Systems biology of ferroptosis: A modeling approach.
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ABSTRACT:
SUBMITTER: Konstorum A
PROVIDER: S-EPMC7254156 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Autism is the fastest growing developmental disorder in the world today. The prevalence of autism in the US has risen from 1 in 2500 in 1970 to 1 in 88 children today. People with autism present with repetitive movements and with social and communication impairments. These impairments can range from mild to profound. The estimated total lifetime societal cost of caring for one individual with autism is $3.2 million US dollars. With the rapid growth in this disorder and the great expense of caring for those with autism, it is imperative for both individuals and society that techniques be developed to model and understand autism. There is increasing evidence that those individuals diagnosed with autism present with highly diverse set of abnormalities affecting multiple systems of the body. To this date, little to no work has been done using a whole body systems biology approach to model the characteristics of this disorder. Identification and modelling of these systems might lead to new and improved treatment protocols, better diagnosis and treatment of the affected systems, which might lead to improved quality of life by themselves, and, in addition, might also help the core symptoms of autism due to the potential interconnections between the brain and nervous system with all these other systems being modeled. This paper first reviews research which shows that autism impacts many systems in the body, including the metabolic, mitochondrial, immunological, gastrointestinal and the neurological. These systems interact in complex and highly interdependent ways. Many of these disturbances have effects in most of the systems of the body. In particular, clinical evidence exists for increased oxidative stress, inflammation, and immune and mitochondrial dysfunction which can affect almost every cell in the body. Three promising research areas are discussed, hierarchical, subgroup analysis and modeling over time. This paper reviews some of the systems disturbed in autism and suggests several systems biology research areas. Autism poses a rich test bed for systems biology modeling techniques.
| S-EPMC3507704 | biostudies-literature
Project description:BackgroundA microorganism is a complex biological system able to preserve its functional features against external perturbations and the ability of the living systems to oppose to these external perturbations is defined "robustness". The antibiotic resistance, developed by different bacteria strains, is a clear example of robustness and of ability of the bacterial system to acquire a particular functional behaviour in response to environmental changes. In this work we have modeled the whole mechanism essential to the methicillin-resistance through a systems biology approach. The methicillin is a beta-lactamic antibiotic that act by inhibiting the penicillin-binding proteins (PBPs). These PBPs are involved in the synthesis of peptidoglycans, essential mesh-like polymers that surround cellular enzymes and are crucial for the bacterium survival.MethodologyThe network of genes, mRNA, proteins and metabolites was created using CellDesigner program and the data of molecular interactions are stored in Systems Biology Markup Language (SBML). To simulate the dynamic behaviour of this biochemical network, the kinetic equations were associated with each reaction.ConclusionsOur model simulates the mechanism of the inactivation of the PBP by methicillin, as well as the expression of PBP2a isoform, the regulation of the SCCmec elements (SCC: staphylococcal cassette chromosome) and the synthesis of peptidoglycan by PBP2a. The obtained results by our integrated approach show that the model describes correctly the whole phenomenon of the methicillin resistance and is able to respond to the external perturbations in the same way of the real cell. Therefore, this model can be useful to develop new therapeutic approaches for the methicillin control and to understand the general mechanism regarding the cellular resistance to some antibiotics.
| S-EPMC2707609 | biostudies-literature
Project description:BackgroundDrug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction.ResultsWe searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test.ConclusionsWhen using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.
| S-EPMC7805064 | biostudies-literature
Project description:The standard drug development model uses reductionist approaches to discover small molecules targeting one pathway. Although systems biology analyzes multiple pathways, the approach is often used to develop a small molecule interacting at only one pathway in the system. Similar to that in physics where a departure from the old reductionist "Copenhagen View" of quantum physics to a new and predictive systems based, collective model has emerged yielding new breakthroughs such as the LASER, a new model is emerging in biology where systems biology is used to develop a new technology acting at multiple pathways called "systems therapeutics."
| S-EPMC4027758 | biostudies-other
Project description:SummaryThe modeling tool PROMOT facilitates the efficient and comprehensible setup and editing of modular models coupled with customizable visual representations. Since its last major publication in 2003, PROMOT has gained new functionality in particular support of logical models, efficient editing, visual exploration, model validation and support for SBML.AvailabilityPROMOT is an open source project and freely available at http://www.mpi-magdeburg.mpg.de/projects/promot/.
| S-EPMC2647835 | biostudies-literature
Project description:Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.
| S-EPMC3824814 | biostudies-other
Project description:The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.
| S-EPMC8593007 | biostudies-literature
Project description:BackgroundGastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.MethodsGene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes.ResultsEleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages.ConclusionsThese results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.
| S-EPMC10364406 | biostudies-literature
Project description:Background Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents. The survival rate of patients with metastatic disease remains very dismal. Nevertheless, metastasis is a complex process and a single-level analysis is not likely to identify its key biological determinants. In this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mRNA and protein expression data in two distinct human metastatic OS models. Results mRNA expression microarray and N-linked glycoproteomic analyses were performed on two commonly used isogenic pairs of human metastatic OS cell lines, namely HOS/143B and SaOS-2/LM7. Pathway analysis of the differentially regulated genes and glycoproteins separately revealed pathways associated to metastasis including cell cycle regulation, immune response, and epithelial-to-mesenchymal-transition. However, no common significant pathway was found at both genomic and proteomic levels between the two metastatic models, suggesting a very different biological nature of the cell lines. To address this issue, we used a topological significance analysis based on a “shortest path” algorithm to identify topological nodes, which uncovered additional biological information with respect to the genomic and glycoproteomic profiles but remained hidden from the direct analyses. Pathway analysis of the significant topological nodes revealed a striking concordance between the models and identified significant common pathways, including “Cytoskeleton remodeling/TGF/WNT”, “Cytoskeleton remodeling/Cytoskeleton remodeling”, and “Cell adhesion/Chemokines and adhesion”. Of these, the “Cytoskeleton remodeling/TGF/WNT” was the top ranked common pathway from the topological analysis of the genomic and proteomic profiles in the two metastatic models. The up-regulation of proteins in the “Cytoskeleton remodeling/TGF/WNT” pathway in the SaOS-2/LM7 and HOS/143B models was further validated using an orthogonal Reverse Phase Protein Array platform. Conclusions In this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in OS. The use of the topological analysis revealed hidden biological pathways that are known to play critical roles in metastasis. Wnt signaling has been previously implicated in OS and other tumors, and inhibitors of Wnt signaling pathways are available for clinical testing. Further characterization of this common pathway and other topological pathways identified from this study may lead to a novel therapeutic strategy for the treatment of metastatic OS.
2012-04-25 | GSE37552 | GEO
Project description:Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways. In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.
| S-EPMC5857180 | biostudies-literature