搜索结果：Interdisciplinary sciences, computational life sciences

Over the past two decades, the Student Council Symposium (SCS), the flagship event of the ISCB Student Council, has grown into a vital forum for early-career researchers in computational biology. Since its inception in 2005, the SCS has served as a platform for scientific exchange, skill development, and community building in a student-led, globally inclusive environment. The 20th edition, held in 2024 in Montréal, Canada, continued the symposium's tradition of global engagement and hybrid accessibility, reaffirming a commitment to in-person dialogue. This article presents a comprehensive retrospective of the evolution of computational biology through the lens of SCS. We trace key advances from genome-scale analyses and structural modeling to single-cell and AI-driven bioinformatics. Based on SCS2024 talks and keynotes, we illustrate how emerging interdisciplinary methods have reshaped the field. We also highlight parallel efforts in global education, regional expansion, and equity, diversity, and inclusion initiatives. This retrospective shows how SCS has not only reflected the transformation of the field but also played a key role in shaping emerging leaders in bioinformatics.

This work presents a decoupled framework for multistep computational chemistry automation built on OpenClaw. OpenClaw serves as the general-purpose agent for task coordination and supervision. Planning skills externalize task descriptions into executable task specifications, domain skills provide computational chemistry procedures, and the DPDispatcher skill grounds computation in heterogeneous HPC environments. In a methane oxidation reactive MD case study, the framework coordinated cross-tool execution, supported bounded recovery from runtime failures, and extracted reaction networks.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

Generative artificial intelligence (AI) is becoming a groundbreaking paradigm in the field of animal genomics and is providing the possibility to take a step towards intelligent agriculture, with better data integration, predictive modeling, and biological design. This review focuses on the shift from predictive to generative modelling paradigms, examining their implications for data synthesis, biological sequence design, and integrative smart livestock systems. It provides a comprehensive overview of recent developments, applications, and challenges at the intersection of generative AI and animal genomics, as well as future directions. In doing so, it sheds light on novel opportunities and constraints specific to livestock genomics that are not adequately addressed in broader AI or human genomics studies. Thus, it bridges the gap between computational innovations and biological constraints. It initially sets the conceptual background in place by looking at the development of smart agriculture, the essentiality of animal genomics, and the development of generative model architectures in life sciences, as well as fundamental methodological aspects, including livestock genomic and multi-omics data peculiarities and the representation of biological sequences. The review then comprehensively discusses a wide range of applications such as genomic data augmentation, prediction of new genetic variants, design of protein and gene sequences, augmentation of genomic selection and trait prediction, regulatory and epigenomic modeling, accurate breeding and reproductive technologies, and cross-species genomic modeling, illustrating how generative AI is transforming genomics into something generative, enhanced through simulation. It is discussed in terms of integration into systems of smart agriculture, connections with precision livestock farming, digital twin, genomics-to-management pipelines, and sustainability-focused systems of decision-making, where the adaptive, individualized, and system-level optimization can be applied. Critical analysis of major challenges and limitations, such as heterogeneity and scarcity of data, model bias and generalization, computational and resource limitations, validation and interpretability issues, and ethical, legal, and social constraints that drove the responsible deployment are also critically reviewed. Lastly, the future opportunities are discussed, which should center on generative genome engineering, multimodal and federated modeling, species preservation, real-time interaction with smart farming technologies, and the creation of responsible and ethical AI frameworks. Overall, it is possible to state that this review makes generative AI a base technology of the new generation of animal genomics and smart agriculture, but it highlights that interdisciplinary cooperation, stringent validation, and alignment with the notions of sustainability, animal welfare, or values are necessary to realize its capabilities.

Identifying protein-protein interaction (PPI) sites is crucial for predicting protein function, uncovering disease mechanisms, and designing drugs. Experimental methods for PPI site identification are often costly and time-consuming, necessitating the development of efficient computational approaches. However, existing methods still face significant challenges in balancing high accuracy with computational efficiency. To address these limitations, we propose ProtFormer-Site, a novel PPI site prediction framework that integrates large protein language models (ESM2 and SaProt) with a parameter-efficient fine-tuning strategy (LoRA). We introduce a specialized ProtFormer backbone featuring a recycling mechanism to iteratively refine residue-level interaction features. The framework includes two variants: a sequence-only model and a structure-enhanced model, catering to different data availabilities. ProtFormer-Site demonstrated outstanding performance on three benchmark datasets, achieving Matthews correlation coefficient (MCC) improvements ranging from 22.4% to 61.5% compared to state-of-the-art methods. Furthermore, ProtFormer-Site demonstrates exceptional scalability, maintaining significantly lower log-transformed inference times across varying sequence lengths compared to state-of-the-art methods. Its computational efficiency makes it uniquely suited for large-scale, high-throughput prediction tasks. These results indicate that ProtFormer-Site offers a robust, accurate, and computationally efficient solution for PPI site prediction.

RNA-binding proteins (RBPs) play critical roles in the regulation of gene expression. Recent studies have begun to detail the RNA recognition mechanisms of diverse RBPs. However, given the array of RBPs studied so far, it is implausible to experimentally profile RBP-binding peaks for hundreds of RBPs in multiple non-model organisms. Here, we introduce MuSIC (Multi-Species RBP-RNA Interactions using Conservation), a deep learning-based framework for predicting cross-species RBP-RNA interactions by leveraging label smoothing and evolutionary conservation of RBPs across 11 phylogenetically diverse species ranging from human to yeast. MuSIC outperforms state-of-the-art computational methods, and achieves highly accurate prediction of RBP-binding peaks across species. The prediction confidence is higher in the metazoan species, partially reflecting differences in RBP conservation patterns. Finally, the effects of homologous genetic variants on RBP binding can be computationally quantified across species, followed by experimental validations. The target transcripts with disrupted binding events are enriched in the ubiquitination-associated pathways. To summarize, MuSIC provides a useful computational framework for predicting RBP-RNA interactions cross-species and quantifying the effects of genetic variants on RBP binding, offering insights into the RBP-mediated regulatory mechanisms implicated in human diseases.

Acute infectious diseases, particularly lots of neglected tropical diseases (NTDs), pose significant public health challenges, especially in resource-limited settings where diagnostic and surveillance capacities are often inadequate. This scoping review systematically explores methodologies for estimating the burden of acute infectious NTDs, focusing on metrics such as incidence, mortality, and disability-adjusted life years (DALYs). We identified 60 studies, predominantly on malaria and dengue, with a growing emphasis on advanced computational approaches like machine learning and Bayesian geospatial modeling. Key findings highlight the evolution from traditional surveillance-based methods to integrated frameworks incorporating environmental, demographic, and health system covariates. However, challenges persist, including data sparsity, underreporting, and methodological uncertainties. The review underscores the need for improved data integration, standardized frameworks, and interdisciplinary collaboration to enhance the accuracy and utility of burden estimates.

Phylogenetic networks represent complex biological scenarios that are overlooked in trees, such as hybridization and horizontal gene transfer. Although numerous methods have been developed for phylogenetic network inference, their scalability is severely limited by the computational demands of likelihood optimization and the vastness of network space. Composite (or pseudo-) likelihood approaches like SNaQ have improved computational tractability for network inference, but they remain inadequate for datasets of sizes routinely handled by tree inference methods. Here, we introduce SNaQ.jl, a new standalone Julia package with the composite likelihood inference originally implemented within PhyloNetworks.jl as well as new scalability features that enhance computational efficiency through (i) parallelization of quartet likelihood calculations during composite likelihood computation, (ii) weighted random selection of quartets, and (iii) probabilistic decision-making during network search. Through a simulation study and empirical data analysis, we show that this new version of SNaQ.jl (version 1.1) improves average runtimes by up to 499% on average with no change in function parameters or method accuracy. SNaQ.jl is a new open source Julia package available at https://github.com/JuliaPhylo/SNaQ.jl.

Lysine crotonylation (Kcr), as an emerging post-translational modification, plays a crucial role in core life activities such as chromatin dynamics and gene expression. To address the current limitations of Kcr site detection techniques, including high experimental costs, complex procedures, and high false-positive rates, as well as the poor generalization performance of existing computational models caused by limited training data and class imbalance, this study proposes an innovative intelligent recognition framework named MVFAN-Kcr. The system integrates multi-view feature fusion and attention mechanisms to synergistically enhance the prediction accuracy and robustness of Kcr site identification. Physicochemical property features are combined with global sequence semantic information derived from the ESM-2 protein language model to construct a fused feature representation that captures both local physicochemical information and global contextual information. To optimize computational efficiency, feature selection is performed using analysis of variance. To effectively address the problem of imbalanced data distribution, a stratified undersampling strategy based on the chi-square test is developed. In addition, a convolutional neural network combined with attention is designed to efficiently extract local sequence patterns and enhance the representation of key features. Under a rigorous evaluation framework based on five-fold cross-validation and an independent test set, MVFAN-Kcr demonstrates excellent predictive performance. The method significantly outperforms baseline approaches in terms of accuracy, achieving an ACC value of 79.18%, while the area under the ROC curve reaches 0.8618. SHAP analysis and gradient-based analysis reveal a strong dependence on pKa-related properties, charge characteristics, and locally enriched small side-chain residues, and indicate that the model can nonlinearly integrate high-order sequence semantic information for effective identification of Kcr sites. Overall, MVFAN-Kcr combines data balancing strategies, multi-view feature fusion, and attention mechanisms to achieve high accuracy, robustness, and interpretability, providing an effective tool for protein Kcr site prediction. The data and code are available on https://github.com/Lilyjoys/MVFAN-Kcr , and a free web platform is provided at http://www.mvfan-kcr.com .

Language evolution in the Gansu-Qinghai (GQ) region provides a key perspective for understanding cultural development along the eastern Silk Road. Previous genetic and archaeological studies have revealed complex, multi-ethnic interactions in this region, shaped by migration and sociocultural exchange. However, the lack of structured linguistic data and computational tools for studying GQ language contacts has limited rigorous analysis of sociocultural evolution. Here, we presented a new hybrid dataset of phonological and morpho-syntactic features from languages sampled across the GQ region. We introduced a computational framework to assess language contact and admixture, allowing us to quantify interaction among GQ languages and trace their origins. Our results showed that GQ languages exhibit distinct contact patterns in their phonological and morpho-syntactic systems, with some languages displaying clear evidence of mixture. Using a new statistical method, Trait Sharing Among Languages (TSAL), based on tree topology, we identified significant influences from Sinitic, Tibetan, Mongolic, and Turkic languages in shaping the GQ linguistic diversity. These findings highlight the GQ region as a linguistic convergence zone on the eastern Silk Road, providing a foundation for quantitative research on language contact and admixture. Our work enhances the linguistic perspective on cultural evolution in the GQ region and supports future interdisciplinary studies that integrate languages, genes, and material cultures.

This study presents a comprehensive bibliometric analysis of zebrafish (Danio rerio) research published between 2000 and 2025, based on data retrieved from the Web of Science Core Collection and analyzed using Clarivate's InCites platform. A total of 74,675 records were examined to uncover trends in publication volume, geographical and institutional distribution, international collaborations, disciplinary coverage, and thematic evolution over time. The results indicate a steady growth in zebrafish-related publications, particularly between 2000 and 2021, followed by a relative plateau. The United States and China lead in research output, with China showing rapid growth over the last decade. Collaboration networks remain dominated by a limited number of high-capacity countries, while many others, particularly those with limited infrastructure, remain underrepresented. The Cooperation in Science and Technology Member Countries also showed a noticeable decline in publication numbers following a 2021 peak. Thematic keyword analysis revealed a clear shift from early developmental biology themes-such as hindbrain and retinal development-toward emerging topics such as regeneration, oxidative stress, and toxicology. However, the findings suggest that this thematic diversification has not yet translated into widespread interdisciplinary integration. Zebrafish research remains largely anchored within classical biological disciplines, despite its increasing relevance to fields such as neuroscience, environmental health, pharmacology, and biomedical engineering. This mismatch between thematic scope and interdisciplinary adoption represents a potentially missed opportunity-especially in addressing complex global challenges. Strengthening cross-disciplinary collaborations and promoting the adoption of zebrafish in innovative, technology-driven research contexts may help unlock the full strategic potential of this versatile model organism.

Enteric infectious diseases claim more than 1 million lives annually and are among the top ten causes of death in children younger than 5 years. Remarkable global investment has been dedicated to enteric infectious disease prevention and control; however, the shifting global health landscape is testing the continuance of progress. To evaluate the current status and guide future interventions, we present the latest epidemiological estimates of enteric infectious diseases from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 and assess progress towards the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) mortality target of fewer than 20 deaths per 100 000 children younger than 5 years by 2025. We quantified the incidence, mortality, and disability-adjusted life-years (DALYs) of enteric infectious diseases by age, sex, and year across 204 countries and territories from 1990 to 2023. In GBD 2023, the following were considered under the category of enteric infectious diseases: diarrhoeal diseases, enteric fever (typhoid and paratyphoid), invasive non-typhoidal Salmonella spp (iNTS) infections, and other intestinal infectious diseases. We also examined 15 aetiologies contributing to diarrhoeal diseases. Incidence and prevalence were estimated with DisMod-MR (version 2.1), a Bayesian meta-regression tool, drawing on data from systematic reviews, population-based surveys, claims data, and hospital sources. Cause-specific mortality was modelled with Cause of Death Ensemble Modelling based on data from sources including vital registration, mortality surveillance, verbal autopsy, and minimally invasive tissue sampling. Years of life lost and years lived with disability were computed and combined to derive DALYs. For aetiology-specific estimation, population-attributable fractions (PAFs) for 15 pathogens were derived with a counterfactual framework. Point estimates and 95% uncertainty intervals (UIs) were generated from 250 draws from the posterior distribution. In 2023, enteric infectious diseases resulted in an estimated 1·27 million (95% UI 0·963-1·68) deaths globally, declining from 3·69 million (3·04-4·56) in 1990. The global age-standardised mortality rate (ASMR) decreased from 74·1 (62·0-92·9) per 100 000 population to 16·4 (12·6-21·3) per 100 000 population during the same period. Diarrhoeal diseases accounted for most deaths in 2023 (1·11 million [0·811-1·54]), followed by enteric fever and iNTS. South Asia and sub-Saharan Africa remained the most affected regions in 2023, with 599 000 (441 000-882 000) and 501 000 (373 000-648 000) deaths due to enteric infectious diseases, respectively, predominantly from diarrhoeal disease. Rotavirus was the leading cause of all-age diarrhoeal disease deaths (PAF 16·3% [12·0-21·5]), followed by norovirus (10·2% [2·4-17·0]) and Shigella spp (9·3% [5·4-15·2]). Among children younger than 5 years, PAFs of deaths due to diarrhoeal diseases were 40·2% (32·5-48·5) for rotavirus, 24·0% (15·1-36·7) for Shigella spp, and 23·4% (13·7-34·3) for adenovirus. Across 204 countries and territories, 141 met the GAPPD mortality target in 2023. The driving aetiologies among countries that did not meet the target in 2023 varied slightly by GBD super-region, but the highest or second-highest number of deaths in children younger than 5 years were consistently attributed to rotavirus. Astrovirus and sapovirus, newly included in GBD 2023, were responsible for 24 600 (6290-49 000) and 18 800 (4650-44 400) deaths, respectively, in 2023, mainly in children younger than 5 years. Our findings show that mortality and ASMRs of enteric infectious diseases declined substantially between 1990 and 2023. This decline is consistent with the expansion of public health measures and broader socioeconomic development. However, the burden in 2023 remains considerably high, with the highest mortality concentrated in sub-Saharan Africa and south Asia. Considering that more than a quarter of all countries had yet to meet the GAPPD mortality target in 2023, sustained efforts are needed to address the persistent burden in affected countries and to adapt to the changing global health landscape. Gates Foundation.

Foodborne diseases are important causes of illness and death. The first estimates of their burden were published by WHO in 2015. We updated WHO estimates of the global, regional, subregional, and national foodborne disease burden caused by 42 infectious and chemical hazards in 2021, including time trends for 2000-21. We provide a high-level summary of foodborne disease burden, expressed as incidence, deaths, and disability-adjusted life-years (DALYs). Data for burden estimation were provided from a WHO-commissioned series of systematic reviews on the incidence, aetiology, sequelae, and case fatality or mortality of the hazards. Data were analysed using hierarchical meta-regression modelling with geographical clustering and a global linear time trend, disease-specific computational models, and uncertainty propagation through Monte Carlo simulations to calculate 95% uncertainty intervals. Attribution to foodborne transmission was principally based on a structured expert judgement process. Economic impact was measured as lost productivity. For 2021, foodborne transmission of the 42 hazards caused 866 million (95% uncertainty interval 680-1090) illnesses, 1·52 million (0·783-2·51) deaths, and 57·1 million (39·4-81·1) DALYs. Inorganic arsenic, lead, and non-typhoidal Salmonella enterica (diarrhoeal and invasive disease) resulted in the most DALYs. The greatest burden of foodborne disease was in the African and South-East Asia regions. The incidence in children younger than 5 years was 2·7 times higher than in people aged 5 years or older, resulting in 4·3 times the rate of DALYs. The total burden from all hazards decreased over time. In 2021, these 42 hazards resulted in productivity losses of US$310 billion in nominal terms, and US$647 billion after adjusting for purchasing power parity. Foodborne diseases causes a burden similar to that from tuberculosis, HIV and AIDS, or malaria. The high burden of both communicable and non-communicable foodborne diseases requires countries to prioritise developing strategies to improve the safety of the food supply. WHO.

Over 10 years ago, WHO estimates of hazard-specific foodborne disease burdens showed that parasites exert considerable health burdens globally. We updated these estimates, focusing on 14 invasive parasitic diseases. Incidences, deaths, and disability-adjusted life-year (DALY) burdens were estimated for each parasitic disease from 2000 to 2021, using data from systematic reviews and the Global Burden of Diseases, Injuries, and Risk Factors Study. For some diseases, structured expert judgement was used to estimate proportions of foodborne infection. Data were pooled via hierarchical meta-regression models with uncertainty propagated through Monte Carlo simulations following disease-specific computational models defined by incidence rates and probability parameters. We estimated that 277 million illnesses were caused by potentially foodborne invasive parasites, with approximately 171 million attributable to foodborne transmission. Considerable heterogeneity by parasite, in magnitude and uncertainty, was observed. Of 4·89 million foodborne DALYs associated with these diseases, highest contributions were from Taenia solium (1·3 million) and Clonorchis sinensis (0·921 million), both also associated with most foodborne deaths. Burden was greatest in the region of the Americas, predominantly due to Chagas disease, followed by the African region, where neurocysticercosis-associated epilepsy caused most burden. Burdens decreased globally from 2000 to 2021, except in the Western Pacific region, where the burden, predominantly associated with clonorchiasis, is rising. Foodborne parasitoses cause considerable suffering, with some populations and regions particularly at risk. These data provide a baseline by which effects of interventions can be assessed and emphasis directed to those parasites exerting the greatest burden. WHO.

Background/Objectives: Precision nutrition is moving beyond population-based guidance and isolated gene-diet interactions toward integrative models of dietary response. However, current approaches remain fragmented across nutrigenomics, microbiome research, multi-omics profiling, digital health, and machine learning. This review proposes the Nutri-Exposome Intelligence Framework as a conceptual, data science-driven model for integrating cumulative dietary, environmental, microbial, molecular, clinical, and digital exposures for precision chronic disease prevention. Methods: This conceptual review synthesizes the literature on precision nutrition, nutrigenetics, nutrigenomics, exposomics, gut microbiome research, multi-omics integration, wearable and biomarker-based monitoring, and machine learning in nutrition studies. Evidence was organized into a framework linking exposure assessment, host susceptibility, microbiome-mediated biotransformation, molecular response profiling, computational modelling, personalized intervention, and longitudinal feedback. Results: The proposed framework consists of seven interconnected layers: diet, environment, and lifestyle exposures; host genome and microbiome; multi-omics molecular responses; machine learning-based integration; risk prediction and responder stratification; personalized dietary intervention; and wearable and biomarker-based feedback. It positions the nutri-exposome as a cumulative exposure-response system and highlights how machine learning can support data harmonization, feature engineering, predictive modelling, responder classification, explainable interpretation, and adaptive refinement of dietary recommendations. Key applications include obesity, type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatotic liver disease, cardiovascular-kidney-metabolic syndrome, and broader cardiometabolic prevention. Conclusions: Nutri-exposome intelligence offers a structured pathway for transforming complex nutrition data into predictive, explainable, and adaptive precision nutrition strategies. Implementation will require longitudinal and multi-ethnic cohorts, standardized metadata, causal validation, interpretable machine learning, ethical governance, and equitable access to support responsible clinical and public health translation globally.

Predicting drug-target affinity (DTA) plays a pivotal role in drug discovery and repurposing. While existing computational approaches predominantly rely on 1D sequences or 2D structural data, they often fail to fully capture the intricate nature of molecular interactions. To address this limitation, we propose Deep3D-DTA, a novel tri-modal deep learning framework that integrates 1D sequence semantics, 2D graph topology, and 3D spatial geometry complementary representations for both drugs and target proteins. The proposed architecture offers three key advancements: First, it employs a pre-trained protein language model to encode amino acid sequences, effectively capturing long-range sequential dependencies. Second, it constructs precise 3D structural representations by computing interatomic distances and bond angles, enabling accurate modeling of the spatial conformations of both proteins and compounds. Third, it leverages a hybrid feature extraction module that combines graph neural networks with multi-head attention mechanisms to learn hierarchical structural patterns. Extensive experiments on three widely used benchmark datasets (Davis, KIBA, and Metz) demonstrate that Deep3D-DTA significantly outperforms state-of-the-art methods in DTA prediction. These results highlight its potential as a robust and reliable computational tool for accelerating drug discovery and reducing development costs through more accurate affinity prediction.

Viruses represent a major threat to human health, while simultaneously exhibiting great potential in a wide range of applications, from virus-inspired devices to therapeutic delivery agents. Addressing virus-related questions from an interdisciplinary standpoint promises to open new avenues, both in the fight against viral diseases and in the exploitation of viral structures to advance technology. This has stimulated the development of 'physical virology', a growing research field gathering researchers from various scientific disciplines with a common interest in viruses. The FEBS|EMBO Lecture course on Physical Virology brought together top researchers working with viruses to inspire and further educate a new generation of transdisciplinary virus-oriented scientists and to cement the growing physical virology community.

The encounters between transcription and DNA replication may remodel replication dynamics, yet the coordination of these two essential processes remains elusive. Here, we developed a replication-associated Micro-C (Repli-MiC) method to map replication fountains, which are dynamic chromatin-interaction structures induced by coupled replication forks, at nucleosome resolution in mammalian cells. We implemented a reinforcement-learning-based computational framework to enable unbiased and quantitative characterization of replication fountains, thereby allowing precise assessment of how transcription influences sister-fork elongation. With this integrated platform, we found that co-directional transcription induces a bias in the speed of sister replication forks toward the transcriptional orientation without compromising fork coupling, which is further enhanced upon depletion of DNA topoisomerase I (TOP1). Conversely, head-on transcription potentially impairs fork elongation to weaken replication fountains. This study provides a comprehensive assay for profiling the entire DNA-replication elongation process and sheds light on the dual roles of transcription in modulating fork elongation.

Neuromorphic ionic computing is inspired by the brain's use of ions for ultralow-energy computation-its massive parallelism, adaptability, and learning capabilities. This emerging paradigm can overcome limitations of conventional silicon-based computing by enabling colocated memory and processing, multicarrier information streams, and massive three-dimensional connectivity. However, substantial knowledge gaps remain in understanding and engineering ionic transport, energy dissipation, materials design, and scalable device architectures. This Review explores these critical challenges across seven key domains, highlighting the need for new theoretical approaches, materials, device concepts, and fabrication strategies. We argue that advancing ionic neuromorphic systems requires an interdisciplinary approach, integrating insights from biology and neuroscience, nanofluidics, materials science, and systems engineering to enable a new class of energy-efficient, robust, and reconfigurable computing technologies.