Abstract For over a decade, the Journal of Integrative Bioinformatics has published special issues providing updated specifications for standards in systems biology and synthetic biology. Beginning in 2024, the special issues expanded to include novel or refined tools that leverage these standards or play a key role in their implementation. The current special issue contains four updated specifications and two tools or libraries. In addition, this document functions as a comprehensive reference for all COMBINE standard specifications, ensuring researchers and practitioners can access the most up-to-date versions available in the field.
Abstract The Systems Biology Graphical Notation (SBGN) is a global, community-driven initiative that provides standardised visual languages for depicting biological pathways and networks. Its purpose is to deliver clear, consistent diagrams that support accurate communication and knowledge exchange across research communities, industry, and other stakeholders in systems biology. SBGN comprises three complementary languages, Process Description (PD), Entity Relationship (ER), and Activity Flow (AF), each capturing distinct facets of biological and biochemical systems at varying levels of detail. The AF language depicts the activities of biological entities and their mutual influences, focusing on performed functions rather than the mechanistic or relational detail emphasised in PD and ER. AF includes activity nodes (e.g., biological activities such as kinase or binding, and phenotypes) and structural elements (e.g., compartments, submaps, tags), with edges representing the influence relationships between activities (e.g., positive and negative influence). AF aligns closely with the style of signaling pathway diagrams found in biological literature, while providing enhanced semantic precision for capturing and communicating biological knowledge. Level 1 Version 1.3 introduces several improvements over Level 1 Version 1.2, including refined document organization and terminology, clearer and updated specification content, and revised figures and rules to strengthen consistency and interpretability throughout the language.
Abstract Helicobacter pylori , a Gram-negative microorganism, infects over half of the human population worldwide. Its extensive genetic variability and capacity to bypass host immune defences continue to impede the creation of an effective vaccine. Nonetheless, contemporary progress in computational biology and immunoinformatics has introduced innovative methodologies that can substantially support and accelerate vaccine design against this pathogen. In the present work, an in silico strategy was employed to develop a multi-epitope vaccine targeting H. pylori . The FlaA, UreB and PSA D15 proteins were chosen as primary antigens due to their strong immunogenic potential, critical roles in bacterial physiology, and comparatively low sequence variability among strains. Using advanced bioinformatics and immunoinformatics pipelines, potential epitopes for both B-cell and T-cell were identified and assembled through rational protein engineering approaches to generate the final vaccine construct. Before computational validation, the safety of the selected epitopes was predicted through toxicity, allergenticity, and non-homology to human analyses, which resulted on positive results. Furthermore, the construct’s physicochemical parameters were under acceptable values, as for the population coverage. This construct was subsequently analysed through molecular docking simulations to evaluate its interaction with host immune receptors. The obtained results demonstrated that the designed molecule could accomplish stable interactions with high affinity with the immune components, supporting its potential as a viable candidate for experimental validation and subsequent development.
Abstract Uterine fibroids (UF) are among the most common benign tumors in women, yet their genetic underpinnings remain largely unexplored. Given the importance of genetic predisposition and the significance of GWAS-identified loci in UF, we aimed to investigate how family history of UF influences the association between these loci and the disease risk. DNA samples from 241 women with a family history of UF and 947 women without were genotyped for seven GWAS SNPs using probe-based PCR. In patients without a family history of UF, rs547025 SIRT3 was associated with a reduced risk (effect allele C, OR = 0.59, 95 % CI = 0.41–0.83, p = 0.002). Conversely, rs7986407 FOXO1 was linked to an increased risk of UF in those with a family history (effect allele G, OR = 1.94, 95 % CI = 1.03–3.68, p = 0.03). The most significant gene-environment interactions in patients with a family history were observed with pelvic inflammatory disease and GWAS SNPs, while in those without a family history, interactions additionally involved medical abortions. Our bioinformatic analysis uncovered the SNPs’ molecular mechanisms, contributing to UF development.
Stochastic model checking is a powerful verification technique used in engineering to assess system reliability and correctness. Many synthetic biological systems, including chemical reaction networks, can be modeled as stochastic processes, making stochastic model checking well suited for evaluating and improving their performance. However, direct application in synthetic biology faces domain-specific challenges that often require adapting existing analysis techniques and developing new algorithms that scale to biological complexity. To support this software development, we present a curated library of case studies representing biologically inspired stochastic models with unbounded state spaces. Each case study is provided in both SBML and PRISM formats to support accessibility and interoperability. By openly releasing the library and encouraging community contributions, this work aims to improve reproducibility, enable meaningful tool comparisons, and accelerate development of robust software infrastructure for stochastic model checking in synthetic biology.
