Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.
Myocardial injury (MI) is a common occurrence in clinical practice caused by various factors such as ischemia, hypoxia, infection, metabolic abnormalities, and inflammation. Such damages are characterized by a reduction in myocardial function and cardiomyocyte death that can result in dangerous outcomes such as cardiac failure and arrhythmias. An endoplasmic reticulum stress (ERS)-induced unfolded protein response (UPR) is triggered by several stressors, and its intricate signaling networks are instrumental in both cell survival and death.
Gastric cancer remains a significant health burden globally, especially prevalent in Asian and European regions. Despite a notable decline in incidence in the United States and Western Europe over recent decades, the disease's persistence underscores the urgency for advanced research in its pathogenesis and treatment strategies. Central to this pursuit is the exploration of the mitogen-activated protein kinase (MAPK) pathway, a pivotal cellular mechanism implicated in the complex processes of gastric cancer development, including cellular proliferation, invasion, migration, and metastasis.
Background: Periodontitis is among the most prevalent inflammatory conditions and greatly impacts oral health. This study aimed to elucidate the role of basement membrane-related genes in the pathogenesis and diagnosis of periodontitis.
Methods: GSE10334 was used for identification of hub genes via the differential analysis, protein-protein interaction network, MCC and DMNC algorithms, and evaluation via LASSO regression and support vector machine analysis to identify basement membrane-related markers in patients with periodontitis.
Doublecortin (DCX) is a microtubule-associated protein known to be a key regulator of neuronal migration and differentiation during brain development. However, the role of DCX, particularly in regulating the survival and growth of glioma cells, remains unclear. In this study, we utilized CRISPR/Cas9 technology to knock down DCX in the human glioma cell line (U251).
National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Mental health disorders emerge from complex interactions among neurobiological processes across multiple scales, which poses challenges in uncovering pathological pathways from molecular dysfunction to neuroimaging changes. Here, we proposed a multiscale fusion (mFusion) method to evaluate the relevance of each gene to the neuroimaging traits of mental health disorders. We combined gene-neuroimaging associations with gene-positron emission tomography (PET) and PET-neuroimaging associations using protein-protein interaction networks, where various genes traced by PET maps are involved in neurotransmission.
