Frontiers in systems biology最新文献

{"title":"Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling","authors":"Evelina Folkesson, B. C. Sakshaug, Andrea D. Hoel, G. Klinkenberg, Å. Flobak","doi":"10.3389/fsysb.2023.1112831","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1112831","url":null,"abstract":"Drug combinations have been proposed to combat drug resistance in cancer, but due to the large number of possible drug targets, in vitro testing of all possible combinations of drugs is challenging. Computational models of a disease hold great promise as tools for prediction of response to treatment, and here we constructed a logical model integrating signaling pathways frequently dysregulated in cancer, as well as pathways activated upon DNA damage, to study the effect of clinically relevant drug combinations. By fitting the model to a dataset of pairwise combinations of drugs targeting MEK, PI3K, and TAK1, as well as several clinically approved agents (palbociclib, olaparib, oxaliplatin, and 5FU), we were able to perform model simulations that allowed us to predict more complex drug combinations, encompassing sets of three and four drugs, with potentially stronger effects compared to pairwise drug combinations. All predicted third-order synergies, as well as a subset of non-synergies, were successfully confirmed by in vitro experiments in the colorectal cancer cell line HCT-116, highlighting the strength of using computational strategies to rationalize drug testing.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43429331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the comorbidity of COPD and tuberculosis, a computational study","authors":"Cheryl L. Sershen, Taha Salim, E. May","doi":"10.3389/fsysb.2023.940097","DOIUrl":"https://doi.org/10.3389/fsysb.2023.940097","url":null,"abstract":"Recent research has shown that people who suffer from chronic obstructive pulmonary disease (COPD) have a greater propensity to contract and develop tuberculosis (TB) than the general population. Not only is the hazard ratio for contracting active tuberculosis triple that of the general population for those with COPD, but that the probability of death from any cause during the first year was double that of the tuberculosis population as a whole. This observation suggests that patients with COPD are less likely to progress to latent tuberculosis infection (LTBI) and are more likely to develop active tuberculosis than the general population. While similar susceptibility rates to TB are known to occur in populations with other ailments of the lung, particularly HIV, emphysema or asthma, patients with COPD (both emphysema and chronic bronchitis) are statistically more at risk for the disease. To examine the comorbidity effects of COPD on tuberculosis disease and granuloma formation, the process by which Mycobacterium tuberculosis (Mtb) is either contained or disseminates, we used a multi-scale model that integrates pathophysiological and immunopathological aspects of COPD and TB. Depicting chronic obstructive pulmonary disease smoker and non-smoker populations, we integrate agent-based models (ABM) of cellular immune response, physiological models of pulmonary capacity for COPD smoker/non-smoker, systems biology models of macrophage immune response to Mtb, and metabolic models to capture intracellular and extracellular Mtb metabolism and proliferation. We use our model to investigate key drivers of disease outcomes of clearance, granuloma-based containment, and disseminated disease in individuals with COPD and TB for smoking and non-smoking populations.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45222588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Illuminating HBV with multi-scale modeling","authors":"S. Means, M. A. Ali, H. Ho","doi":"10.3389/fsysb.2023.1045754","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1045754","url":null,"abstract":"Unfortunately for the estimated 250 million sufferers of chronic hepatitis-B viral (HBV) infection worldwide, the liver terrain is typically ignored. An immuno-tolerant environment attractive for pathogens, the essential metabolic roles and structural features of the liver are aligned with distinctive gradients of oxygen and nutrients established along blood flows through fundamental hepatic processing units known as sinusoids. Capillaries surrounded by banks of hepatocytes, sinusoids express spatial configurations and concentrations of not only metabolic roles but also immune cell localisations, blood filtering and transporter specialisations: the liver terrain. HBV targets proteins regulating gluconeogenesis, a crucial liver function of blood glucose management, highly active at blood entry points—the periportal sites of sinusoids. Meanwhile, at these same sites, specialised liver macrophages, Kupffer cells (KC), aggregate and perform critical pathogen capture, detection and signaling for modulating immune responses. In tandem with KC, liver sinusoidal endothelial cells (LSECs) complement KC blood filtration and capture of pathogens as well as determine KC aggregation at the periportal sites. Failure of these systems to establish critical spatial configurations could ironically facilitate HBV invasion and entrenchment. Investigating the impacts of spatial and structural variations on the HBV infection dynamic is experimentally challenging at best. Alternatively, mathematical modeling methods provide exquisite control over said variations, permitting teasing out the subtle and competing dynamics at play within the liver terrain. Coordinating with experimental observations, multi-scale modeling methods hold promise to illuminate HBV reliance on features of the liver terrain, and potentially how it may be defeated.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48749852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clonal abundance patterns in hematopoiesis: Mathematical modeling and parameter estimation","authors":"Yu-Cheng Pan, M. D’Orsogna, M. Tang, T. Stiehl, T. Chou","doi":"10.3389/fsysb.2023.893366","DOIUrl":"https://doi.org/10.3389/fsysb.2023.893366","url":null,"abstract":"Hematopoiesis has been studied via stem cell labeling using barcodes, viral integration sites (VISs), or in situ methods. Subsequent proliferation and differentiation preserve the tag identity, thus defining a clone of mature cells across multiple cell type or lineages. By tracking the population of clones, measured within samples taken at discrete time points, we infer physiological parameters associated with a hybrid stochastic-deterministic mathematical model of hematopoiesis. We analyze clone population data from Koelle et al. (Koelle et al., 2017) and compare the states of clones (mean and variance of their abundances) and the state-space density of clones with the corresponding quantities predicted from our model. Comparing our model to the tagged granulocyte populations, we find parameters (stem cell carrying capacity, stem cell differentiation rates, and the proliferative potential of progenitor cells, and sample sizes) that provide reasonable fits in three out of four animals. Even though some observed features cannot be quantitatively reproduced by our model, our analyses provides insight into how model parameters influence the underlying mechanisms in hematopoiesis. We discuss additional mechanisms not incorporated in our model.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48293055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling","authors":"B. Szalai, D. Veres","doi":"10.3389/fsysb.2023.1126044","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1126044","url":null,"abstract":"High dimensional characterization of drug targets, compound effects and disease phenotypes are crucial for increased efficiency of drug discovery. High-throughput gene expression measurements are one of the most frequently used data acquisition methods for such a systems level analysis of biological phenotypes. RNA sequencing allows genome wide quantification of transcript abundances, recently even on the level of single cells. However, the correct, mechanistic interpretation of transcriptomic measurements is complicated by the fact that gene expression changes can be both the cause and the consequence of altered phenotype. Perturbation gene expression profiles, where gene expression is measured after a genetic or chemical perturbation, can help to overcome these problems by directly connecting the causal perturbations to their gene expression consequences. In this Review, we discuss the main large scale perturbation gene expression profile datasets, and their application in the drug discovery process, covering mechanisms of action identification, drug repurposing, pathway activity analysis and quantitative modelling.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42861344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Systems biology, women in science 2021/22: Data and model integration","authors":"M. Rodríguez Martínez, Angelyn R. Lao, Leda Torres","doi":"10.3389/fsysb.2023.1134055","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1134055","url":null,"abstract":"Despite recent progress in encouraging and retaining talented women in science, technology, engineering, and mathematics (STEM) careers, women still face stiff penalties in the academic world. Research shows that women receive less funding, awards, teaching scores, invitations to speak at conferences, and citations than male colleagues (Berggren et al., 2022; Ainslie, 2022). To facilitate the success of our female colleagues and trainees in academia, this Research Topic aimed to highlight the work of women in Systems Biology, with a special focus on showcasing research on Data and Model Integration. It spans advances in theory, methodology, and experimental work with applications to biologically compelling problems. This Research Topic includes six original research articles, one perspective article and one technology and code article, with the participation of 41 authors from 10 countries: Colombia, France, Germany, Greece, Ireland, Mexico, Netherlands, Philippines, Switzerland, and the United Kingdom. We have a total of 7,493 views as of 9 January 2023. Overall, we were very pleased by the quality of the submissions we received in response to the call. In the Model Integration area, Connolly and colleagues presented a methodology for pandemic modelling motivated by the current COVID-19 outbreak with the title “From Epidemic to Pandemic Modelling” (Connolly et al.) Pandemicmodels are important to design effective controlmeasures, such as travel or quarantine restrictions. Here, the authors proposed a methodology for systematically extending epidemic models to multilevel and multiscale spatiotemporal pandemic models that integrate information about geography and travel connections. PetriNuts, a publicly available webbased platform, supports model construction, simulation, and output visualization. It also enables deterministic, stochastic and hybrid simulation, as well as structural and behavioural analysis. Flores-Garza and co-authors published “Mathematical Model of the Immunopathological Progression of Tuberculosis,” an elegant model to understand tuberculosis, a worldwide persistent infectious disease caused by the bacteriaMycobacterium tuberculosis (Flores-Garza et al.). Amechanistic mathematical model integrates multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays. Ordinary differential equations (ODEs) were used to describe the regulatory interplay between the cell phenotypic variation and the inflammatory microenvironment. The model can predict disease outcomes for different mouse genotypes and simulate the interaction between host and pathogen genotypes. In doing so, it provides a powerful tool to test the effect of host-pathogen interaction alterations on infection outcomes. These in silico experiments can lead to future experimentation and help reduce the number of in vivo experiments. OPEN ACCESS","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46875487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What can go wrong when observations are not independently and identically distributed: A cautionary note on calculating correlations on combined data sets from different experiments or conditions","authors":"E. Saccenti","doi":"10.3389/fsysb.2023.1042156","DOIUrl":"https://doi.org/10.3389/fsysb.2023.1042156","url":null,"abstract":"In the scientific literature data analysis results are often presented when samples from different experiments or different conditions, technical replicates or times series are merged to increase the sample size before calculating the correlation coefficient. This way of proceeding violates two basic assumptions underlying the use of the correlation coefficient: sampling from one population and independence of the observations (independence of errors). Since correlations are used to measure and infer associations between biological entities, this has tremendous implications on the reliability of scientific results, as the violation of these assumption leads to wrong and biased results. In this technical note, I review some basic properties of the Pearson’s correlation coefficient and illustrate some exemplary problems with simulated and experimental data, taking a didactic approach with the use of supporting graphical examples.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47028841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative systems modeling approaches towards model-informed drug development: Perspective through case studies","authors":"Meghna Verma, Louis Gall, J. Biasetti, G. D. Di Veroli, C. Pichardo-Almarza, M. Gibbs, Holly Kimko","doi":"10.3389/fsysb.2022.1063308","DOIUrl":"https://doi.org/10.3389/fsysb.2022.1063308","url":null,"abstract":"Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients’ treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42829618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data","authors":"Xinye Zhao, Alexander Du, Peng-Chao Qiu","doi":"10.3389/fsysb.2022.1082309","DOIUrl":"https://doi.org/10.3389/fsysb.2022.1082309","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) data often contain doublets, where a doublet manifests as 1 cell barcode that corresponds to combined gene expression of two or more cells. Existence of doublets can lead to spurious biological interpretations. Here, we present single-cell MOdel-driven Doublet Detection (scMODD), a model-driven algorithm to detect doublets in scRNA-seq data. ScMODD achieved similar performance compared to existing doublet detection algorithms which are primarily data-driven, showing the promise of model-driven approach for doublet detection. When implementing scMODD in simulated and real scRNA-seq data, we tested both the negative binomial (NB) model and the zero-inflated negative binomial (ZINB) model to serve as the underlying statistical model for scRNA-seq count data, and observed that incorporating zero inflation did not improve detection performance, suggesting that consideration of zero inflation is not necessary in the context of doublet detection in scRNA-seq.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"37 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41295868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systems biology of asphalt pollutants and their human molecular targets","authors":"Eran Rozewski, Omran Taqi, E. Fini, Nastassja A. Lewinski, J. Klein-Seetharaman","doi":"10.3389/fsysb.2022.928962","DOIUrl":"https://doi.org/10.3389/fsysb.2022.928962","url":null,"abstract":"More than 90% of all the roads in the United States are covered with asphalt, despite hundreds of scientific studies demonstrating the detrimental effect of asphalt on human health. Asphalt is a complex mixture of thousands of compounds. Here, we not only review studies of the effects of asphalt on human health, but go a step further by taking a novel view of these health effects from a systems biology perspective. In particular, we propose an analogy to protein-protein interaction networks, which can be within species and across species when looking at host-pathogen interactions. While in the former, all nodes are of the same type (e.g., human proteins), in the latter nodes can be of different types, such as human proteins and pathogen proteins. To build a corresponding network of interactions between different nodes for asphalt, we retrieved the literature studying the molecular targets of identified components in asphalt and their corresponding cellular biomarkers. Using this approach, we show that a complex trans pollutant-human target network appears in which multiple health effects can be triggered through interactions of multiple pollutant molecules with multiple human targets. We envision that the insights gained from this analysis may assist future efforts at regulating the use of asphalt.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47331715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}