Distinct parallel activation and interaction between dorsal and ventral pathways during phonological and semantic processing: A cTBS-fMRI study.

Successful visual word recognition requires the integration of phonological and semantic information, which is supported by the dorsal and ventral pathways in the brain. However, the functional specialization or interaction of these pathways during phonological and semantic processing remains unclear. Previous research has been limited by its dependence on correlational functional magnetic resonance imaging (fMRI) results or causal validation using patient populations, which are susceptible to confounds such as plasticity and lesion characteristics. To address this, the present study employed continuous theta-burst stimulation combined with fMRI in a within-subject design to assess rapid adaptation in regional activity and functional connectivity of the dorsal and ventral pathways during phonological and semantic tasks. This assessment followed the precise inhibition of the left inferior parietal lobule and anterior temporal lobe in the dorsal and ventral pathways, respectively. Our results reveal that both the dorsal and ventral pathways were activated during phonological and semantic processing, while the adaptation activation and interactive network were modulated by the task type and inhibited region. The two pathways exhibited interconnectivity in phonological processing, and disruption of either pathway led to rapid adaptation across both pathways. In contrast, only the ventral pathway exhibited connectivity in semantic processing, and disruption of this pathway alone resulted in adaptive effects primarily in the ventral pathway. These findings provide essential evidence supporting the interactive theory, phonological information processing in particular, potentially providing meaningful implications for clinical populations.

ProNetView-ccRCC: A Web-Based Portal to Interactively Explore Clear Cell Renal Cell Carcinoma Proteogenomics Networks.

To better understand the molecular basis of cancer, the NCI's Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene and protein regulatory networks are subsequently being derived based on these proteogenomic profiles, which serve as tools to gain systems-level understanding of the molecular regulatory factories underlying these diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation, and communication of these gene/protein regulatory networks by the cancer research community. In this work, ProNetView-ccRCC (http://ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data is introduced. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade.

Phase-field modeling of constrained interactive fungal networks.

Fungi develop structures that interact with their surroundings and evolve adaptively in the presence of geometrical constraints, finding optimal solutions for complex combinatorial problems. The pathogenic fungus Ophiocordyceps constitutes a perfect model for the study of constrained interactive networks. Modeling these networks is challenging due to the highly coupled physics involved and their interaction with moving boundaries. In this work, we develop a computational phase-field model to elucidate the mechanics of the emerging properties observed in fungal networks. We use a variational approach to derive the equations governing the evolution in time of the mycelium biomass and the nutrients in the medium. We present an extensive testing of our model, reproduce growing and decaying phenomena, and capture spatial and temporal scales. We explore the variables interplay mechanism that leads to different colony morphologies, and explain abrupt changes of patterns observed in the laboratory. We apply our model to simulate analogous processes to the evolution of Ophiocordyceps as it grows through confined geometry and depletes available resources, demonstrating the suitability of the formulation to study this class of biological networks.

Noncanonical Gß Gib2 is a scaffolding protein promoting cAMP signaling through functions of Ras1 and Cac1 proteins in Cryptococcus neoformans.

Gß-like/RACK1 functions as a key mediator of various pathways and contributes to numerous cellular functions in eukaryotic organisms. In the pathogenic fungus Cryptococcus neoformans, noncanonical Gß Gib2 promotes cAMP signaling in cells lacking normal Gpa1 function while displaying versatility in interactions with Gα Gpa1, protein kinase Pkc1, and endocytic intersectin Cin1. To elucidate the Gib2 functional mechanism(s), we demonstrate that Gib2 is required for normal growth and virulence. We show that Gib2 directly binds to Gpa1 and Gγ Gpg1/Gpg2 and that it interacts with phosphodiesterase Pde2 and monomeric GTPase Ras1. Pde2 remains functionally dispensable, but Ras1 is found to associate with adenylyl cyclase Cac1 through the conserved Ras association domain. In addition, the ras1 mutant exhibits normal capsule formation, whereas the ras1 gpa1 mutant displays enhanced capsule formation, and the ras1 gpa1 cac1 mutant is acapsular. Collectively, these findings suggest that Gib2 promotes cAMP levels by relieving an inhibitory function of Ras1 on Cac1 in the absence of Gpa1. In addition, using GST affinity purification combined with mass spectrometry, we identified 47 additional proteins that interact with Gib2. These proteins have putative functions ranging from signal transduction, energy generation, metabolism, and stress response to ribosomal function. After establishing and validating a protein-protein interactive network, we believe Gib2 to be a key adaptor/scaffolding protein that drives the formation of various protein complexes required for growth and virulence. Our study reveals Gib2 as an essential component in deciphering the complexity of regulatory networks that control growth and virulence in C. neoformans.

[Comparative analysis of the role of CD4(+) and CD8(+) T cells in severe asthma development].

The role of CD8^(+) T cells in asthma has not been fully discussed. The mechanisms of CD4^(+) and CD8^(+) cells in severe asthma (SA) development were compared. The microarray data (GSE31773) was downloaded from the Gene Expression Omnibus (GEO) database, including 20 samples of CD4^(+) and CD8^(+) T cells, which were collected from 8 health controls (HC), 4 non-severe asthma (NSA) and 8 SA patients. DEGs of CD4^(+) and CD8^(+) T cells in the HC vs. NSA and HC vs. SA groups were identified using the limma package in R. GO and pathway enrichment analysis of the common DEGs between the two groups were analyzed using DAVID. The interactive network of DEGs and significant modules were further explored. In CD4^(+) cells, there were 168 DEGs in HC vs. NSA group and 685 DEGs in HC vs. SA group, while for CD8^(+) T cells there were 719 DEGs in the HC vs. NSA groups and 1255 DEGs in the HC vs. SA groups. Besides, 80 common DEGs from CD4^(+) samples were enriched in the MAPKKK cascade and molecular metabolism, and 385 common DEGs of CD8^(+) T cells were significantly related with cell apoptosis and transformation. Moreover, two significant modules of DEGs in CD4^(+) were found to be involved with MPO and BPI. One module of CD8^(+) T cells containing PDHA1 and MRPL42 was identified to be related with glycolysis. In conclusion, MPO and BPI in CD4^(+), and PDHA1 and MRPL42 in CD8^(+) T cells might be used as specific biomarkers of SA progression. Therapy targeting the functions of CD4^(+) and CD8^(+) T cells may provide a novel perspective for SA treatment.

PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.

OBJECTIVES: To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies. MATERIALS AND METHODS: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies. RESULTS: The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME. DISCUSSION: PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records. CONCLUSION: PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.

Long-term temporal variation in the organization of an ant-plant network.

BACKGROUND AND AIMS: Functional groups of species interact and coevolve in space and time, forming complex networks of interacting species. A long-term study of temporal variation of an ant-plant network is presented with the aims of: (1) depicting its structural changes over a 20-year period; (2) detailing temporal variation in network topology, as revealed by nestedness and modularity analysis and other parameters (i.e. connectance, niche overlap); and (3) identifying long-term turnover in taxonomic structure (i.e. switches in ant resource use or plant visitor assemblages according to taxa). METHODS: Fieldwork was carried out at La Mancha, Mexico, and ant-plant interactions were observed between 1989 and 1991, between 1998 and 2000, and between May 2010 and 2011. Occurrences of ants on extrafloral nectaries (EFNs) were recorded. The resulting ant-plant networks were constructed from qualitative presence-absence data determined by a species-species matrix defined by the frequency of occurrence of each pairwise ant-plant interaction. KEY RESULTS: Network variation across time was stable and a persistent nested structure may have contributed to the maintenance of resilient and species-rich communities. Modularity was lower than expected, especially in the most recent networks, indicating that the community exhibited high overlap among interacting species (e.g. few species were hubs in the more recent network, being partly responsible for the nested pattern). Structurally, the connections created among modules by super-generalists gave cohesion to subsets of species that otherwise would remain unconnected. This may have allowed an increasing cascade-effect of evolutionary events among modules. Mutualistic ant-plant interactions were structured 20 years ago mainly by the subdominant nectarivorous ant species Camponotus planatus and Crematogaster brevispinosa, which monopolized the best extrafloral nectar resources and out-competed other species with broader feeding habits. Through time, these ants, which are still present, lost their position as network hubs and diminished in their importance in structuring the network; simultaneously, plants gained in importance. CONCLUSIONS: The long-term network analysis reveals a decrease in attended plant species richness, a notable increase in plant species participation from 1990 to 2010 (sustained by less plant taxonomic similarity in the older 1990 network), an increase in the number of ant species and a diminishing dominance of super-generalist ants. The structure of the community has remained highly nested and connected with low modularity, suggesting overall a more participative, homogeneous, cohesive interaction network. Although previous studies have suggested that interactions between ants and EFN-bearing plants are susceptible to seasonality, abiotic factors and perturbation, this cohesive structure appears to be the key for biodiversity and community maintenance.

Interacting Networks of the Hypothalamic-Pituitary-Ovarian Axis Regulate Layer Hens Performance.

Egg production is a vital biological and economic trait for poultry breeding. The 'hypothalamic-pituitary-ovarian (HPO) axis' determines the egg production, which affects the layer hens industry income. At the organism level, the HPO axis is influenced by the factors related to metabolic and nutritional status, environment, and genetics, whereas at the cellular and molecular levels, the HPO axis is influenced by the factors related to endocrine and metabolic regulation, cytokines, key genes, signaling pathways, post-transcriptional processing, and epigenetic modifications. MiRNAs and lncRNAs play a critical role in follicle selection and development, atresia, and ovulation in layer hens; in particular, miRNA is known to affect the development and atresia of follicles by regulating apoptosis and autophagy of granulosa cells. The current review elaborates on the regulation of the HPO axis and its role in the laying performance of hens at the organism, cellular, and molecular levels. In addition, this review provides an overview of the interactive network regulation mechanism of the HPO axis in layer hens, as well as comprehensive knowledge for successfully utilizing their genetic resources.

PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis.

Motivation: Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations. Results: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia. Availability and implementation: The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME. Data availability statement: The data underlying this article are available in the article and in its online web application or supplementary material.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Exploring the User Interaction Network in an Anxiety Disorder Online Community: An Exponential Random Graph Model with Topical and Emotional Effects.

The increasing number of people with anxiety disorders presents challenges when gathering health information. Users in anxiety disorder online communities (ADOCs) share and obtain a variety of health information, such as treatment experience, drug efficacy, and emotional support. This interaction alleviates the difficulties involved in obtaining health information. Users engage in community interaction via posts, comments, and replies, which promotes the development of an online community as well as the wellbeing of community users, and research concerning the formation mechanism of the user interaction network in ADOCs could be beneficial to users. Taking the Anxiety Disorder Post Bar as the research object, this study constructed an ADOC user interaction network based on users' posts, comments, and personal information data. With the help of exponential random graph models (ERGMs), we studied the effects of the network structure, user attributes, topics, and emotional intensity on user interaction networks. We found that there was significant reciprocity in the user interaction network in ADOCs. In terms of user attributes, gender homogeneity had no impact on the formation of the user interaction network. Experienced users in the community had obvious advantages, and experienced users could obtain replies more easily from other members. In terms of topics, pathology popularization showed obvious homogeneity, and symptoms of generalized anxiety disorder showed obvious heterogeneity. In terms of emotional intensity, users with polarized emotions were more likely to receive replies from users with positive emotions. The probability of interaction between two users with negative emotions was small, and users with opposite emotional polarity tended to interact, especially when the interaction was initiated by users with positive emotions.

Gene Biomarkers Derived from Clinical Data of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common cancer of high mortality, mainly due to the difficulty in diagnosis during its clinical stage. Here we aim to find the gene biomarkers, which are of important significance for diagnosis and treatment. In this work, 3682 differentially expressed genes on HCC were firstly differentiated based on the Cancer Genome Atlas database (TCGA). Co-expression modules of these differentially expressed genes were then constructed based on the weighted correlation network algorithm. The correlation coefficient between the co-expression module and clinical data from the Broad GDAC Firehose was thereafter derived. Finally, the interactive network of genes was then constructed. Then, the hub genes were used to implement enrichment analysis and pathway analysis in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Results revealed that the abnormally expressed genes in the module played an important role in the biological process including cell division, sister chromatid cohesion, DNA repair, and G1/S transition of mitotic cell cycle. Meanwhile, these genes also enriched in a few crucial pathways related to Cell cycle, Oocyte meiosis, and p53 signaling. Via investigating the closeness centrality of the interactive network, eight gene biomarkers including the CKAP2, TPX2, CDCA8, KIFC1, MELK, SGO1, RACGAP1, and KIAA1524 gene were discovered, whose functions had been indeed revealed to be correlated with HCC. This study, therefore, suggests that the abnormal expression of those eight genes may be taken as gene biomarkers of HCC.

A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein⁻Protein Interactive Networks.

Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein⁻protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks' similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.

Knockdown of ribosomal protein S15A inhibits human kidney cancer cell growth in vitro and in vivo.

Ribosomal protein S15A (RPS15A), a member of the ribosomal protein gene family, was demonstrated to be closely associated with tumorigenesis in multiple human malignancies. Nevertheless, the role of RPS15A in the progression of renal cell carcinoma (RCC) remains unknown. In the present study, by comparing the publicly available data from RCC tissues and reverse transcription­quantitative polymerase chain reaction results, it was identified that RPS15A was upregulated in RCC tissues and cell lines (P<0.001). Notably, knockdown of RPS15A suppressed 786­O cell proliferation (P<0.001) and promoted its apoptosis/necrotic (P=0.0001) in vitro. Additionally, tumour formation and growth of transfected 786­O cells were observed to be restrained in a mouse model (P<0.05). Subsequent to analysing the microarray data, 747 genes were differentially expressed in the RPS15A­knockdown 786­O cells. The enriched canonical pathways, diseases and functions of differentially expressed genes, and the interactive network of RPS15A in RCC were successfully constructed by ingenuity pathway analysis. Overall, the present results provided a preliminary experimental basis for RPS15A as a novel oncogene and potential therapeutic target in RCC.

Low BIK outside-inside-out interactive inflammation immune-induced transcription-dependent apoptosis through FUT3-PMM2-SQSTM1-SFN-ZNF384.

Eighteen different Pearson mutual-positive-correlation BIK-activatory molecular feedback upstream and downstream networks were constructed from 79 overlapping of 376 GRNInfer and 98 Pearson under BIK CC ≥ 0.25 in low normal adjacent tissues of Taiwan compared with high lung adenocarcinoma. Our identified BIK interactive total feedback molecular network showed FUT3 [fucosyltransferase 3 (galactoside 3(4)-L-fucosyltransferase Lewis blood group)], PMM2 (phosphomannomutase 2), SQSTM1 (sequestosome 1), SFN_2 [REX2 RNA exonuclease 2 homolog (S. cerevisiae)] and ZNF384 (zinc finger protein 384) in low normal adjacent tissues of lung adenocarcinoma. BIK interactive total feedback terms included mitochondrial envelope, endomembrane system, integral to membrane, Golgi apparatus, cytoplasm, nucleus, cytosol, intracellular signaling cascade, mitochondrion, extracellular space, inflammation, immune response, apoptosis, cell differentiation, cell cycle, regulation of cell cycle, cell proliferation, estrogen-responsive protein Efp controls cell cycle and breast tumors growth, induction or regulation of apoptosis based on integrative GO, KEGG, GenMAPP, BioCarta and disease databases in low normal adjacent tissues of lung adenocarcinoma. Therefore, we propose low BIK outside-inside-out interactive inflammation immune-induced transcription-dependent apoptosis through FUT3-PMM2-SQSTM1-SFN-ZNF384 in normal adjacent tissues of lung adenocarcinoma.

Biological network exploration with Cytoscape 3.

Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways. Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface, and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data (attributes), and identifying clusters. It also highlights new features that benefit experienced users.

In silico analysis of TTR gene (coding and non-coding regions, and interactive network) and its implications in transthyretin-related amyloidosis.

INTRODUCTION: Transthyretin (TTR)-related amyloidosis is a life-threatening disease. Currently, several questions about the pathogenic mechanisms of TTR-related amyloidosis remain unanswered. METHODS: We have investigated various TTR-related issues using different in silico approaches. RESULTS: Using an amino acid similarity-based analysis, we have indicated the most relevant TTR secondary structures in determining mutation impact. Our amyloidogenic propensity analysis of TTR missense substitutions has highlighted a similar pattern for wild-type and mutated TTR amino ß acid sequences. However, some mutations present differences with respect to the general distribution. We have identified non-coding variants in cis-regulatory elements of the TTR gene, and our analysis on V122I-related haplotypes has indicated differences in non-coding regulatory variants, suggesting differences among V122I carriers. The analysis of methylation status indicated CpG sites that may affect TTR expression. Finally, our interactive network analysis revealed functional partners of TTR that may play a modifier role in the pathogenesis of TTR-related amyloidosis. DISCUSSION AND CONCLUSION: Our data provided new insights into the pathogenesis of TTR-related amyloidosis that, if they were to be confirmed through experimental investigations, could significantly improve our understanding of the disease.

Interactive network analysis of the plasma amino acids profile in a mouse model of hyperglycemia.

Amino acids are a group of metabolites that are important substrates for protein synthesis, are important as signaling molecules and play central roles as highly connected metabolic hubs, and therefore, there are many reports that describe disease-specific abnormalities in plasma amino acids profile. However, the causes of progression from a healthy control to a manifestation of the plasma amino acid changes remain obscure. Here, we extended the plasma amino acids profile to relationships that have interactive properties, and found remarkable differences in the longitudinal transition of hyperglycemia as a diabetes emergency. What is especially important is to understand pathogenesis for better treatment and early diagnosis of diabetes. In this study, we performed interactive analysis using time course data of the plasma samples of AKITA mice, which develop hyperglycemia. Primarily, we decided to analyze the interactive property of amino acids which had highly significant association with hyperglycemia, namely alanine, glycine, leucine, isoleucine and valine. Next, we inferred the interactive network structure, which reproduces the actual time course within an error allowance of 10% using an S-system model (a conceptual mathematical model for analyzing and simulating networks). The emphasis of this study was altered interactions of plasma amino acids that show stabilizing and destabilizing features in a variety of clinical settings. By performing sensitivity analysis, the most dominant relations in this network were selected; the control paths from glycine to isoleucine in healthy control and from alanine to glycine in hyperglycemia. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from alanine to glycine.