Frontiers in systems biology最新文献

[This corrects the article DOI: 10.3389/fsysb.2025.1631901.].

Bacteria rely on two-component signaling systems (TCSs) to detect environmental cues and orchestrate adaptive responses. Despite their apparent simplicity, TCSs exhibit a rich spectrum of dynamic behaviors arising from network architectures, such as bifunctional enzymes, multi-step phosphorelays, transcriptional feedback loops, and auxiliary interactions. This study develops a generalized mathematical model of a TCS that integrates these various elements. Using systems-level analysis, we elucidate how network architecture and biochemical parameters shape key properties such as stability, monotonicity, and signal amplification. Analytical conditions are derived for when the steady-state levels of phosphorylated proteins exhibit robustness to variations in protein abundance. The model characterizes how equilibrium phosphorylation levels depend on the absolute and relative abundances of the two components. Specific scenarios are explored, including the MprAB system from Mycobacterium tuberculosis and the EnvZ/OmpR system from textit Escherichia coli, to describe the potential role of reverse phosphotransfer reactions. By combining mechanistic modeling with system-level techniques, such as nullcline analysis, this study offers a unified perspective on the design principles underlying the versatility of bacterial signal transduction. The generalized modeling framework lays a theoretical foundation for interpreting experimental dynamics and rationally engineering synthetic TCS circuits with prescribed response dynamics.

Purpose: Fasting is known to have beneficial effects on human physiology and health due to changes in gut microbiota and its associated metabolites. We investigated the effects of intermittent and Ramadan fasting on the gut microbial composition, diversity, and short-chain fatty acid (SCFA) profile in a Pakistani population.

Methods: Paired fecal samples-a total of 29 for Ramadan fasting (divided into three groups, before and after completion and after 3 months) and 22 for intermittent fasting (divided into two groups, day 1 and day 10)-were collected for both 16S rRNA microbiome profiling and SCFA analysis. Study volunteers also provided a detailed questionnaire about the dietary regimen before and during the fasting period. Descriptive statistics were applied to ascertain variations in the gut microbiome and SCFAs attributable to changes in food consumption during fasting.

Results: Ramadan fasting increased the bacterial taxonomic and functional diversity and decreased the abundance of certain harmful microbes such as Blautia, Haemophilus, Desulfovibrio, Lachnoclostridium, and Porphyromonas. Intermittent fasting showed increased abundance of Prevotella, Lactobacillus, and Anaerostipes. Ramadan fasting also led to a significant increase in SCFAs including C7, iC4, and iC6, accounting for variability in microbial composition and phylogeny, respectively. In intermittent fasting, C5, iC5, and iC6 contributed to variability in microbial composition, phylogeny, and function, respectively.

Conclusion: Both fasting regimens impacted gut microbiome and metabolic signatures, but Ramadan fasting showed a more drastic effect due to the 30 days compliance period and water restriction than intermittent fasting. Ramadan fasting also improved metabolic health by increasing the abundance of SCFA-producing microbes. With Ramadan fasting, most microbial taxa reverted to their prefasting state after resumption of normal feeding patterns with few exceptions, indicating impact on microbial niche creation with prolonged fasting regimens that benefit Enterococcus, Turibacter, and Klebsiella colonization. The dietary regimen adopted during fasting, especially the consumption of high-fat-content food items, accounted for persistent gut microbial changes.

Study objectives: This study examines age-related variations in activity patterns using actigraphy data from the National Health and Nutrition Examination Survey (NHANES). By analyzing sleep onset, wake times, and daily activity levels across different age groups, we aim to uncover key changes in chronotype and physical engagement with aging. From a systems-biology perspective, minute-level rest-activity traces are emergent outputs of coupled circadian-homeostatic-behavioral networks. Treating actigraphy as a high-throughput phenotyping readout, we use NHANES to extract system-level markers (phase, amplitude, and transition dynamics) that reflect network organization across the lifespan.

Methods: Actigraphy data from NHANES (2011-2013) were analyzed using machine learning techniques to identify distinct activity clusters among four age groups (19-30, 31-50, 51-70, 71-80). We implemented an unsupervised machine learning pipeline that clustered average-day actigraphy profiles, enabling the identification of distinct, age-dependent rest-activity phenotypes from the NHANES dataset. Sleep-wake cycles, activity intensities, and circadian periodicities were assessed through clustering and statistical modeling. Key metrics, including winding down activity and time to alertness, were derived to evaluate age-related variations.

Results: Younger individuals exhibited delayed chronotypes with later sleep and wake times, whereas older adults showed advanced and more structured schedules. Winding down periods lengthened with age, and overall activity levels declined progressively. Time to alertness showed a strong correlation with wake time in younger groups but diminished with age, indicating a weakening circadian influence.

Conclusion: Aging is associated with shifts in sleep-wake cycles and activity patterns, reflecting biological and behavioral adaptations. These findings highlight the importance of personalized interventions to support optimal activity and sleep alignment across the lifespan. Insights from actigraphy data can inform public health strategies and clinical approaches to aging-related changes in physical activity and circadian regulation. These age-stratified, interpretable "dynamical phenotypes" provide observables to calibrate and validate systems-level models of sleep-wake regulation and behavior-physiology coupling, supporting hypothesis generation and intervention design in systems biology.

Prioritizing actionable drug targets is a critical challenge in cancer research, where high-dimensional genomic data and the complexity of tumor biology often hinder effective prioritization. To address this, we developed GETgene-AI, a novel computational framework that integrates network-based prioritization, machine learning, and automated literature analysis to prioritize and rank potential therapeutic targets. Central to GETgene-AI is the G.E.T. strategy, which combines three data streams: mutational frequency (G List), differential expression (E List), and known drug targets (T List). These components are iteratively refined and ranked using the Biological Entity Expansion and Ranking Engine (BEERE), leveraging protein-protein interaction networks, functional annotations, and experimental evidence. Additionally, GETgene-AI incorporates GPT-4o, an advanced large language model, to automate literature-based ranking, reducing manual curation and increasing efficiency. In this study, we applied GETgene-AI to pancreatic cancer as a case study. The framework successfully prioritized high-priority targets such as PIK3CA and PRKCA, validated through experimental evidence and clinical relevance. Benchmarking against GEO2R and STRING demonstrated GETgene-AI's superior performance, achieving higher precision, recall, and efficiency in prioritizing actionable targets. Moreover, the framework mitigated false positives by deprioritizing genes lacking functional or clinical significance. While demonstrated on pancreatic cancer, the modular design of GETgene-AI enables scalability across diverse cancers and diseases. By integrating multi-omics datasets with advanced computational and AI-driven approaches, GETgene-AI provides a versatile and robust platform for accelerating cancer drug discovery. This framework bridges computational innovations with translational research to improve patient outcomes.

In silico clinical trials that simulate individualized mechanisms of action offer a powerful approach to assess drug efficacy across large and diverse patient populations, while also enabling the identification of predictive biomarkers. In this study, we conducted an in silico clinical trial of first-line, single-agent regorafenib in 399 elderly patients with metastatic colorectal cancer (mCRC). Individualized network-based models were constructed using patient-specific differential transcriptomic profiles and employed to simulate the target-specific effects of regorafenib. From this analysis, we identified both predictive and mechanistic biomarkers of treatment response. Notably, four proteins-MARK3, RBCK1, LHCGR, and HSF1-emerged as dual biomarkers, showing associations with both response mechanisms and predictive potential. Three of these (MARK3, RBCK1, and HSF1) were validated in an independent cohort of mCRC patients and were also found to be targets of previously reported regorafenib-predictive miRNAs. This study demonstrates a novel systems biology strategy for evaluating drug response in silico, leveraging transcriptomic data to simulate individual treatment outcomes and uncover clinically relevant biomarkers. Our findings suggest that such approaches may serve as valuable complements to traditional clinical trials for assessing drug efficacy and guiding precision oncology.

Collaborations between industry leaders and academia are crucial for advancing systems biology education and training. This article explores opportunities for partnerships to enhance the educational landscape and develop a workforce skilled in systems modelling, particularly for quantitative systems pharmacology (QSP) in drug development. Companies with a strong focus on innovation frequently explore collaborative ventures involving joint research, co-designed curricula, and specialized training programs. These partnerships provide students and researchers with insights into real-world applications of systems biology and QSP. We explicitly review success criteria for collaboration at MSc and PhD levels, discuss earlier pipeline considerations, and carefully balance the roles, incentives, and challenges for both academia and industry in collaborative ventures. Challenges in aligning academic and industry objectives exist, including resource allocation and intellectual property concerns. However, the importance of training skilled systems biologists for advancing drug discovery and development outweighs these challenges. The article concludes by highlighting successful industry-academia partnerships and offering recommendations for optimizing collaborations to meet the evolving needs of systems biology education and drive innovation in pharmaceutical research.

The microbiome represents a complex community of trillions of microorganisms residing in various body parts and plays critical roles in maintaining host health and wellbeing. Understanding the interactions between microbiota and their host offers valuable insights into potential strategies for promoting health, including microbiome-targeted interventions. We have created MicrobiomeKG, a knowledge graph for microbiome research, that bridges various taxa and microbial pathways with host health. This novel knowledge graph derives algorithmically generated knowledge assertions from the supplementary tables that support published microbiome papers. By identifying knowledge assertions from supplementary tables and expressing them as knowledge graphs, we are casting this valuable content into a format that is ideal for hypothesis generation. To address the high heterogeneity of study contexts, methodologies, and reporting standards, we leveraged neural networks to implement a standardized edge scoring system, which we use to perform centrality analyses. We present three example use cases: linking helminth infections with non-alcoholic fatty-liver disease via microbial taxa, exploring connections between the Alistipes genus and inflammation, and identifying the Bifidobacterium genus as the most central connection with attention deficit hyperactivity disorder. MicrobiomeKG is deployed for integrative analysis and hypothesis generation, both programmatically and via the Biomedical Data Translator ecosystem. By bridging data gaps and facilitating the discovery of new biological relationships, MicrobiomeKG will help advance personalized medicine through a deeper understanding of the microbial contributions to human health and disease mechanisms.

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. A wide range of software, tools, and packages have been developed for BNs analysis and made available to academic researchers and industry practitioners. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, with a focus on causal discovery tools, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader's understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it.

Mathematical models for cellular systems have become more and more important for understanding the complex interplay between metabolism, signalling, and gene expression.In this manuscript, starting from the well-known flux balance analysis, tools and methods are summarised and illustrated by various examples that describe the way to models with kinetics for individual reactions steps that are finally self-contained. While flux analysis requires known (measured) input fluxes, self-contained (or self-sustained) models only get information on concentrations of environmental components. Kinetic reaction laws, feedback structures, and protein allocation then determine the temporal output of all intracellular metabolites and macromolecules. Emphasis is placed on (i) mass conservation, a crucial system property frequently overlooked in models incorporating cellular structures like macromolecular structures like proteins, RNA, and DNA, and (ii) thermodynamic constraints which further limit the solution space. Matlab Live Scripts are provided for all simulation studies shown and additional reading material is given in the appendix.