Computational and structural biotechnology journal最新文献

{"title":"Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification","authors":"Sabrina Napoletano ,&nbsp;David Dannhauser ,&nbsp;Paolo Antonio Netti ,&nbsp;Filippo Causa","doi":"10.1016/j.csbj.2024.12.023","DOIUrl":"10.1016/j.csbj.2024.12.023","url":null,"abstract":"<div><div>MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 233-242"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the pan-cancer role of exosomal miRNAs in metastasis across cancers","authors":"Piyush Agrawal ,&nbsp;Gulden Olgun ,&nbsp;Arashdeep Singh ,&nbsp;Vishaka Gopalan ,&nbsp;Sridhar Hannenhalli","doi":"10.1016/j.csbj.2024.12.025","DOIUrl":"10.1016/j.csbj.2024.12.025","url":null,"abstract":"<div><div>Exosomal microRNAs (exomiRs) play a critical role in intercellular communication, especially in cancer, where they regulate key cellular processes like proliferation, angiogenesis, and metastasis, highlighting their significance as potential diagnostic and therapeutic targets. Here, we aimed to characterize the role of exomiRs, derived from seven cancer types (four cell lines and three tumors), in influencing the pre-metastatic niche (PMN). In each cancer type we extracted high confidence exomiRs (LogFC &gt;= 2 in exosomes relative to control), their experimentally validated targets, and the enriched pathways among those targets. We then selected the top100 high-confidence targets based on their frequency of appearance in the enriched pathways. We observed significantly higher GC content in exomiRs relative to genomic background. Gene Ontology analysis revealed both general cancer processes, such as wound healing and epithelial cell proliferation, as well as cancer-specific processes, such as “angiogenesis” in the kidney and “ossification” in the lung. ExomiR targets were enriched for cancer-specific tumor suppressor genes and downregulated in PMN formed in lungs compared to normal. Motif analysis showed high inter-cancer similarity among motifs enriched in exomiRs. Our analysis recapitulated exomiRs associated with M2 macrophage differentiation and chemoresistance, such as miR-21 and miR-222–3p, regulating signaling pathways like PTEN/PI3/Akt, NF-kB, etc. Additionally, Cox regression analysis in TCGA indicated that exomiR targets are significantly associated with better overall survival of patients. Lastly, support vector machine model using exomiR targets gene expression classified responders and non-responders to therapy with an AUROC ranging from 0.72 to 0.96, higher than previously reported gene signatures.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 252-264"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11763893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network-based estimation of therapeutic efficacy and adverse reaction potential for prioritisation of anti-cancer drug combinations","authors":"Arindam Ghosh,&nbsp;Vittorio Fortino","doi":"10.1016/j.csbj.2024.12.003","DOIUrl":"10.1016/j.csbj.2024.12.003","url":null,"abstract":"<div><div>Drug combinations, although a key therapeutic agent against cancer, are yet to reach their full applicability potential due to the challenges involved in the identification of effective and safe drug pairs. <em>In vitro</em> or in vivo screening would have been the optimal approach if combinatorial explosion was not an issue. <em>In silico</em> methods, on the other hand, can enable rapid screening of drug pairs to prioritise for experimental validation. Here we present a novel network medicine approach that systematically models the proximity of drug targets to disease-associated genes and adverse effect-associated genes, through the combination of network propagation algorithm and gene set enrichment analysis. The proposed approach is applied in the context of identifying effective drug combinations for cancer treatment starting from a training set of drug combinations curated from DrugComb and DrugBank databases. We observed that effective drug combinations usually enrich disease-related gene sets while adverse drug combinations enrich adverse-effect gene sets. We use this observation to systematically train classifiers distinguishing drug combinations with higher therapeutic effects and no known adverse reaction from combinations with lower therapeutic effects and potential adverse reactions in six cancer types. The approach is tested and validated using drug combinations curated from in vitro screening data and clinical reports. Trained classification models are also used to identify novel potential anti-cancer drug combinations for experimental validation. We believe our framework would be a key addition to the anti-cancer drug combination identification pipeline by enabling rapid yet robust estimation of therapeutic efficacy or adverse reaction potential.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 65-77"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IGN: Invariable gene set-based normalization for chromatin accessibility profile data analysis","authors":"Shengen Shawn Hu ,&nbsp;Hai-Hui Xue ,&nbsp;Chongzhi Zang","doi":"10.1016/j.csbj.2025.01.018","DOIUrl":"10.1016/j.csbj.2025.01.018","url":null,"abstract":"<div><div>Chromatin accessibility profiles generated using ATAC-seq or DNase-seq carry functional information of the regulatory genome that controls gene expression. Appropriate normalization of ATAC-seq and DNase-seq data is essential for accurate differential analysis when studying chromatin dynamics. Existing normalization methods usually assume the same distribution of genomic signals across samples; however, this assumption may not be appropriate when there are global changes in chromatin accessibility levels between experimental conditions/samples. We present IGN (Invariable Gene Normalization), a method for ATAC-seq and DNase-seq data normalization. IGN normalizes the promoter chromatin accessibility signals for a set of genes that are unchanged in expression, usually obtained from accompanying RNA-seq data, and extrapolating to normalize the genome-wide chromatin accessibility profile. We demonstrate the effectiveness of IGN in analyzing central memory CD8⁺ T cell activation, a system with anticipated global reprogramming of chromatin and gene expression, and show that IGN outperforms existing methods. As the first chromatin accessibility normalization method that accounts for global differences, IGN can be widely applied to differential ATAC-seq and DNase-seq analysis. The package and source code are available on GitHub at <span><span>https://github.com/zang-lab/IGN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 501-507"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ecological and stochastic perspective on persisters resuscitation","authors":"Tania Alonso-Vásquez ,&nbsp;Michele Giovannini ,&nbsp;Gian Luigi Garbini ,&nbsp;Mikolaj Dziurzynski ,&nbsp;Giovanni Bacci ,&nbsp;Ester Coppini ,&nbsp;Donatella Fibbi ,&nbsp;Marco Fondi","doi":"10.1016/j.csbj.2024.12.002","DOIUrl":"10.1016/j.csbj.2024.12.002","url":null,"abstract":"<div><div>Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 1-9"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure-based identification of HNF4α agonists: Rosmarinic acid as a promising candidate for NAFLD treatment","authors":"Xi Chen ,&nbsp;Xinqi Zhu ,&nbsp;Gang Wu ,&nbsp;Xiaobo Wang ,&nbsp;Yu Zhang ,&nbsp;Nan Jiang","doi":"10.1016/j.csbj.2024.12.014","DOIUrl":"10.1016/j.csbj.2024.12.014","url":null,"abstract":"<div><div>The prevention and treatment of metabolic disorders, such as non-alcoholic fatty liver disease (NAFLD), have emerged as critical global health challenges. Current lipid-lowering pharmacotherapies are associated with side effects, including hepatotoxicity, rhabdomyolysis, and decreased erythrocyte counts, underscoring the urgent need for safer therapeutic alternatives. Hepatocyte nuclear factor 4α (HNF4α) has been identified as a pivotal regulator of lipid metabolism, making it an attractive target for drug development. In this study, we investigated the structural characteristics and binding interactions of four HNF4α agonists: Alverine, Benfluorex, N-trans caffeoyltyramine (NCT), and N-trans feruloyltyramine (NFT). Our results indicate that the conjugated structure formed by the amide bond and the aromatic ring in NCT and NFT enhances electron density, potentially contributing to their increased specificity for HNF4α relative to Alverine and Benfluorex. Additionally, electrostatic interactions between the aromatic moieties of the compounds and HNF4α residues were found to play a crucial role in ligand binding. Leveraging these insights, we performed a high-throughput virtual screening of 2131 natural compounds, using the binding modes of NCT and NFT as reference templates. Rosmarinic acid emerged as a promising HNF4α agonist, exhibiting a high consensus score and favorable binding affinity. Subsequent biological assays demonstrated that rosmarinic acid significantly inhibited HepG2 cell proliferation which related to the enhancement of autophagy. After the knockdown of P2 isoform of HNF4α, HepG2 was more sensitive to the administration of NCT and rosmarinic acid. Furthermore, the proliferation of DLD-1 cell, which only expresses the P2 isoform of HNF4α, was not significantly inhibited by the administration of NCT and rosmarinic acid. Collectively, these findings suggest that rosmarinic acid is a promising HNF4α agonist which is more effective to activate the P1 isoform of HNF4α and holds potential as an effective treatment for NAFLD, providing a foundation for the development of novel lipid-lowering drugs with enhanced efficacy and reduced side effect.</div></div><div><h3>Data Availability</h3><div>Data will be made available on request.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 171-183"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice","authors":"Guan-Heng Liu ,&nbsp;Chin-Ling Li ,&nbsp;Chih-Yuan Yang ,&nbsp;Shih-Feng Liu","doi":"10.1016/j.csbj.2025.01.015","DOIUrl":"10.1016/j.csbj.2025.01.015","url":null,"abstract":"<div><h3>Background</h3><div>Chronic Obstructive Pulmonary Disease (COPD) is a major contributor to global morbidity and healthcare costs. Accurately predicting these costs is crucial for resource allocation and patient care. This study developed and validated an AI-driven COPD Medical Cost Prediction Index (MCPI) to forecast healthcare expenses in COPD patients.</div></div><div><h3>Methods</h3><div>A retrospective analysis of 396 COPD patients was conducted, utilizing clinical, demographic, and comorbidity data. Missing data were addressed through advanced imputation techniques to minimize bias. The final predictors included interactions such as Age × BMI, alongside Tumor Presence, Number of Comorbidities, Acute Exacerbation frequency, and the DOSE Index. A Gradient Boosting model was constructed, optimized with Recursive Feature Elimination (RFE), and evaluated using 5-fold cross-validation on an 80/20 train-test split. Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²).</div></div><div><h3>Results</h3><div>On the training set, the model achieved an MSE of 0.049, MAE of 0.159, MAPE of 3.41 %, and R² of 0.703. On the test set, performance metrics included an MSE of 0.122, MAE of 0.258, MAPE of 5.49 %, and R² of 0.365. Tumor Presence, Age, and BMI were identified as key predictors of cost variability.</div></div><div><h3>Conclusions</h3><div>The MCPI demonstrates strong potential for predicting healthcare costs in COPD patients and enables targeted interventions for high-risk individuals. Future research should focus on validation with multicenter datasets and the inclusion of additional socioeconomic variables to enhance model generalizability and precision.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 541-547"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data","authors":"Jonathan Elliot Perdomo ,&nbsp;Mian Umair Ahsan ,&nbsp;Qian Liu ,&nbsp;Li Fang ,&nbsp;Kai Wang","doi":"10.1016/j.csbj.2025.01.019","DOIUrl":"10.1016/j.csbj.2025.01.019","url":null,"abstract":"<div><div>While several well-established quality control (QC) tools exist for short-read sequencing data, there is a general paucity of computational tools that efficiently deliver comprehensive metrics across a wide range of long-read sequencing data formats, such as Oxford Nanopore (ONT) POD5, ONT FAST5, ONT basecall summary, Pacific Biosciences (PacBio) unaligned BAM, and Illumina Complete Long Read (ICLR) FASTQ file formats. In addition to nucleotide sequence information, some file formats such as POD5 contain raw signal information used for base calling, while other file formats such as aligned BAM contain alignments to a linear reference genome or transcriptome and may also contain base modification information. There is currently no single available QC tool capable of summarizing each of these features. Furthermore, high-performance tools are required to efficiently process the growing data volumes from long-read sequencing platforms. To address these challenges, here we present LongReadSum, a high-performance tool for generating a summary QC report for major types of long-read sequencing data. We also demonstrate a few examples using LongReadSum to analyze cDNA sequencing, direct RNA sequencing, ONT reduced representation methylation sequencing (RRMS), and whole genome sequencing (WGS) data.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 556-563"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the impact of missense mutations on an unresolved protein’s stability, structure, and function: A case study of Alzheimer’s disease-associated TREM2 R47H variant","authors":"Joshua Pillai ,&nbsp;Kijung Sung ,&nbsp;Chengbiao Wu","doi":"10.1016/j.csbj.2025.01.024","DOIUrl":"10.1016/j.csbj.2025.01.024","url":null,"abstract":"<div><div>AlphaFold2 (AF2) has spurred a revolution in predicting unresolved structures of wild-type proteins with high accuracy. However, AF2 falls short of predicting the effects of missense mutations on unresolved protein structures that may be informative to efforts in personalized medicine. Over the last decade, countless in-silico methods have been developed to predict the pathogenicity of point mutations on resolved structures, but no studies have evaluated their capabilities on unresolved protein structures predicted by AF2. Herein, we investigated Alzheimer's disease (AD)-causing coding variants of the triggering receptor expressed on myeloid cells 2 (TREM2) receptor using in-silico mutagenesis techniques on the AF2-predicted structure. We first demonstrated that the predicted structure retained a high accuracy in critical regions of the extracellular domain and subsequently validated the in-silico mutagenesis methods by evaluating the effects of the strongest risk variant R47H of TREM2. After validation of the R47H variant, we predicted the molecular basis and effects on protein stability and ligand-binding affinity of the R62H and D87N variants that remain unknown in current literature. By comparing it with the R47H variant, our analysis reveals that R62H and D87N variants exert a much less pronounced effect on the structural stability of TREM2. These in-silico findings show the possibility that the R62H and D87N mutations are likely less pathogenic than the R47H AD. Lastly, we investigated the Nasu-Hakola (NHD)-causing Y38C and V126G TREM2 as a comparison and found that they imposed greater destabilization compared to AD-causing variants. We believe that the in-silico mutagenesis methods described here can be applied broadly to evaluate the ever-growing numbers of protein mutations/variants discovered in human genetics study for their potential in diseases, ultimately facilitating personalized medicine.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 564-574"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation","authors":"Seok Joo Chae ,&nbsp;Seolah Shin ,&nbsp;Kangmin Lee ,&nbsp;Seunggyu Lee ,&nbsp;Jae Kyoung Kim","doi":"10.1016/j.csbj.2025.01.004","DOIUrl":"10.1016/j.csbj.2025.01.004","url":null,"abstract":"<div><div>Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, this algorithm typically assumes that DNA is homogeneously distributed throughout the nucleus, which is not realistic. In this study, we evaluated whether stochastic simulations based on the assumption of spatial homogeneity can accurately capture the dynamics of gene regulation. Our findings indicate that when transcription factors diffuse slowly, these simulations fail to accurately capture gene expression, highlighting the necessity to account for spatial heterogeneity. However, incorporating spatial heterogeneity considerably increases computational time. To address this, we explored various stochastic quasi-steady-state approximations (QSSAs) that simplify the model and reduce simulation time. While both the stochastic total quasi-steady state approximation (stQSSA) and the stochastic low-state quasi-steady-state approximation (slQSSA) reduced simulation time, only the slQSSA provided an accurate model reduction. Our study underscores the importance of utilizing appropriate methods for efficient and accurate stochastic simulations of gene regulatory dynamics, especially when incorporating spatial heterogeneity.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 411-422"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}