A preference for link operator functions can drive Boolean biological networks towards critical dynamics.

Boolean modelling is a powerful framework to understand the operating principles of biological networks. The regulatory logic between biological entities in these networks is expressed as Boolean functions (BFs). There exist various types of BFs (and thus regulatory logic rules) which are meaningful in the biological context.

Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations.

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem.

An atlas of fragrance chemicals in children's products.

Exposure to environmental chemicals during early childhood is a potential health concern. At a tender age, children are exposed to fragrance chemicals used in toys and child care products. Although there are few initiatives in Europe and United States towards monitoring and regulation of fragrance chemicals in children's products, such efforts are still lacking elsewhere.

Network biology approach to human tissue-specific chemical exposome.

Human exposure to environmental chemicals is a major contributor to the global disease burden. To characterize the external exposome it is important to assess its chemical components and to study their impact on human health. Biomonitoring studies measure the body burden of environmental chemicals detected in biospecimens from a wide range of the population.

Potential phytochemical inhibitors of SARS-CoV-2 helicase Nsp13: a molecular docking and dynamic simulation study.

The SARS-CoV-2 helicase Nsp13 is a promising target for developing anti-COVID drugs. In the present study, we have identified potential natural product inhibitors of SARS-CoV-2 Nsp13 targeting the ATP-binding site using molecular docking and molecular dynamics (MD) simulations. MD simulation of the prepared crystal structure of SARS-CoV-2 Nsp13 was performed to generate an ensemble of structures of helicase Nsp13 capturing the conformational diversity of the ATP-binding site.

Reprogramming of microRNA expression via E2F1 downregulation promotes Salmonella infection both in infected and bystander cells.

Cells infected with pathogens can contribute to clearing infections by releasing signals that instruct neighbouring cells to mount a pro-inflammatory cytokine response, or by other mechanisms that reduce bystander cells' susceptibility to infection. Here, we show the opposite effect: epithelial cells infected with Salmonella Typhimurium secrete host factors that facilitate the infection of bystander cells. We find that the endoplasmic reticulum stress response is activated in both infected and bystander cells, and this leads to activation of JNK pathway, downregulation of transcription factor E2F1, and consequent reprogramming of microRNA expression in a time-dependent manner.

Network geometry and market instability.

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account.

NeurotoxKb 1.0: Compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals.

Exposure to environmental neurotoxicants is a significant concern due to their potential to cause permanent or irreversible damage to the human nervous system. Here, we present the first dedicated knowledgebase, NeurotoxKb 1.0, on environmental neurotoxicants specific to mammals.

ExHuMId: A curated resource and analysis of Exposome of Human Milk across India.

Human milk is a vital source of nourishment for infants. However, numerous environmental contaminants also find their way into human milk, making up the major part of a newborn's external exposome. While there are chemical regulations in India and scientific literature on environmental contaminants is available, the systematic compilation, monitoring, and risk management of human milk contaminants are inadequate.

MeFSAT: a curated natural product database specific to secondary metabolites of medicinal fungi.

Fungi are a rich source of secondary metabolites which constitutes a valuable and diverse chemical space of natural products. Medicinal fungi have been used in traditional medicine to treat human ailments for centuries. To date, there is no devoted resource on secondary metabolites and therapeutic uses of medicinal fungi.

Degree difference: a simple measure to characterize structural heterogeneity in complex networks.

Despite the growing interest in characterizing the local geometry leading to the global topology of networks, our understanding of the local structure of complex networks, especially real-world networks, is still incomplete. Here, we analyze a simple, elegant yet underexplored measure, 'degree difference' (DD) between vertices of an edge, to understand the local network geometry. We describe the connection between DD and global assortativity of the network from both formal and conceptual perspective, and show that DD can reveal structural properties that are not obtained from other such measures in network science.

Edge-based analysis of networks: curvatures of graphs and hypergraphs.

The relations, rather than the elements, constitute the structure of networks. We therefore develop a systematic approach to the analysis of networks, modelled as graphs or hypergraphs, that is based on structural properties of (hyper)edges, instead of vertices. For that purpose, we utilize so-called network curvatures.

DEDuCT 2.0: An updated knowledgebase and an exploration of the current regulations and guidelines from the perspective of endocrine disrupting chemicals.

The regulatory assessment of endocrine disrupting chemicals (EDCs) is complex due to the lack of a standardized definition of EDCs and validated testing criteria. In spite of these challenges, there is growing scientific interest in EDCs which has resulted in the rapid expansion of published literature on endocrine disruption upon chemical exposure. Here, we explore how academic research leading to curated knowledgebases can inform current chemical regulations on EDCs.

In Silico Identification of Potential Natural Product Inhibitors of Human Proteases Key to SARS-CoV-2 Infection.

Presently, there are no approved drugs or vaccines to treat COVID-19, which has spread to over 200 countries and at the time of writing was responsible for over 650,000 deaths worldwide. Recent studies have shown that two human proteases, TMPRSS2 and cathepsin L, play a key role in host cell entry of SARS-CoV-2. Importantly, inhibitors of these proteases were shown to block SARS-CoV-2 infection.

Broad Substrate-Specific Phosphorylation Events Are Associated With the Initial Stage of Plant Cell Wall Recognition in .

Fungal plant cell wall degradation processes are governed by complex regulatory mechanisms, allowing the organisms to adapt their metabolic program with high specificity to the available substrates. While the uptake of representative plant cell wall mono- and disaccharides is known to induce specific transcriptional and translational responses, the processes related to early signal reception and transduction remain largely unknown. A fast and reversible way of signal transmission are post-translational protein modifications, such as phosphorylations, which could initiate rapid adaptations of the fungal metabolism to a new condition.

Persistent homology of unweighted complex networks via discrete Morse theory.

Topological data analysis can reveal higher-order structure beyond pairwise connections between vertices in complex networks. We present a new method based on discrete Morse theory to study topological properties of unweighted and undirected networks using persistent homology. Leveraging on the features of discrete Morse theory, our method not only captures the topology of the clique complex of such graphs via the concept of critical simplices, but also achieves close to the theoretical minimum number of critical simplices in several analyzed model and real networks.

A curated knowledgebase on endocrine disrupting chemicals and their biological systems-level perturbations.

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents.

Comparative analysis of two discretizations of Ricci curvature for complex networks.

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks.

Comparative systems analysis of the secretome of the opportunistic pathogen Aspergillus fumigatus and other Aspergillus species.

Aspergillus fumigatus and multiple other Aspergillus species cause a wide range of lung infections, collectively termed aspergillosis. Aspergilli are ubiquitous in environment with healthy immune systems routinely eliminating inhaled conidia, however, Aspergilli can become an opportunistic pathogen in immune-compromised patients. The aspergillosis mortality rate and emergence of drug-resistance reveals an urgent need to identify novel targets.

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics.

Phytochemicals of medicinal plants encompass a diverse chemical space for drug discovery. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable computational approaches towards natural product based drug discovery.

Isosteric substitution in cationic-amphiphilic polymers reveals an important role for hydrogen bonding in bacterial membrane interactions.

Biomimetic antibacterial polymers, the functional mimics of antimicrobial peptides (AMPs), targeting the bacterial cell membrane have been developed to combat the problem of antibiotic resistance. Amphiphilicity, a balance of cationic charge and hydrophobicity, in these polymers has been shown to be pivotal for their selective interactions with anionic lipid membranes of bacteria instead of zwitterionic mammalian (human erythrocyte) membranes. However, it is unclear if and to what extent hydrogen bonding in amphiphilic antibacterial polymers contributes to this membrane binding specificity.

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

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks.