limiting inference about which and why species go extinct, and how Red Queen processes that link speciation to extinction rates affect the subsequent recovery of biodiversity, structure and function. Here, we apply network reconstruction, secondary extinction modelling and community structure analysis to the Early Toarcian (Lower Jurassic; 183 Ma) Extinction Event and recovery. We find that primary extinctions targeted towards infaunal guilds, which caused secondary extinction cascades to higher trophic levels, reproduce the empirical post-extinction community most accurately. We find that the extinction event caused a switch from a diverse community with high levels of functional redundancy to a less diverse, more densely connected community of generalists. Recovery was characterised by a return to pre-extinction levels of some elements of community structure and function prior to the recovery of biodiversity. Full ecosystem recovery took ~7 million years at which point we see evidence of dramatically increased vertical structure linked to the Mesozoic Marine Revolution and modern marine ecosystem structure.