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The response of a biological network to ligand binding is of crucial importance for regulatory control in various cellular biophysical processes that is achieved with information transmission through the different ligand-bound states of such networks. In this work, we address a vital issue regarding the link between the information content of such network states and the experimentally measurable binding statistics. Several fundamental networks of cooperative ligand binding, with the bound states being adjacent in time only and in both space and time, are considered for this purpose using the chemical master equation approach. To express the binding characteristics in the language of information, a quantity denoted as differential information index is employed based on the Shannon information. The index, determined for the whole network, follows a linear relationship with (logarithmic) ligand concentration with a slope equal to the size of the system. On the other hand, the variation of Shannon information associated with the individual network states and the logarithmic sensitivity of its slope are shown to have generic forms related to the average binding number and variance, respectively, the latter yielding the Hill slope, the phenomenological measure of cooperativity. Furthermore, the variation of Shannon information entropy, the average of Shannon information, is also shown to be related to the average binding. [ABSTRACT FROM AUTHOR]

Protein kinases are critical components of a myriad biological processes and strongly associated with various diseases. While kinase research has been a point of focus in biomedical research for several decades, a large portion of the kinome is still considered understudied or "dark," because prior research is targeted towards a subset of kinases with well-established roles in cellular processes. We present an empirical and in-silico hybrid workflow to extend the functional knowledge of understudied kinases. Utilizing multiplex peptide activity arrays and robust in-silico analyses, we extended the functional knowledge of five dark tyrosine kinases (AATK, EPHA6, INSRR, LTK, TNK1) and explored their roles in schizophrenia, Alzheimer's dementia (AD), and major depressive disorder (MDD). Using this hybrid approach, we identified 195 novel kinase-substrate interactions with variable degrees of affinity and linked extended functional networks for these kinases to biological processes that are impaired in psychiatric and neurological disorders. Biochemical assays and mass spectrometry were used to confirm a putative substrate of EPHA6, an understudied dark tyrosine kinase. We examined the EPHA6 network and knowledgebase in schizophrenia using reporter peptides identified and validated from the multi-plex array with high affinity for phosphorylation by EPHA6. Identification and confirmation of putative substrates for understudied kinases provides a wealth of actionable information for the development of new drug treatments as well as exploration of the pathophysiology of disease states using signaling network approaches. [ABSTRACT FROM AUTHOR]

This paper derives well-posedness and asymptotic results that provide qualitative information about the behavior, mechanism and strategies used by living organisms to navigate their biological networks. Chemical driven swimming is a captivating phenomenon that is observed in various living organisms like bacteria and protozoa but the problem in weighted networks is more complex, since the equations of parabolic-parabolic Keller-Segel model coupled with incompressible Navier-Stokes equations must be reformulated in a discrete setting. The starting point is to transpose the coupled system from the Euclidean case to the connected networks with certain network-theoretic simplified approaches, which yields fruitful key results. These results not only enable the construction of global solutions but also serve as a foundation for determining information about the stability and large time behavior of the system. Then, decay rates are well established to predict important features, such as how quickly weak solutions at a given point decrease over time due to dissipative processes. Additionally, the L 1 - convergence of cell densities towards the self-similar Gaussian solution of the heat equation is well proved by time dependent scaling, which shows that the solution maintains its shape and only scales in time and space as time evolves. Finally, this paper includes many numerical tests through a recent robust numerical scheme to illustrate the theoretical results and to develop computational control and prediction of living organisms' trajectories around central nodes in networked flows. [ABSTRACT FROM AUTHOR]

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the collectively most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. Our approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. It thus overcomes common problems in hypothesis testing-based motif analysis and guarantees robust statistical inference. We validate our methodology on numerical data and then apply it on synaptic-resolution biological neural networks, as a medium for comparative connectomics, by evaluating their respective compressibility and characterize their inferred circuit motifs. Author summary: Networks provide a useful abstraction to study complex systems by focusing on the interplay of the units composing a system rather than on their individual function. Network theory has proven particularly powerful for unraveling how the structure of connections in biological networks influence the way they may process and relay information in a variety of systems ranging from the microscopic scale of biochemical processes in cells to the macroscopic scales of social and ecological networks. Of particular interest are small stereotyped circuits in such networks, termed motifs, which may correspond to building blocks implementing fundamental operations, e.g., logic gates or filters. We here present a new tool that finds sets of motifs in networks based on an information-theoretic measure of how much they allow to compress the network. This approach allows us to evaluate the collective significance of sets of motifs, as opposed to only individual motifs. We apply our methodology to compare the neural wiring diagrams, termed "connectomes", of the tadpole larva Ciona intestinalis, the ragworm Platynereis dumerelii, and the nematode Caenorhabditis elegans and the fruitfly Drosophila melanogaster at different developmental stages. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Study pipeline. Highlights: The present study has focused on the comorbidities associated with Alzheimer's disease (AD) by constructing a landscape of these comorbidities at various levels, including diseases, genetics, and pathways. 1. The study findings reveal novel and significant pathways that contribute to the etiology of AD and its comorbidities. 2. By exploring pleiotropic genes and reported biomarkers of AD comorbidities, the study has identified several potential diagnostic biomarker candidates for AD. [ABSTRACT FROM AUTHOR]

Widespread interest in nondestructive biomarkers of aging has led to a multitude of biological ages that each proffers a "true" health-adjusted individual age. Although each measure provides salient information on the aging process, they are each univariate, in contrast to the "hallmark" and "pillar" theories of aging, which are explicitly multidimensional, multicausal, and multiscale. Fortunately, multiple biological ages can be systematically combined into a multidimensional network representation. The interaction network between these biological ages permits analysis of the multidimensional effects of aging, as well as quantification of causal influences during both natural aging and, potentially, after anti-aging intervention. The behavior of the system as a whole can then be explored using dynamical network stability analysis, which identifies new, efficient biomarkers that quantify long-term resilience scores on the timescale between measurements (years). We demonstrate this approach using a set of 8 biological ages from the longitudinal Swedish Adoption/Twin Study of Aging (SATSA). After extracting an interaction network between these biological ages, we observed that physiological age, a proxy for cardiometabolic health, serves as a central node in the network, implicating it as a key vulnerability for slow, age-related decline. We furthermore show that while the system as a whole is stable, there is a weakly stable direction along which recovery is slow—on the timescale of a human lifespan. This slow direction provides an aging biomarker, which correlates strongly with chronological age and predicts longitudinal decline in health—suggesting that it estimates an important driver of age-related changes. [ABSTRACT FROM AUTHOR]

The use of graph centrality measures applied to biological networks, such as protein interaction networks, underpins much research into identifying key players within biological processes. This approach however is restricted to dyadic interactions and it is well-known that in many instances interactions are polyadic. In this study we illustrate the merit of using hypergraph centrality applied to a hypernetwork as an alternative. Specifically, we review and propose an extension to a recently introduced node and edge nonlinear hypergraph centrality model which provides mutually dependent node and edge centralities. A Saccharomyces Cerevisiae protein complex hypernetwork is used as an example application with nodes representing proteins and hyperedges representing protein complexes. The resulting rankings of the nodes and edges are considered to see if they provide insight into the essentiality of the proteins and complexes. We find that certain variations of the model predict essentiality more accurately and that the degree-based variation illustrates that the centrality-lethality rule extends to a hypergraph setting. In particular, through exploitation of the models flexibility, we identify small sets of proteins densely populated with essential proteins. One of the key advantages of applying this model to a protein complex hypernetwork is that it also provides a classification method for protein complexes, unlike previous approaches which are only concerned with classifying proteins. [ABSTRACT FROM AUTHOR]

Physical contact between cells – often mediated by transmembrane proteins – is an important factor in cell–cell interactions. Cell–cell contact affects gene expression, but its effect is still poorly understood. Many molecular methods have been developed to deliver measurable signals when physical contact occurs between two cells. Contrasting expression profiles of single cell types and multicellular conjugates highlights contact-specific gene expression changes. Spatial profiling of the transcriptome provides a novel way to measure gene expression changes resulting from cell–cell contact while maintaining a broader cellular context and in vivo tissue structure. Cell–cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements – including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools – have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell–cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell–cell communication. [ABSTRACT FROM AUTHOR]

Network science has witnessed a surge in popularity, driven by the transformative power of node representation learning for diverse applications like social network analysis and biological modeling. While shallow embedding algorithms excel at capturing network structure, they face a critical limitation—failing to generalize to unseen nodes. This paper addresses this challenge by introducing Inductive Shallow Node Embedding—as a main contribution—pioneering a novel approach that extends shallow embeddings to the realm of inductive learning. It has a novel encoder architecture that captures the local neighborhood structure of each node, enabling effective generalization to unseen nodes. In the generalization, robustness is essential to avoid degradation of performance arising from noise in the dataset. It has been theoretically proven that the covariance of the additive noise term in the proposed model is inversely proportional to the cardinality of a node's neighbors. Another contribution is a mathematical lower bound to quantify the robustness of node embeddings, confirming its advantage over traditional shallow embedding methods, particularly in the presence of parameter noise. The proposed method demonstrably excels in dynamic networks, consistently achieving over 90% performance on previously unseen nodes compared to nodes encountered during training on various benchmarks. The empirical evaluation concludes that our method outperforms competing methods on the vast majority of datasets in both transductive and inductive tasks. [ABSTRACT FROM AUTHOR]

Background: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods. Results: In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies. Conclusions: By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been a surge in research attention towards tasks involving graph data, largely due to the impressive performance demonstrated by graph neural networks (GNNs) in handling such information. Currently, out-of-distribution (OOD) detection in graphs is a hot research topic. The goal of graph OOD detection is to identify nodes or new graphs that differ from the training data distribution, primarily in terms of attributes and structures. OOD detection is crucial for enhancing the stability, security, and robustness of models. In various applications, such as biological networks and financial fraud, graph OOD detection can help models identify anomalies or unforeseen situations, thereby enabling appropriate responses. In node-level OOD detection, existing models typically only consider first-order neighbors. This paper introduces graph diffusion to the OOD detection task for the first time, proposing the HOOD model, a graph diffusion-based OOD node detection algorithm. Specifically, the original graph is processed through graph diffusion to obtain a new graph that can directly capture high-order neighbor information, overcoming the limitation that message passing must go through first-order neighbors. The new graph is then sparsified using a top-k approach. Based on entropy information, regularization is employed to ensure the uncertainty of OOD nodes, thereby giving these nodes higher scores and enabling the model to effectively detect OOD nodes while ensuring the accuracy of in-distribution node classification. Experimental results demonstrate that the HOOD model outperforms existing methods in both node classification and OOD detection tasks on multiple benchmarks, highlighting its robustness and effectiveness. [ABSTRACT FROM AUTHOR]

Metals play a crucial role in the human body, especially as ions in metalloproteins. Essential metals, such as calcium, iron, and zinc are crucial for various physiological functions, but their interactions within biological networks are complex and not fully understood. Mesenchymal stem/stromal cells (MSCs) are essential for tissue regeneration due to their ability to differentiate into various cell types. This review article addresses the effects of physiological and unphysiological, but not directly toxic, metal ion concentrations, particularly concerning MSCs. Overloading or unbalancing of metal ion concentrations can significantly impair the function and differentiation capacity of MSCs. In addition, excessive or unbalanced metal ion concentrations can lead to oxidative stress, which can affect viability or inflammation. Data on the effects of metal ions on MSC differentiation are limited and often contradictory. Future research should, therefore, aim to clarify the mechanisms by which metal ions affect MSC differentiation, focusing on aspects such as metal ion interactions, ion concentrations, exposure duration, and other environmental conditions. Understanding these interactions could ultimately improve the design of biomaterials and implants to promote MSC-mediated tissue regeneration. It could also lead to the development of innovative therapeutic strategies in regenerative medicine. [ABSTRACT FROM AUTHOR]

The rising incidence of metabolic diseases is linked to elevated blood glucose levels, contributing to conditions such as diabetes and promoting the accumulation of advanced glycation end products (AGEs). AGEs, formed by non-enzymatic reactions between sugars and proteins, build up in tissues and are implicated in various diseases. This article explores the relationship between glycemic control and AGE accumulation, focusing on fertility implications. A computational model using network theory was developed, featuring a molecular database and a network with 145 nodes and 262 links, categorized as a Barabasi–Albert scale-free network. Three main subsets of nodes emerged, centered on glycemic control, fertility, and immunity, with AGEs playing a critical role. The transient receptor potential vanilloid 1 (TRPV1), a receptor expressed in several tissues including sperm, was identified as a key hub, suggesting that the modulation of TRPV1 in sperm by AGEs may influence fertility. Additionally, a novel link between glycemic control and immunity was found, indicating that immune cells may play a role in endocytosing specific AGEs. This discovery underscores the complex interplay between glycemic control and immune function, with significant implications for metabolic, immune health, and fertility. [ABSTRACT FROM AUTHOR]

Endocrine-disrupting chemicals (EDCs) impair growth and development. While EDCs can occur naturally in aquatic ecosystems, they are continuously introduced through anthropogenic activities such as industrial effluents, pharmaceutical production, wastewater, and mining. To elucidate the chronic toxicological effects of endocrine-disrupting chemicals (EDCs) on aquatic organisms, we collected experimental data from a standardized chronic exposure test using Daphnia magna (D. magna), individuals of which were exposed to a potential EDC, trinitrotoluene (TNT). The chronic toxicity effects of this compound were explored through differential gene expression, gene ontology, network construction, and putative adverse outcome pathway (AOP) proposition. Our findings suggest that TNT has detrimental effects on the upstream signaling of Tcf/Lef, potentially adversely impacting oocyte maturation and early development. This study employs diverse bioinformatics approaches to elucidate the gene-level toxicological effects of chronic TNT exposure on aquatic ecosystems. The results provide valuable insights into the molecular mechanisms of the adverse impacts of TNT through network construction and putative AOP proposition. [ABSTRACT FROM AUTHOR]

Background: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging. Results: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property. Conclusions: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM. [ABSTRACT FROM AUTHOR]

Bats are natural reservoirs for zoonotic pathogens, yet the determinants of microbial persistence as well as the specific functionality of their immune system remain largely enigmatic. Their propensity to harbor viruses lethal to humans and/or livestock, mostly in absence of clinical disease, makes bats stand out among mammals. Defending against pathogens relies on avoidance, resistance, and/or tolerance strategies. In bats, disease tolerance has recently gained increasing attention as a prevailing host defense paradigm. We here summarize the current knowledge on immune responses in bats in the context of infection with zoonotic agents and discuss concepts related to disease tolerance. Acknowledging the wide diversity of bats, the broad spectrum of bat-associated microbial species, and immune-related knowledge gaps, we identify research priorities necessary to provide evidence-based proofs for disease tolerance in bats. Since disease tolerance relies on networks of biological processes, we emphasize that investigations beyond the immune system, using novel technologies and computational biology, could jointly advance our knowledge about mechanisms conferring bats reservoir abilities. Although disease tolerance may not be the "one fit all" defense strategy, deciphering disease tolerance in bats could translate into novel therapies and inform prevention of spillover infections to humans and livestock. [ABSTRACT FROM AUTHOR]

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease. Author summary: Biological regulation is governed by a complex network of interactions involving transcription factors, RNA-binding proteins, and microRNAs. To reveal the regulatory programs underlying gene expression modulations, researchers often take advantage of gene-set enrichment analysis, an approach that studies concerted changes in a group of genes rather than observing genes in isolation. Previously, we developed a tool called iPAGE to facilitate this analysis. However, both iPAGE and other similar tools implicitly assume that different genes have uniform gene-set membership. Our recent observations challenge this assumption, revealing that some genes form regulatory networks far more frequently than others. These technical biases and redundancies in gene-set annotations complicate the accurate inference of true regulatory relationships in specific contexts. To overcome these limitations, we introduced pyPAGE, an enhanced method that extends our information-theoretic framework by incorporating conditional mutual information to account for specific biases and artifacts. We applied pyPAGE to Alzheimer's disease, a neurodegenerative disorder marked by its complexity and characterized by progressive cognitive decline, memory loss, and behavioral changes. Pathological processes of Alzheimer's disease involve various cell types and diverse molecular mechanisms, which pose significant challenges for study. In our study we took advantage of our newly developed framework pyPAGE, performing analysis of gene expression changes at both tissue and cell type levels. This way we were able to describe regulation patterns of known regulators of Alzheimer's as well as several new ones. Additionally, we performed a cohort study of the association between the enrichment of the identified regulons and survival prognosis for patients, showing that increased activity of a subset of RBPs is positively associated with a lifespan. In total the results highlight the utility of pyPAGE and provide a valuable set of biomarkers for Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Identifying critical genes is important for understanding the pathogenesis of complex diseases. Traditional studies typically comparing the change of biomecules between normal and disease samples or detecting important vertices from a single static biomolecular network, which often overlook the dynamic changes that occur between different disease stages. However, investigating temporal changes in biomolecular networks and identifying critical genes is critical for understanding the occurrence and development of diseases. Methods: A novel method called Quantifying Importance of Genes with Tensor Decomposition (QIGTD) was proposed in this study. It first constructs a time series network by integrating both the intra and inter temporal network information, which preserving connections between networks at adjacent stages according to the local similarities. A tensor is employed to describe the connections of this time series network, and a 3-order tensor decomposition method was proposed to capture both the topological information of each network snapshot and the time series characteristics of the whole network. QIGTD is also a learning-free and efficient method that can be applied to datasets with a small number of samples. Results: The effectiveness of QIGTD was evaluated using lung adenocarcinoma (LUAD) datasets and three state-of-the-art methods: T-degree, T-closeness, and T-betweenness were employed as benchmark methods. Numerical experimental results demonstrate that QIGTD outperforms these methods in terms of the indices of both precision and mAP. Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation. Conclusion: In conclusion, QIGTD effectively captures the temporal changes in gene networks and identifies critical genes. It provides a valuable tool for studying temporal dynamics in biological networks and can aid in understanding the underlying mechanisms of diseases such as LUAD. [ABSTRACT FROM AUTHOR]

Significance: Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology. Aim: The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods. Approach: Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension 250 × 250 × 84 pixels. We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention. Results: In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted F1 score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumormargins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted F1 score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted F1 score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features. Conclusions: The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the F1 score, and the sensitivity score. [ABSTRACT FROM AUTHOR]

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery. [ABSTRACT FROM AUTHOR]

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF–enhancer linkage prediction, and enhancer–gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer–gene relations associated with pseudotime, as well as key TF-enhancer–gene relations and TF cooperation underlying tumor cells. [ABSTRACT FROM AUTHOR]

A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coalesce to form a complex reaction network. Biochemical pathways play a critical role in the biochemical domain as they can reveal the flow of biochemical reactions in living organisms, making them essential for understanding life processes. Existing studies of biochemical pathway networks are mainly based on experimentation and pathway database analysis methods, which are plagued by substantial cost constraints. Inspired by the success of representation learning approaches in biomedicine, we develop the biochemical pathway prediction (BPP) platform, which is an automatic BPP platform to identify potential links or attributes within biochemical pathway networks. Our BPP platform incorporates a variety of representation learning models, including the latest hypergraph neural networks technology to model biochemical reactions in pathways. In particular, BPP contains the latest biochemical pathway-based datasets and enables the prediction of potential participants or products of biochemical reactions in biochemical pathways. Additionally, BPP is equipped with an SHAP explainer to explain the predicted results and to calculate the contributions of each participating element. We conduct extensive experiments on our collected biochemical pathway dataset to benchmark the effectiveness of all models available on BPP. Furthermore, our detailed case studies based on the chronological pattern of our dataset demonstrate the effectiveness of our platform. Our BPP web portal, source code and datasets are freely accessible at https://github.com/Glasgow-AI4BioMed/BPP. [ABSTRACT FROM AUTHOR]

Objective: To investigate the role and function of eIF6 in gastric cancer (GC). Methods: The expression level of eIF6 in GC tissues and normal tissues was detected in different high-throughput sequencing cohorts. Survival analysis, gene differential analysis, and enrichment analysis were performed in the TCGA cohort. Biological networks centered on eIF6 were constructed through two different databases. Immunohistochemistry (IHC) and Western blot were used to detect protein expression of eIF6, and qRT-PCR was used to detect eIF6 mRNA expression. The correlation between the expression of eIF6 in GC tissues and clinicopathological parameters of GC was analyzed. siRNA knockout of eIF6 was used to study the proliferation, migration, and invasion. The effects of eIF6 on cell cycle and Cyclin B1 were detected by flow cytometry and Western blot. Results: eIF6 was significantly overexpressed in GC tissues and predicted poor prognosis. In addition, 113 differentially expressed genes were detected in cancer-related biological pathways and functions by differential analysis. Biological networks revealed interactions of genes and proteins with eIF6. The expression intensity of eIF6 in cancer tissues was higher than that in adjacent tissues (P = 0.0001), confirming the up-regulation of eIF6 expression in GC tissues. The expression level of eIF6 was statistically significant with pTNM stage (P = 0.006). siRNA knockout of eIF6 significantly reduced the proliferation, colony formation, migration, and invasion ability of GC cells. Silencing of eIF6 also inhibited the cell cycle of GC cells in G2/M phase and decreased the expression level of CyclinB1. Conclusion: Our study suggests that eIF6 is up-regulated in GC and may promote the proliferation, migration, and invasion of GC by regulating cell cycle. [ABSTRACT FROM AUTHOR]

Our study highlights the apoptosis, cell cycle, DNA ploidy, and autophagy molecular mechanisms network to identify prostate pathogenesis and its prognostic role. Caspase 3/7 expressions, cell cycle, adhesion glycoproteins, autophagy, nuclear shrinkage, and oxidative stress by flow-cytometry analysis are used to study the BPH microenvironment's heterogeneity. A high late apoptosis expression by caspases 3/7 activity represents an unfavorable prognostic biomarker, a dependent predictor factor for cell adhesion, growth inhibition by arrest in the G2/M phase, and oxidative stress processes network. The heterogeneous aggressive phenotype prostate adenoma primary cell cultures present a high S-phase category (>12%), with an increased risk of death or recurrence due to aneuploid status presence, representing an unfavorable prognostic biomarker, a dependent predictor factor for caspase 3/7 activity (late apoptosis and necrosis), and cell growth inhibition (G2/M arrest)-linked mechanisms. Increased integrin levels in heterogenous BPH cultures suggest epithelial–mesenchymal transition (EMT) that maintains an aggressive phenotype by escaping cell apoptosis, leading to the cell proliferation necessary in prostate cancer (PCa) development. As predictor biomarkers, the biological mechanisms network involved in apoptosis, the cell cycle, and autophagy help to establish patient prognostic survival or target cancer therapy development. [ABSTRACT FROM AUTHOR]

Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysis and biological molecular network analysis provide robust solutions. This study proposed a random walk with a multi-restart probability (RWMRP) algorithm, based on the Restarted Random Walk (RWR) algorithm, to operate on rice MultiPlex biological networks. It explores the interactions between biological molecules across various levels and ranks potential genes. RWMRP uses eigenvector centrality to evaluate node importance in the network and adjusts the restart probabilities accordingly, diverging from the uniform restart probability employed in RWR. In the random walk process, it can be better to consider the global relationships in the network. Firstly, we constructed a MultiPlex biological network by integrating the rice protein–protein interaction, gene pathway, and gene co-expression network. Then, we employed RWMRP to predict the potential genes associated with rice tolerance to drought stress. Enrichment and correlation analyses resulted in the identification of 12 drought-related genes. We further conducted quantitative real-time polymerase chain reaction (qRT-PCR) analysis on these 12 genes, ultimately identifying 10 genes responsive to drought stress. [ABSTRACT FROM AUTHOR]

Summary: Grain filling in maize (Zea mays) is intricately linked to cell development, involving the regulation of genes responsible for the biosynthesis of storage reserves (starch, proteins, and lipids) and phytohormones. However, the regulatory network coordinating these biological functions remains unclear.In this study, we identified 1744 high‐confidence target genes co‐regulated by the transcription factors (TFs) ZmNAC128 and ZmNAC130 (ZmNAC128/130) through chromatin immunoprecipitation sequencing coupled with RNA‐seq analysis in the zmnac128/130 loss‐of‐function mutants. We further constructed a hierarchical regulatory network using DNA affinity purification sequencing analysis of downstream TFs regulated by ZmNAC128/130.In addition to target genes involved in the biosynthesis of starch and zeins, we discovered novel target genes of ZmNAC128/130 involved in the biosynthesis of lipids and indole‐3‐acetic acid (IAA). Consistently, the number of oil bodies, as well as the contents of triacylglycerol, and IAA were significantly reduced in zmnac128/130. The hierarchical regulatory network centered by ZmNAC128/130 revealed a significant overlap between the direct target genes of ZmNAC128/130 and their downstream TFs, particularly in regulating the biosynthesis of storage reserves and IAA.Our results indicated that the biosynthesis of storage reserves and IAA is coordinated by a multi‐TFs hierarchical regulatory network in maize endosperm. [ABSTRACT FROM AUTHOR]

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed. [ABSTRACT FROM AUTHOR]

Irreversibility, in which a transient perturbation leaves a system in a new state, is an emergent property in systems of interacting entities. This property has well-established implications in statistical physics but remains underexplored in biological networks, especially for bacteria and other prokaryotes whose regulation of gene expression occurs predominantly at the transcriptional level. Focusing on the reconstructed regulatory network of Escherichia coli, we examine network responses to transient single-gene perturbations. We predict irreversibility in numerous cases and find that the incidence of irreversibility increases with the proximity of the perturbed gene to positive circuits in the network. Comparison with experimental data suggests a connection between the predicted irreversibility to transient perturbations and the evolutionary response to permanent perturbations. [ABSTRACT FROM AUTHOR]

Single-cell omics technologies can measure millions of cells for up to thousands of biomolecular features, enabling data-driven studies of complex biological networks. However, these high-throughput experimental techniques often cannot track individual cells over time, thus complicating the understanding of dynamics such as time trajectories of cell states. These "dynamical phenotypes" are key to understanding biological phenomena such as differentiation fates. We show by mathematical analysis that, in spite of high dimensionality and lack of individual cell traces, three time-points of single-cell omics data are theoretically necessary and sufficient to uniquely determine the network interaction matrix and associated dynamics. Moreover, we show through numerical simulations that an interaction matrix can be accurately determined with three or more time-points even in the presence of sampling and measurement noise typical of single-cell omics. Our results can guide the design of single-cell omics time-course experiments, and provide a tool for data-driven phase-space analysis. [ABSTRACT FROM AUTHOR]

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression. Author summary: Cancer driver identification is of great value in cancer research. Recent advancements have demonstrated that incorporating the intricate regulatory relationships among genes can facilitate driver genes identification. However, current methods do not fully capture the regulatory relationships at different layers. In this study, we propose a new method for predicting cancer driver genes (IMI-driver), which integrates multi-omics data into eight biological networks (including common networks such as protein-protein interaction networks and various cancer-specific networks) and embeds multi-level network information as features to predict driver gene sets. Experiment demonstrates that our method outperforms mainstream tools, and can discover new drivers. [ABSTRACT FROM AUTHOR]

Identifying metabolite-disease associations is of paramount significance. With the advancement of research, computational methods have surpassed traditional experiments in efficiency. Nevertheless, current computational methods often overlook the integration of multiomics data, and the performance of the predictive models used is limited. To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately. Second, the metabolite and disease similarity networks are combined to obtain the original link features. Third, nonnegative Matrix Factorization (NMF) is applied to extract effective features from the original features, thereby reducing noise. Finally, Support Vector Machine (SVM) is utilized to predict potential associations between metabolites and diseases. Experimental results demonstrate that the SVMBN algorithm achieves an average AUC of 0.98 in 5-fold cross-validation, indicating its superiority over other methods. Furthermore, case studies prove that the SVMBN algorithm can accurately forecast the relationships between metabolites and diseases. [ABSTRACT FROM AUTHOR]

Premise Methods Results Conclusions The impact of inbreeding on biological processes is well documented in individuals with severe inbreeding depression. However, the biological processes influencing the adaptive growth of normal selfed individuals are unknown. Here, we aimed to investigate how inbreeding affects gene expression for adaptive growth of normal selfed seedlings from a self‐fertilizing parent in Chinese fir (Cunninghamia lanceolata).Using RNA‐seq data from above‐ and underground tissues of abnormal and normal selfed seedlings, we analyzed GO biological processes network. We also sequenced small RNAs in the aboveground tissues and measured the copy number variations (CNV) of the hub genes.Phenotypic fitness analysis revealed that the normal seedlings were better adapted than their abnormal counterparts. Upregulated differentially expressed genes (DEGs) were associated with development processes, and downregulated DEGs were mainly enriched in fundamental metabolism and stress response. Results of mRNA‐miRNA parallel sequencing revealed that upregulated target genes were predominantly associated with development, highlighting their crucial role in phosphorylation in signal transduction networks. We also discovered a moderate correlation (0.1328 < R2 < 0.6257) between CNV and gene expression levels for three hub genes (TMKL1, GT2, and RHY1A).We uncovered the key biological processes underpinning the growth of normal selfed seedlings and established the relationship between CNV and the expression levels of hub genes in selfed seedlings. Understanding the candidate genes involved in the growth of selfed seedlings will help us comprehend the genetic mechanisms behind inbreeding depression in the evolutionary biology of plants. [ABSTRACT FROM AUTHOR]

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico. [ABSTRACT FROM AUTHOR]

Objective: Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis to be used as biomarkers or therapeutic targets. Methods: Differentially expressed genes were explored between human gastric cancerous and noncancerous tissues, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were done. Hub genes were identified based on the protein–protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further investigation using GEPIA2 and DrugBank databases. Results: Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients. Conclusion: A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. The identified genes could be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC, but further clinical validation is required. [ABSTRACT FROM AUTHOR]

Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted. [ABSTRACT FROM AUTHOR]

Biomacromolecular networks, crucial components of biological systems, are of great importance in biological and nanomedical science, in which the interfaces between nanoparticle and macromolecular networks are intriguing and drawing great attention. Herein, this review seeks to present the state‐of‐art progress and perspective of the dynamic behaviors, confinement and entropy at the nanoparticle‐biomacromolecular network interfaces. The basic biomacromolecular networks in living systems, recent research on networks with dynamic bonds and various diffusion behaviors of nanoparticles within biomacromolecular networks are summarized. In particular, entropic effects in the confined environments of biological networks are discussed, along with how they impact the mechanical properties and dynamical behaviors. The interesting research and findings included in this review might offer the scientific community a better understanding of how this field has developed thus far, and are anticipated to trigger more systematic and fundamental research to elucidate the underlying physics and broaden the potential applications of macromolecular networks, biological and artificial. [ABSTRACT FROM AUTHOR]

Knitting interloops one-dimensional yarns into three-dimensional fabrics that exhibit behaviour beyond their constitutive materials. How extensibility and anisotropy emerge from the hierarchical organization of yarns into knitted fabrics has long been unresolved. We seek to unravel the mechanical roles of tensile mechanics, assembly and dynamics arising from the yarn level on fabric nonlinearity by developing a yarn-based dynamical model. This physically validated model captures the mechanical response of knitted fabrics, analogous to flexible metamaterials and biological fibre networks due to geometric nonlinearity within such hierarchical systems. Fabric anisotropy originates from observed yarn–yarn rearrangements during alignment dynamics and is topology-dependent. This yarn-based model also provides a design space of knitted fabrics to embed functionalities by varying geometric configuration and material property in instructed procedures compatible to machine manufacturing. Our hierarchical approach to build up a knitted fabric computationally modernizes an ancient craft and represents a first step towards mechanical programmability of knitted fabrics in wide engineering applications. [ABSTRACT FROM AUTHOR]

For Brusselator diffusion–reaction model involving complex forming reaction with the activator species, an amplitude equation has been derived in the framework of a weakly nonlinear theory. Complexing reaction with the activator species strongly influences the time-dependent amplitudes such as in Hopf-wave bifurcations, whereas time-independent amplitudes such as in Turing—bifurcations, are independent of complexing reaction with the activator species. Complexing reaction arrests the arrival of Hopf—bifurcations and the domain of excitable non-oscillations such created may be used effectively for Turing-structure generation by inducing inhomogeneous perturbations of nonzero wavenumber mode. Any major complexing interaction with the activator species in a biological oscillatory network is bound to alter the domains of Hopf/Turing bifurcations affecting the course of physiological self-organization processes. [ABSTRACT FROM AUTHOR]

Simple Summary: Boolean modeling is a graphical analytic approach used for analyzing qualitative models of biological systems, including protein–protein interaction networks. This study performed an analysis of the protein–protein interaction network related to four individuals diagnosed with Autism Spectrum Disorder (ASD) to identify the underlying etiology of the observed phenotype. The genetic mutations in each of these patients have been found to be convergent with the widely recognized Wnt and mTOR signaling pathways, which have previously been implicated in the development of ASD. The disturbance in the activity of these genetic mutations caused abnormal activation levels of critical proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD, which regulate gene expression, translation, cell adhesion, shape, and migration. The varied functions of these proteins contribute to the observed traits in these individuals, yet they reveal potential therapeutic options for them. Cellular molecules interact with one another in a structured manner, defining a regulatory network topology that describes cellular mechanisms. Genetic mutations alter these networks' pathways, generating complex disorders such as autism spectrum disorder (ASD). Boolean models have assisted in understanding biological system dynamics since Kauffman's 1969 discovery, and various analytical tools for regulatory networks have been developed. This study examined the protein–protein interaction network created in our previous publication of four ASD patients using the SPIDDOR R package, a Boolean model-based method. The aim is to examine how patients' genetic variations in INTS6L, USP9X, RSK4, FGF5, FLNA, SUMF1, and IDS affect mTOR and Wnt cell signaling convergence. The Boolean network analysis revealed abnormal activation levels of essential proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD. These proteins affect gene expression, translation, cell adhesion, shape, and migration. Patients 1 and 2 showed consistent patterns of increased β-catenin activity and decreased MTORC1, RPS6, and eIF4E activity. However, patient 2 had an independent decrease in Cadherin and SMAD activity due to the FLNA mutation. Patients 3 and 4 have an abnormal activation of the mTOR pathway, which includes the MTORC1, RPS6, and eIF4E genes. The shared mTOR pathway behavior in these patients is explained by a shared mutation in two closely related proteins (SUMF1 and IDS). Diverse activities in β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD contributed to the reported phenotype in these individuals. Furthermore, it unveiled the potential therapeutic options that could be suggested to these individuals. [ABSTRACT FROM AUTHOR]

Simple Summary: The classification of breast cancer has been a complex subject, with diverse origins of normal breast epithelial cells and evolutionary pathways contributing to its extensive heterogeneity, which further complicates the treatment process. We developed a dynamic network model to simulate the emergence of different cellular states and explore the underlying biological mechanisms. This model revealed cellular states that align with four recognized molecular subtypes of breast cancer. It also identified feedback loops that either reinforce or facilitate phenotypic transitions, and allowed us to trace these transitions through the model's topological structure. This approach provides new insights into the mechanisms driving breast cancer heterogeneity and may benefit the development of more targeted therapeutic strategies. Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular–cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets. [ABSTRACT FROM AUTHOR]

Analyzing protein–protein interaction (PPI) networks using machine learning and deep learning algorithms, alongside centrality measures, holds paramount importance in understanding complex biological systems. These advanced computational techniques enable the extraction of valuable insights from intricate network structures, shedding light on the functional relationships between proteins. By leveraging AI-driven approaches, researchers can uncover key regulatory mechanisms, identify critical nodes within the network and predict novel protein interactions with high accuracy. Ultimately, this integration of computational methodologies enhances our ability to comprehend the dynamic behavior of biological systems at a molecular level, paving the way for advancements in drug discovery, disease understanding and personalized medicine. This review paper starts by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. It primarily delves into the centrality-driven discoveries within PPI networks in biological systems and suggests using edge centrality measures and a hybrid version of node and edge centrality measures in machine learning algorithms and deep learning algorithms to predict hidden knowledge much more effectively. The review paper begins by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. A detailed view of various findings of different types of centrality measures in PPI networks is presented. [ABSTRACT FROM AUTHOR]

Integrating genome-wide association studies (GWASs) with molecular quantitative trait loci (xQTLs), including context-dependent (cd)-xQTLs, across multiple 'omics levels helps unveil the putative causal genes underlying GWAS signals, the relevant cell types, and the likely genetic regulation mechanisms of the prioritized genes. High-resolution enhancer–gene connection maps can be utilized for gene prioritization by linking the fine-mapped GWAS variants in regulatory elements to their target genes. Incorporating GWAS data with biological networks aids in identifying disease-associated genes, even those with weak GWAS signals. Integrative approaches that leverage GWAS findings, perturbation-induced transcriptomic profiles, and biological networks hold immense potential for drug repurposing. Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer–gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field. [ABSTRACT FROM AUTHOR]

Conservation is a major topic in primatology. In addition to focusing on individual species, the protection of interaction networks is key to conservation success, but difficulties in identifying these networks hinder research. We hope to inspire research in primate conservation through network analysis. We illustrate an approach for studying species networks using data for Tibetan macaques (Macaca thibetana) in three national nature reserves in Sichuan Province, China: Wolong, Tangjiahe, and Heizhugou. With data on species presence/absence from 185 cameras each continuously working for >12 months, we used the Phi coefficient rø to identify spatial association networks between species and the Lambda statistic LB to test the properties of the associations. We identified four networks across the reserves. Tibetan macaques were associated with four terrestrial/semi-arboreal bird and mammal species in Wolong and nine species in Tangjiahe, but only two species in Heizhugou. Macaques formed symmetrical associations, suggesting competition, unidirectional asymmetrical associations, suggesting commensalism, and bidirectional asymmetrical associations, suggesting mutualism. The macaque's community environment may be predation-free. Commensal and mutualistic interactions with Sichuan snub-nosed monkeys (Rhinopithecus roxellana) and three other species may enhance the ecological fitness of Tibetan macaques in Tangjiahe over that in Wolong and conservation investment for the macaque in Tangjiahe may benefit more species than that in Wolong. Asymmetrical associations may facilitate the ecological restoration of the degraded community in Heizhugou. Our approach may provide a sensitive method for ecological monitoring for conservation management, and facilitate primate community ecological research. [ABSTRACT FROM AUTHOR]

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time‐consuming and costly nature of laboratory‐based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe–disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe–disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine‐tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five‐fold cross‐validation (5‐fold‐CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5‐fold‐CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe–disease associations. [ABSTRACT FROM AUTHOR]

Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.

Abstract Objective Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis to be used as biomarkers or therapeutic targets. Methods Differentially expressed genes were explored between human gastric cancerous and noncancerous tissues, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were done. Hub genes were identified based on the protein–protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further investigation using GEPIA2 and DrugBank databases. Results Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients. Conclusion A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. The identified genes could be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC, but further clinical validation is required.

Abstract Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.

Understanding the algorithmic behaviors that are in principle realizable in a chemical system is necessary for a rigorous understanding of the design principles of biological regulatory networks. Further, advances in synthetic biology herald the time when we will be able to rationally engineer complex chemical systems and when idealized formal models will become blueprints for engineering. Coupled chemical interactions in a well-mixed solution are commonly formalized as chemical reaction networks (CRNs). However, despite the widespread use of CRNs in the natural sciences, the range of computational behaviors exhibited by CRNs is not well understood. Here, we study the following problem: What functions f : Rk → R can be computed by a CRN, in which the CRN eventually produces the correct amount of the “output” molecule, no matter the rate at which reactions proceed? This captures a previously unexplored but very natural class of computations: For example, the reaction X1 + X2 → Y can be thought to compute the function y = min(x1, x2). Such a CRN is robust in the sense that it is correct whether its evolution is governed by the standard model of mass-action kinetics, alternatives such as Hill-function or Michaelis-Menten kinetics, or other arbitrary models of chemistry that respect the (fundamentally digital) stoichiometric constraints (what are the reactants and products?). We develop a reachability relation based on a broad notion of “what could happen” if reaction rates can vary arbitrarily over time. Using reachability, we define stable computation analogously to probability 1 computation in distributed computing and connect it with a seemingly stronger notion of rate-independent computation based on convergence in the limit t → ∞ under a wide class of generalized rate laws. Besides the direct mapping of a concentration to a nonnegative analog value, we also consider the “dual-rail representation” that can represent negative values as the difference of two concentrations and allows the composition of CRN modules. We prove that a function is rate-independently computable if and only if it is piecewise linear (with rational coefficients) and continuous (dual-rail representation), or non-negative with discontinuities occurring only when some inputs switch from zero to positive (direct representation). The many contexts where continuous piecewise linear functions are powerful targets for implementation, combined with the systematic construction we develop for computing these functions, demonstrate the potential of rate-independent chemical computation. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease is a multifaceted disease that progresses through multiple phases; it involves metabolic as well as structural changes. These alterations can be measured directly or indirectly through blood, non-invasive imaging, and/or tissue analyses. While some studies have evaluated the correlations between two sets of measurements (e.g., histopathology with cross-sectional imaging or blood biomarkers), the interrelationships, if any, among histopathology, clinical blood profiles, cross-sectional imaging, and metabolomics in a pediatric cohort remain unknown. We created a multiparametric clinical MRI-histopathologic NMR network map of pediatric NAFLD through multimodal correlation networks, in order to gain insight into how these different sets of measurements are related. We found that leptin and other blood markers were correlated with many other measurements; however, upon filtering out the blood biomarkers, the network was decomposed into three independent hubs centered around histopathological features, each with associated MRI and plasma metabolites. These multi-modality maps could serve as a framework for characterizing disease status and progression and could potentially guide medical interventions.

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks. Author summary: Understanding how genes work together is crucial for unraveling the biological processes underlying complex traits. To gain insight into these genetic interactions, researchers often analyze gene co-expression networks, in which genes are linked based on the similarity of their expression patterns among different individuals. Traditionally, it has been assumed that these networks are organized into distinct assortative modules, in which genes are more connected to each other within modules than between them. However, by using a novel non-parametric clustering approach called the Stochastic Block Model, we show that fruit fly transcriptomes contain not only assortative, modular gene clusters, but also functionally relevant non-modular clusters that would be overlooked by standard methods. This suggests that transcriptional networks may be more complex and diverse than previously thought. Our findings highlight the importance of using unbiased clustering techniques to fully capture the various architectures of gene co-expression networks and their potential biological significance. [ABSTRACT FROM AUTHOR]