Systems Biology Research Articles

Recent developments of oleaginous yeasts toward sustainable biomanufacturing.

Recent developments of oleaginous yeasts toward sustainable biomanufacturing.

GOReverseLookup: A gene ontology reverse lookup tool.

GOReverseLookup: A gene ontology reverse lookup tool.

Network pharmacology analysis of phytochemicals in targeting cancer: A systems biology approach.

e15130 Background: The intricate nature of cancer necessitates an integrative approach to uncover novel therapeutic strategies. Phytochemicals derived from medicinal plants offer a rich source of bioactive compounds with potential anti-cancer properties. Methods: This study utilises a network pharmacology framework to investigate the molecular mechanisms and multi-target effects of key phytochemicals, including quercetin, berberine, resveratrol, epigallocatechin gallate, salvianolic acids, and ginsenosides, against cancer pathways. Using a curated database of cancer-related targets, we employed molecular docking, ADMET profiling, and pathway enrichment analysis to identify and validate critical interactions between these phytochemicals and oncogenic pathways. Results: Gene Ontology (GO) analysis revealed significant enrichment in biological processes such as apoptosis regulation, cell cycle modulation, and oxidative stress responses. KEGG pathway mapping highlighted pivotal pathways, including PI3K-Akt, MAPK, and p53 signaling, as central nodes in the network, underscoring their role in tumour suppression and proliferation control. Moreover, systems pharmacology tools demonstrated the interconnectedness of phytochemicals in modulating mitochondrial function, cellular metabolism, and immune response which are recognized hallmarks of cancer progression. Conclusions: Our findings suggest that the multi-target and pleiotropic actions of these compounds are instrumental in mitigating cancer's complex pathophysiology. By integrating in silico predictions with experimental validations, we provide a blueprint for developing phytochemical-based combinatory therapies tailored to specific cancer types. This study not only advances our understanding of plant-derived compounds in oncology but also emphasizes the potential of network pharmacology as a transformative approach in drug discovery. We propose that future research and clinical trials should explore the therapeutic efficacy of these phytochemicals as part of multi-target regimens to improve treatment outcomes.

Deciphering the molecular mechanisms of Dashamoola in inflammatory bowel disease: A systems biology approach integrating network pharmacology, molecular simulations, and DFT analysis

Deciphering the molecular mechanisms of Dashamoola in inflammatory bowel disease: A systems biology approach integrating network pharmacology, molecular simulations, and DFT analysis

Peering into the Bacterial Cell: From Transcription to Functional Genomics.

Peering into the Bacterial Cell: From Transcription to Functional Genomics.

Harnessing the interplay of protein posttranslational modifications: Enhancing plant resilience to heavy metal toxicity.

Harnessing the interplay of protein posttranslational modifications: Enhancing plant resilience to heavy metal toxicity.

Exploring therapeutic paradigm focusing on genes, proteins, and pathways to combat leprosy and tuberculosis: A network medicine and drug repurposing approach.

Exploring therapeutic paradigm focusing on genes, proteins, and pathways to combat leprosy and tuberculosis: A network medicine and drug repurposing approach.

Systems biology approach reveals succinyl-CoA and hydroxycitrate as potential therapeutics for the treatment of vulvovaginal candidiasis

Systems biology approach reveals succinyl-CoA and hydroxycitrate as potential therapeutics for the treatment of vulvovaginal candidiasis

Recent advances in feature selection methods for breast cancer recurrence prediction: A systematic review.

e13664 Background: Breast cancer recurrence prediction using genomic data faces challenges due to high dimensionality and complex biological relationships. This review evaluates recent methodological advances in feature selection approaches for improving prediction accuracy while preserving biological relevance. Methods: We systematically searched SCOPUS, Web of Science, and PubMed databases and identified studies published from 2018 to 2024 focusing on feature selection methods for breast cancer recurrence prediction. We analyzed four novel feature selection methods that outperform conventional univariate and multivariate approaches. These include Outlier-based Gene Selection (OGS), Semi-Supervised Survival Laplacian Regression (S3LR), pathway-based classification, and an ensemble systems biology approach. These methods were evaluated using standardized genomic datasets to compare their performance in terms of prediction accuracy, dimensionality reduction, computational efficiency, and biological significance. Results: The OGS method achieved the highest subtype-specific accuracy (F1 score: 1.0 for basal subtype) while reducing features by 91%. S3LR demonstrated superior handling of censored data (C-index: 68.80%) with 95% dimensionality reduction. The pathway-based approach maintained 85% accuracy across subtypes with 98% feature reduction. The ensemble method showed balanced performance (F1: 0.94) with 68% feature reduction. All methods demonstrated significant enrichment in cancer-related pathways (p < 0.001). Comparative analysis revealed method-specific strengths: OGS excelled in outlier detection, S3LR in handling censored data, pathway-based in biological interpretation, and ensemble in network analysis. Conclusions: Modern feature selection methods demonstrated better accuracy with fewer features compared to traditional statistical and machine learning approaches. Future studies focusing on the clinical application of these methods for real-time prediction are needed.

Review of the Possible Role of the Stomach in the Gut–Joint Axis in Osteoarthritis: Avicenna’s Perspective Versus Modern Medicine

Osteoarthritis (OA) is the most common debilitating chronic joint disorder with no definitive treatment. Avicenna considers a strong relation between the Gastrointestinal Tract (GIT), with other body organs, including the joints. Specifically, he regards disorders of the stomach as the most important underlying cause of arthritis. The present review study aims to collect the available scientific evidence on the role of the stomach in OA in order to provide a new insight of the gut-joint axis based on Persian Medicine (PM) theory. In this narrative review, the term “vajae-al-mafasel” was searched (the equivalent term for arthritis) in Avicenna’s medical masterpiece, Canon of Medicine. Additionally, PubMed, Web of Science and Google Scholar databases were queried with keywords including OA, gut, stomach, PM, and systems biology. After gathering data, they were classified, coded, analyzed, and compared. Mechanisms that play a role in the GUT-OA axis include: 1) Gut Microbiota (GM) dysbiosis; 2) contribution of GM metabolites; 3) leaky gut syndrome; 4) bacteria transfer phenomenon; and 5) Metabolism disturbance. Growing evidence shows the pivotal role of the stomach, as part of the GIT, in the balance of metabolic functions and gut-joint axis. the role of the stomach is discussed in OA in the four sections: maintaining the metabolic balance by stomach, bone metabolism and gastric acid, controlling cartilage homeostasis by gastric hormones, gastric microbiota dysbiosis and OA.

Bioprospecting potential genetic biomarkers of gallbladder cancer.

Gallbladder cancer (GBC) is a rare and aggressive cancer of the biliary tract with a very low survival rate. The availability of diagnostic biomarkers and targeted therapies for its management is limited. The study identifies potential genetic biomarkers of GBC by analyzing differentially expressed genes (DEGs) through microarray profiling and constructing regulatory networks using systems biology techniques. We used Clariom™ D Array in gallbladder cancer, cholelithiasis, and normal tissues (10 cases in each group), identifying DEGs and key biological pathways. Functional analysis via Metascape, DisGeNET, and KEGG-SIGNOR network mapping revealed gene-disease relationships and protein interactions. There were 3,898 significant DEGs (|Fold Change| > 2.0, p < 0.05) identified in GBC compared to normal gallbladder tissue, with 2,575 genes upregulated and 1,323 downregulated. On comparison with cholelithiasis, 2523 DEGs (|Fold Change|>2.0, p < 0.05) were upregulated and 1451 downregulated. The functional analyses have shown that these DEGs were mainly involved in anatomical structure maturation and cell-cycle regulation. Top ten identified hub genes were XAB2, XPA, RPA1, RAD51B, RPS27A, BRCA2, ATR, PDS5B, CCNB2 and RANBP2. The top 3 related pathways were mismatch repair pathway, nucleotide excision repair and homologous recombination. A significantly high differential gene expression was identified in gallbladder cancer compared to control groups. For the first time, we identified key genes-XAB2, XPA, RPA1, RAD51B, RPS27A, BRCA2, ATR, PDS5B, CCNB2, and RANBP2-as crucial players in homologous recombination, mismatch repair, DNA damage repair, and DNA replication processes that contribute to gallbladder carcinogenesis.

Refining Drug-Induced Cholestasis Prediction: An Explainable Consensus Model Integrating Chemical and Biological Fingerprints.

Effective drug safety assessment, guided by the 3R principle (Replacement, Reduction, Refinement) to minimize animal testing, is critical in early drug development. Drug-induced liver injury (DILI), particularly drug-induced cholestasis (DIC), remains a major challenge. This study introduces a computational method for predicting DIC by integrating PubChem substructure fingerprints with biological data from liver-expressed targets and pathways, alongside nine hepatic transporter inhibition models. To address class imbalance in the public cholestasis data set, we employed undersampling, a technique that constructs a small and robust consensus model by evaluating distinct subsets. The most effective baseline model, which combined PubChem substructure fingerprints, pathway data and hepatic transporter inhibition predictions, achieved a Matthews correlation coefficient (MCC) of 0.29 and a sensitivity of 0.79, as validated through 10-fold cross-validation. Subsequently, target prediction using four publicly available tools was employed to enrich the sparse compound-target interaction matrix. Although this approach showed lower sensitivity compared to experimentally derived targets and pathways, it highlighted the value of incorporating specific systems biology related information. Feature importance analysis identified albumin as a potential target linked to cholestasis within our predictive model, suggesting a connection worth further investigation. By employing an expanded consensus model and applying probability range filtering, the refined method achieved an MCC of 0.38 and a sensitivity of 0.80, thereby enhancing decision-making confidence. This approach advances DIC prediction by integrating biological and chemical descriptors, offering a reliable and explainable model.

High efficiency rare earth element bioleaching with systems biology guided engineering of Gluconobacter oxydans

Biological methods are a promising route for the environmentally-friendly production of rare earth elements (REE), which are essential for sustainable energy and defense technologies. In earlier work we identified the key genetic mechanisms contributing to the REE-bioleaching capability of Gluconobacter oxydans B58. Here we have targeted two of these mechanisms to generate a high-efficiency bioleaching strain of G. oxydans. Disruption of the phosphate-specific transport system through a clean deletion of pstS constitutively turns on the phosphate starvation response, yielding a much more acidic biolixiviant, and increasing bioleaching by up to 30%. Coupling knockout of pstS with the over-expression of the mgdh membrane-bound glucose dehydrogenase gene using the P112 promoter (strain G. oxydans ΔpstS, P112:mgdh) reduces biolixiviant pH by 0.39 units; increases REE-bioleaching by 53% at a pulp density of 10% and increases it by 73% at a pulp density of 1%.

Network Pharmacology Insights into Homeopathy: Concepts, Limitations, and Future Directions

AbstractHomeopathy has long faced challenges in elucidating its mechanisms of action within the frameworks of modern biomedical science, primarily due to its high dilution and individualistic approaches. Recent advances in network pharmacology offer a promising avenue to bridge this gap by emphasizing the interconnectedness of biological systems and the multitarget nature of therapeutic agents. This paradigm aligns conceptually with homeopathy's holistic approach to health and disease. This article explores how network pharmacology can be applied to homeopathic research, particularly for remedies derived from plant, mineral, and animal sources. By leveraging bioinformatics databases, molecular docking tools, and systems biology platforms, potential targets and pathways modulated by homeopathic remedies can be identified. Integration of phytochemical profiling, disease–gene associations, and symptomatology data from materia medica allows the construction of remedy–target–disease networks, providing insights into the systemic effects of homeopathic medicines. We discuss key methodological strategies, such as in silico target prediction, multi-omics integration, and network visualization, as well as the challenges posed by ultra-high dilutions and limited pharmacological data. Future directions emphasize the need for interdisciplinary research, experimental validation, and dedicated homeopathy-specific databases to enhance the utility of network pharmacology in this domain. By aligning the principles of homeopathy with systems-level scientific tools, network pharmacology has the potential to transform our understanding of homeopathic therapeutics. This integrative approach may lead to greater scientific acceptance, improved clinical outcomes, and a renewed role for homeopathy in contemporary health care.

System biology approach in predictive model design for the control of Parkinsons disease and degradation of chlorpyrifos by human gut microbiome

Abstract Chlorpyrifos strikes out as an extensively utilized organophosphorus insecticide, certified for its neurotoxic properties that have been embroiled in the etiology of Parkinson’s disease, highlighting the imperativeness of constructive degradation strategies. While bioremediation approaches have been explored for the metabolism of chlorpyrifos, there remains a pronounced void in apprehending its degradation via the human gut microbiome. This study addresses this considerable shortfall by scrutinizing the degradative proficiency of Ralstonia pickettii and Escherichia coli, leveraging CellDesigner Ver. 4.4 for the development of gene-regulatory and biochemical pathways. Pathway construction was succeeded by the ascertainment of rate equation employing SBML squeezer 2.1, aiming to elucidate the biochemical and physiological metabolism of chlorpyrifos across various compartments within the human system. Experimental results demonstrated a conspicuous reduction in chlorpyrifos concentration, alongside its metabolites TCP and DETP, over time, facilitated by the action of tcpA, dhpJ, pdeA, and phoA genes derived from the microbial community. These findings stipulate that human gut microbes endow a substantial capability for the degradation of chlorpyrifos and its metabolites, thus promoting the goal of positioning the human gut microbiome as a biomarker to investigate the bioaccumulation dynamics of pesticides within the body and their potential relationship with Parkinson’s disease.

Computational systems biology approaches to cellular aging—Integrating network maps and dynamical models

AbstractCellular aging is a multifaceted complex process. Many genes and factors have been identified that regulate cellular aging. However, how these genes and factors interact with one another and how these interactions drive the aging processes in single cells remain largely unclear. Recently, computational systems biology has demonstrated its potential to empower aging research by providing quantitative descriptions and explanations of complex aging phenotypes, mechanistic insights into the emergent dynamic properties of regulatory networks, and testable predictions that can guide the design of new experiments and interventional strategies. In general, current complex systems approaches can be categorized into two types: (1) network maps that depict the topologies of large‐scale molecular networks without detailed characterization of the dynamics of individual components and (2) dynamical models that describe the temporal behavior in a particular set of interacting factors. In this review, we discuss examples that showcase the application of these approaches to cellular aging with a specific focus on the progress in quantifying and modeling the replicative aging of budding yeast Saccharomyces cerevisiae. We further propose potential strategies for integrating network maps and dynamical models toward a more comprehensive, mechanistic, and predictive understanding of cellular aging. Finally, we outline directions and questions in aging research where systems‐level approaches may be especially powerful.

Systems Biology of Streptophyte Cell Evolution.

More than 500 million years ago, a streptophyte algal population established a foothold on land and started terraforming Earth through an unprecedented radiation. This event is called plant terrestrialization and yielded the Embryophyta. Recent advancements in the field of plant evolutionary developmental biology (evo-devo) have propelled our knowledge of the closest algal relatives of land plants, the zygnematophytes, highlighting that several aspects of plant cell biology are shared between embryophytes and their sister lineage. High-throughput exploration determined that routes of signaling cascades, biosynthetic pathways, and molecular physiology predate plant terrestrialization. But how do they assemble into biological programs, and what do these programs tell us about the principal functions of the streptophyte cell? Here, we make the case that streptophyte algae are unique organisms for understanding the systems biology of the streptophyte cell, informing on not only the origin of embryophytes but also their fundamental biology.

Preliminary Study of Differential circRNA Expression and Investigation of circRNA-miRNA-mRNA Competitive Endogenous Network in Rumen Acidosis of Holstein Cattle.

Rumen acidosis is a widespread digestive disorder in livestock, causing inflammation and lowering animal performance. Unraveling its molecular mechanisms is vital for improving cattle health and welfare. Circular RNAs (circRNAs) are noncoding RNAs functioning as miRNA or protein sponges. This study employed high-throughput RNA sequencing to identify differentially expressed (DE) circRNAs in subacute rumen acidosis (SARA) in Holstein cattle, revealing 65 DE-circRNAs. We constructed a competitive endogenous RNA (ceRNA) network comprising 57 circRNAs, 14 miRNAs, and 22 mRNAs. Key hub nodes included circRNAs (8:69996068-69996853, 16:2614111-2615445, 5:109525933-109531380, 20:63115665-63116774), miRNAs (bta-miR-146b, bta-miR-181a, bta-miR-223, bta-miR-130b), and mRNAs (SLC2A3, SOCS3, DLC1, ARRDC4). Examination of hub circRNA host genes identified 30 DE transcription factors (TFs). Functional and pathway enrichment analysis pinpointed inflammation and immune response pathways, such as NF-kappa B and TNF signaling. This pioneering study offers the first circRNA expression profile and ceRNA network in SARA cattle, indicating circRNAs' role in inflammation regulation, thus enhancing our understanding of SARA's systems biology and potential treatment strategies.

Conformal prediction for uncertainty quantification in dynamic biological systems.

Uncertainty quantification (UQ) is the process of systematically determining and characterizing the degree of confidence in computational model predictions. In systems biology, and particularly with dynamic models, UQ is critical due to the nonlinearities and parameter sensitivities that influence the behavior of complex biological systems. Addressing these issues through robust UQ enables a deeper understanding of system dynamics and more reliable extrapolation beyond observed conditions. Many state-of-the-art UQ approaches in this field are grounded in Bayesian statistical methods. While these frameworks naturally incorporate uncertainty quantification, they often require the specification of parameter distributions as priors and may impose parametric assumptions that do not always reflect biological reality. Additionally, Bayesian methods can be computationally expensive, posing significant challenges when dealing with large-scale models and seeking rapid, reliable uncertainty calibration. As an alternative, we propose using conformal predictions methods and introduce two novel algorithms designed for dynamic biological systems. These approaches can provide non-asymptotic guarantees, improving robustness and scalability across various applications, even when the predictive models are misspecified. Through several illustrative scenarios, we demonstrate that these conformal algorithms can serve as powerful complements-or even alternatives-to conventional Bayesian methods, delivering effective uncertainty quantification for predictive tasks in systems biology.

Food hoarding, anxiety, and stress in a mammalian hibernator.

Diverse vertebrate species utilize hibernation as an energy-management strategy to survive long-term resource scarcity. Many hibernating and non-hibernating species employ food hoarding as a behavioral mechanism for storing surplus energy. Although the thirteen-lined ground squirrel (Ictidomys tridecemlineatus, 13LGS) relies mainly on body fat as fuel during hibernation, they also hoard food under natural and captive conditions. We tested the hypothesis that 13LGS individual variation in facultative hoarding behavior is driven by internal physiology, including body mass, sex, anxiety, and baseline stress. We recorded food hoard composition and body weight biweekly in summer active squirrels, subjected them to the open-field test (OFT) to measure state anxiety, and quantified fecal corticosterone (CORT) levels to measure baseline stress. We found that hoard sizes increased significantly across the summer, peaking at the end of June when body weight was still linearly increasing. Individual variation accounted for 10-20% of total variation in hoarding patterns. We observed a significant effect of sex on hoard size and composition, with males hoarding more than females. Contrary to our predictions, there was no relationship between hoarding and anxiety-like behavior in the OFT, and non-hoarders had significantly higher fecal CORT than hoarders. Together, our results suggest that time, sex, and baseline stress are significant factors that affect hoarding behavior, but body weight and anxiety-like behaviors are not. In the context of organismal systems biology, food hoarding is a redundant mechanism to fat storage that increases an organism's resilience against future resource scarcity and is likely regulated by dynamic interactions between multiple brain-body networks.