Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 415 records · Page 23Linked to original sources

bioRxiv2024

Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells

Although mitochondrial DNA (mtDNA) variants hold promise as endogenous barcodes for tracking human cell lineages, their efficacy as reliable lineage markers is hindered by the complex dynamics of mtDNA in somatic tissues. Here, we utilized computational modeling and single-cell genomics to thoroughly interrogate the origin and clonal dynamics of mtDNA lineage markers across various biological settings. Our findings revealed the majority of purported "clone informative variants (CIVs)" were pre-existing heteroplamies in the first cell instead of de novo somatic mutations during divisions. Moreover, CIVs demonstrated limited discriminatory power among different lineages during normal development; however, certain CIVs with consistently high allele frequencies proved capable of faithfully labeling cell lineages in scenarios of stringent clonal expansion. Inspired by our simulations, we introduced a lineage informative (LI) score, facilitating the identification of reliable mitochondrial lineage markers across different modalities of single-cell genomic data.

bioinformatics↗

bioRxiv2024

The phylogenetic position of the Yunxian cranium elucidates the origin of Dragon Man and the Denisovans

Diverse Middle Pleistocene forms of Homo coexisted in Africa, Europe, and Asia, and its controversial whether these fossil humans represent different species or clades. The [~]1 Ma old Yunxian 2 fossil from China is crucial for understanding the cladogenesis of Homo and the origin of Homo sapiens. Here, we restored and reconstructed the distorted Yunxian 2 cranium using new technology. The results show that this cranium displays mosaic features of plesiomorphy and apomorphy. Geometric-morphometric, phylogenetic and Bayesian tip-dating analyses including the reconstructed Yunxian 2 suggest that it is an early member of the Asian Homo longi clade, which probably includes the Denisovans, and is the sister group to the Homo sapiens clade. Both the H. sapiens and H. longi clades had deep roots extending beyond the Middle Pleistocene, and the basal position of the Yunxian fossil cranium suggests that it represents a population lying close to the last common ancestor of the two clades. One-Sentence SummaryThe newly-reconstructed Yunxian 2 cranium represents a basal member of the Homo longi and Denisovan clade, and probably lies close to the last common ancestor of that clade and the clade of H. sapiens.

evolutionary biology↗

bioRxiv2024

Unveiling Tissue Structure and Tumor Microenvironment from Spatially Resolved Transcriptomics by Hypergraph Learning

Spatial omics technologies have revolutionized life sciences by enabling the simultaneous acquisition of biomolecular and spatial information. Identifying spatial patterns is crucial for understanding organ development and tumor microenvironments. However, the emergence of diverse spatial omics resolutions in these technologies has made it challenging to accurately characterize spatial domains at finer resolutions. To address this, we propose HyperSTAR, a hypergraph-based method designed to precisely identify spatial domains across varying resolutions by leveraging higher-order relationships among spatially adjacent tissue programs. Specifically, a gene expression-guided hyperedge decomposition module is introduced to refine the hypergraph structure to accurately delineate spatial domains boundaries. Additionally, a hypergraph attention convolutional neural network is designed to adaptively learn the importance of each hyperedge, enhancing the models ability to capture complex higher-order relationships within spatially neighboring multi-spots and/or single cells. HyperSTAR outperforms existing graph neural network models in tasks such as uncovering tissue substructures, inferring spatiotemporal patterns, and denoising spatially resolved gene expressions. It effectively handles diverse spatial omics data types and scales seamlessly to large datasets. The method successfully reveals spatial heterogeneity in breast cancer sections, with findings validated through functional and survival analyses of independent clinical data. HyperSTAR represents a significant advancement in spatial omics analysis, representing a robust tool for exploring complex spatial patterns across varying resolutions and data types. Its ability to capture intricate higher-order relationships among spatially neighboring spots/cells makes it an invaluable tool for advancing research in life sciences, particularly in cancer and developmental biology.

bioinformatics↗

bioRxiv2024

Molecular basis for the host range function of the poxvirus PKR inhibitor E3

The antiviral protein kinase R (PKR) is activated by viral double-stranded RNA and phosphorylates translation initiation factor eIF2, thereby inhibiting translation and virus replication. Most poxviruses contain two PKR inhibitors, called E3 and K3 in vaccinia virus (VACV), which are determinants of viral host range. The prevailing model for E3 function is that it inhibits PKR through the non-specific sequestration of double-stranded (ds) RNA. Our data revealed that Syrian hamster PKR was resistant to E3, which is at odds with the sequestration model. However, Syrian hamster PKR was still sensitive to K3 inhibition. In contrast, Armenian hamster PKR showed opposite sensitivities, being sensitive to E3 and resistant to K3 inhibition. Mutational analyses of hamster PKRs showed that sensitivity to E3 inhibition was largely determined by the region linking the dsRNA-binding domains and the kinase domain of PKR, whereas two amino acid residues in the kinase domain (helix G) determined sensitivity to K3. Expression of PKRs in congenic cells showed that Syrian hamster PKR containing the two Armenian hamster PKR residues in helix-G was resistant to wild type VACV infection, and that cells expressing either hamster PKR recapitulated the phenotypes observed in species-derived cell lines. The observed resistance of Syrian hamster PKR to E3 explains its host range function and challenges the paradigm that dsRNA-binding PKR inhibitors mainly act by the sequestration of dsRNA. SignificanceThe molecular mechanisms that govern the host range of viruses are incompletely understood. A small number of poxvirus genes have been identified that influence the host range of poxviruses. We show that the host range functions of E3 and K3, two host range factors from vaccinia virus, are a result of species-specific interactions with the antiviral protein kinase R (PKR) and that PKR from closely related species displayed dramatic differences in their sensitivities to these viral inhibitors. While there is a substantial body of work demonstrating host-specific interactions with K3, the current model for E3-mediated PKR inhibition is that E3 non-specifically sequesters dsRNA to prevent PKR activation. This model does not predict species-specific sensitivity to E3; therefore, our data suggest that the current model is incomplete, and that dsRNA sequestration is not the primary mechanism for E3 activity.

microbiology↗

bioRxiv2024

Multi-tissue network analysis reveals the effect of JNK inhibition on dietary sucrose-induced metabolic dysfunction in rats

Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunctionLassociated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modelling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.

bioinformatics↗

bioRxiv2024

Linkage: an interactive shiny app and R package for linking of DNA regulatory peaks to genes

SummaryExisting studies have demonstrated that the integration analysis of transcriptomic and epigenomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, such investigations on large-scale sequencing data remain challenging for researchers or clinicians with limited bioinformatics knowledge. To facilitate the interpretation of gene regulatory landscape, we developed an R Shiny application and R package [Linking of atac-seq to gene expression data (Linkage)] for exploring and visualizing potential cis-regulatory elements (CREs) of genes based on ATAC-seq and RNA-seq data. Linkage offers six modules to systematically identify, annotate, and interpret potential gene regulatory elements from the whole genome step by step. Linkage can provide interactive visualization for the correlation between chromatin accessibility and gene expression. More than that, Linkage identifies transcription factors (TFs) that potentially drive the chromatin changes through identifying TF binding motifs within the CREs and constructing trans-regulatory networks of the target gene set. This powerful tool enables researchers to conduct extensive multiomics integration analysis and generate visually appealing visualizations that effectively highlight the relationship between genes and corresponding regulatory elements. With Linkage, users can obtain publishable results and gain deeper insights into the gene regulatory landscape. Availability and implementation Linkage is freely available as a Shiny web application (https://xulabgdpu.org.cn/linkage) and an R package (https://github.com/XuLab-GDPU/Linkage). The documentation is available at (https://aicplane.github.io/Linkage-tutorial/).

bioinformatics↗

bioRxiv2024

Fish CDK2 recruits Dtx4 to degrade TBK1 through ubiquitination in the antiviral response

Although the classical biological protein cell cycle protein kinase CDK2 has been extensively studied in higher vertebrates, its function in lower vertebrates beyond the regulation of mitosis remains unknown. In this study, we report a distinct mechanism whereby IFN expression is negatively regulated in fish by CDK2. After infection with the spring viremia of carp virus (SVCV), fish CDK2 expression significantly increased in tissues and cells. Moreover, antiviral resistance was improved in cdk2-/- homozygotes, and the antiviral cytokine interferon (IFN) expression was significantly higher. At the cellular level, CDK2 overexpression reduced IFN expression, while cdk2 knockdown increased the ability of cells to produce IFN. Subsequently, it was discovered that fish CDK2 binds and degrades TBK1, resulting in reduced IFN. CDK2 increases the K48-linked ubiquitination of TBK1, causing its degradation, while E3 ubiquitin ligase Dtx4 was found to be involved in this process following the significant enhancement of TBK1 K48-linked ubiquitination. Protein mass spectrometry and immunoblot analysis confirmed that the K567 site on TBK1 is essential for CDK2 to engage with Dtx4 and degrade TBK1; thus, after mutating the K567 site, K48-linked ubiquitination of TBK1 was not enhanced by Dtx4, and TBK1 was not degraded by CDK2. Our data demonstrate that fish CDK2 recruits the E3 ubiquitin ligase Dtx4 to target the K567 site of TBK1 and promote its degradation. These results suggest that CDK2 in lower vertebrates is implicated in a specialized role for antiviral innate immunity.

immunology↗

bioRxiv2024

A high-continuity and annotated reference genome of allotetraploid Siberian wildrye (Elymus sibiricus L., Poaceae: Triticeae)

Elymus sibiricus L. (Siberian wildrye, Es), a species belonging to the wheat tribe, is extensively employed as forage and for the reclamation of degraded grasslands within the Qinghai-Tibet Plateau (QTP). This study provides a high-quality reference genome assembly for the allotetraploid Es, which is composed of 14 pseudomolecules with the total genome size of 6.57 Gb. Our finding suggest that large-scale bursts of retrotransposons are critical for the genome expansion of Es. We discovered a translocation event between the Es4H and Es6H chromosomes with a low frequency of combination. Phylogenetic analyses of 90 Es accessions and 25 diploid accessions representing proposed ancestors from various habitats revealed the existence of four distinct populations. We further provided support for the hypothesis that the QTP is the center of origin and genetic diversity for Es. Collectively, our study offers valuable insights into the evolution of Es, as well as providing genomic resources for genetic enhancement in the Elymus genus and wheat tribe.

genomics↗

bioRxiv2024

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Single-cell and single-nucleus RNA sequencing (sc/snRNA-Seq) have become essential tools for profiling gene expression across different cell types in biomedical research. While factors like RNA integrity, cell count, and sequencing depth are known to influence data quality, quantitative benchmarks and actionable guidelines are lacking. This gap contributes to variability in study designs and inconsistencies in downstream analyses. In this study, we systematically evaluated quantitative precision and accuracy in expression measures across 23 sc/snRNA-Seq datasets comprising 3,682,576 cells from 339 samples. Precision was assessed using technical replicates based on pseudo-bulks created from subsampling. Accuracy was evaluated using sample-matched scRNA-Seq and pooled-cell RNA-Seq data of mononuclear phagocytes from four species. Our results show that precision and accuracy are generally low at the single-cell level, with reproducibility being strongly influenced by cell count and RNA quality. We establish data-driven thresholds for optimizing study design, recommending at least 500 cells per cell type per individual to achieve reliable quantification. Furthermore, we show that signal-to-noise ratio is a key metric for identifying reproducible differentially expressed genes. To support future research, we developed VICE (Variability In single-Cell gene Expressions), a tool that evaluates sc/snRNA-seq data quality and estimates the true positive rate of differential expression results based on sample size, observed noise levels, and expected effect size. These findings provide practical, evidence-based guidelines to enhance the reliability and reproducibility of sc/snRNA-seq studies.

genomics↗

bioRxiv2024

In vivo evaluation of thermally drawn biodegradable optical fibers as brain neural interfaces

Optical fiber technology has a critical role in modern neuroscience towards understanding the complex neuronal dynamics within the nervous system. In this study, we manufactured amorphous thermally drawn poly D, L-lactic acid (PDLLA) optical fibers in different diameters. These fibers were then implanted into the lateral posterior (LP) region of the mouse brain for 4 months, allowing us to assess their degradation characteristics. The gradual dissolution of the implanted PDLLA optical fibers in the brain was confirmed by optical microscope and scanning electron microscopy (SEM), molecular weight measurements, and light transmission spectroscopy. The results indicate that the degradation rate was mainly pronounced during the first week. Degradation after 4 months resulted in the formation of micropores on the surface of the implanted fiber within the gray matter region of the brain. Moreover, the current PDLLA optical fiber implant offers efficient light transmission in the short-wavelength near-infrared (SW-NIR) range. SW-NIR enables deeper tissue penetration and reduced light scattering, making it ideal for optogenetics and functional imaging with therapeutic potential in neurological disorders. We believe that the provided PDLLA optical fiber in this study constitutes a promising candidate for the development of next-generation biocompatible, soft, and biodegradable bi-directional neural interfaces

bioengineering↗

bioRxiv2024

Flow zoometry of Drosophila

Drosophila serves as a highly valuable model organism across numerous fields including genetics, immunology, neuroscience, cancer biology, and developmental biology. Central to Drosophila-based biological research is the ability to perform comprehensive genetic or chemical screens. However, this research is often limited by its dependence on laborious manual handling and analysis, making it prone to human error and difficult to discern statistically significant or rare events amid the noise of individual variations resulting from genetic and environmental factors. In this article we present flow zoometry, a whole-animal equivalent of flow cytometry for large-scale, individual-level, high-content screening of Drosophila. Our flow zoometer automatically clears the tissues of Drosophila melanogaster, captures three-dimensional (3D) multi-color fluorescence tomograms of single flies with single-cell volumetric resolution at an unprecedented throughput of over 1,000 animals within 48 hours (24 hr for clearing; 24 hr for imaging), and performs AI-enhanced data-driven analysis - a task that would traditionally take months or years with manual techniques. To demonstrate its broad applications, we employed the flow zoometer in various laborious screening assays, including those in toxicology, genotyping, and tumor screening. Flow zoometry represents a pivotal evolution in high-throughput screening technology: previously from molecules to cells, now from cells to whole animals. This advancement serves as a foundational platform for "statistical spatial biology", to improve empirical precision and enable serendipitous discoveries across various fields of biology.

bioengineering↗

bioRxiv2024

Biosynthesis of oxyresveratrol in mulberry (Morus alba L.) is mediated by a group of p-coumaroyl-CoA 2'-hydroxylases acting upstream of stilbene synthases

Mulberry (Morus alba L.) is considered a millenary medicinal plant and a food source for silkworms. Different M. alba extracts offer a variety of biological and pharmacological properties that are in part attributed to stilbenoids, a small group of phenylpropanoids that include resveratrol and oxyresveratrol. These are naturally present in non-renewable parts of mulberry trees, impeding their efficient extraction. As a way to bypass this spatiotemporal restriction, we generated cell suspensions from mulberry twigs and demonstrated that the combined use of methyl jasmonate and methyl- or hydroxypropyl-{beta}-cyclodextrins elicited a high production of resveratrol and oxyresveratrol, both intra and extracellularly. To identify oxyresveratrol-producing enzymes (unknown to date), we first improved the structural and functional annotation of the mulberry genome by integrating short and long-read sequencing data. We further combined this data with transcriptome, metabolite and proteome time-series evidence to identify a complete set of elicited phenylpropanoid- and stilbenoid-related genes. These included 22 stilbene synthase (STS) genes and a group of six p-coumaroyl-CoA 2-hydroxylases (C2Hs) that were highly co-expressed with resveratrol and oxyresveratrol accumulation. We transiently transformed Nicotiana benthamiana plants and grapevine (Vitis vinifera L.) cell suspensions to functionally validate the role of C2Hs as the first committed step of oxyresveratrol synthesis, providing an alternative substrate for STSs by hydroxylating p-coumaroyl-coA into 24-dihydroxycinnamoyl-CoA. We offer tools for genomic and transcriptomic exploration in the context of jasmonate elicitation aiding in the characterization of novel stilbenoid-modifying and regulatory genes in the Morus genus.

molecular biology↗

bioRxiv2024

A platform for mapping reactive cysteines within the immunopeptidome

The major histocompatibility complex class I (MHC-I) antigen presentation pathways play pivotal roles in orchestrating immune responses. Recent studies have begun to utilize cysteines within the immunopeptidome for therapeutic applications, such as using covalent ligands to create haptenated neoantigens for inducing an immune response. In this study, we report a platform for mapping reactive cysteines present on MHC-I-bound peptide antigens. We have developed cell-impermeable sulfonated maleimide probes capable of effectively capturing reactive cysteines on antigens. Utilizing these probes in chemoproteomic experiments, we discovered that cysteines on MHC-I-bound antigens exhibit various degrees of reactivity. Furthermore, interferon-gamma stimulation produces increased reactivity of cysteines at position 8 of 9-mer MHC-I-bound antigens. Our findings may open up new avenues for understanding the distinctive roles of cysteine within the MHC-I immunopeptidome and leveraging the differentially reactive cysteines for therapeutic intervention.

systems biology↗

bioRxiv2024

Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process

The immune system undergoes progressive functional remodeling from neonatal stages to old age. Therefore, understanding how aging shapes immune cell function is vital for precise treatment of patients at different life stages. Here, we constructed the first transcriptomic atlas of immune cells encompassing human lifespan, ranging from newborns to supercentenarians, and comprehensively examined gene expression signatures involving cell signaling, metabolism, differentiation, and functions in all cell types to investigate immune aging changes. By comparing immune cell composition among different age groups, HLA highly expressing NK cells and CD83 positive B cells were identified with high percentages exclusively in the teenager (Tg) group, whereas CD4_CTL precursors were exclusively enriched in the supercentenarian (Sc) group. Notably, we found that the biological age (BA) of pediatric COVID-19 patients with multisystem inflammatory syndrome accelerated aging according to their chronological age (CA). Besides, we proved that inflammatory shift-myeloid abundance and signature correlate with the progression of complications in Kawasaki disease (KD). Finally, based on those age-related immune cell compositions, we developed a novel BA prediction model, PHARE (https://xiazlab.org/phare/), which applies to both scRNA-seq and bulk RNA-seq data. Overall, our study revealed changes in immune cell proportions and function associated with aging, both in health and disease, and provided a novel tool for successfully capturing features that accelerate or delay aging.

immunology↗

bioRxiv2024

Mid-infrared photoacoustic brain imaging enabled by cascaded gas-filled hollow-core fiber lasers

SignificanceExtending the photoacoustic microscopy (PAM) into the mid-infrared (MIR) molecular fingerprint region constitutes a promising route towards label-free imaging of biological molecular structures. Realizing this objective requires a high-energy nano-second MIR laser source. However, existing MIR laser technologies are limited to either low pulse energy or free-space structure which is sensitive to environmental conditions. Fiber lasers are promising technologies for PAM for their potential of offering both high pulse energy and robust performance against environmental conditions. However, MIR high energy fiber laser has not yet been used for PAM because it is still at the infant research stage. AimWe aim to employ the emerging gas-filled anti-resonant hollow-core fiber (ARHCF) laser technology for MIR-PAM for the purpose of imaging myelin-rich regions in a mouse brain. ApproachThis laser source is developed with a [~]2.75 J high-pulse-energy nano-second laser at 3.4 m, targeting the main absorption band of myelin sheaths, the primary chemical component of axons in the central nervous system. The laser mechanism relies on two-orders gas-induced vibrational stimulated Raman scattering (SRS) for nonlinear wavelength conversion, starting from a 1060 nm pump laser to 1409 nm through the 1st order Stokes generation in the nitrogen-filled 1st stage ARHCF, then, from 1409 nm to 3.4 m through the 2nd stage hydrogen-filled ARHCF. ResultsThe developed Raman laser was used for the first time for transmission-mode MIR-PAM of mouse brain regions containing rich myelin structures. ConclusionsThis work pioneers the potential use of high-energy and nano-second gas-filled ARHCF laser source to MIR-PAM, with a first attempt to report this kind of fiber laser source for PAM of lipid-rich myelin regions in a mouse brain. The proposed ARHCF laser technology is also expected to generate high-energy pulses at the ultraviolet (UV) region, which can significantly improve the lateral resolution of the PAM.

neuroscience↗

bioRxiv2024

Efficient differentiation of human retinal pigment epithelium cells from chemically induced pluripotent stem cells

Human induced pluripotent stem cells (hiPSCs) hold considerable promise for autologous cellular therapies, particularly in ocular disease treatment, because of the low numbers of cells required for transplantation and the non-invasive nature of graft monitoring. However, the use of hiPSCs in ocular disease treatment faces challenges because the production of clinical-grade autologous hiPSCs via genetic techniques remains expensive, time-consuming, and subject to safety concerns. Here, we utilize a recently reported chemical method to derive human chemically induced pluripotent stem cells (hCiPSCs), demonstrating that hiPSC lines can be generated using small molecules in a simple and robust manner. Moreover, we show that these cell lines can be efficiently differentiated into retinal pigment epithelium (RPE) cells, which may aid in the treatment of age-related macular degeneration (AMD). Our study provides a foundation for cost-effective, fast, and safe methods that enable efficient production of autologous retinal cell types for ocular disease treatment.

cell biology↗

bioRxiv2024

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted [~]15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

bioinformatics↗

bioRxiv2024

Physics-informed machine learning for automatic model reduction in chemical reaction networks

Physics-informed machine learning emerges as a transformative approach, bridging the gap between the high fidelity of mechanistic models and the adaptive, data-driven insights afforded by artificial intelligence and machine learning. In the realm of chemical reaction network modeling, this synergy is particularly valuable. It offers a solution to the pro-hibitive computational costs associated with detailed mechanistic models, while also capitalizing on the predictive power and flexibility of machine learning algorithms. This study exemplifies this innovative fusion by applying it to the critical biomedical challenge of A{beta} fibril aggregation, shedding light on the mechanisms underlying Alzheimers disease. A corner-stone of this research is the introduction of an automatic reaction order model reduction framework, tailored to optimize the scale of reduced order kinetic models. This framework is not merely a technical enhancement; it represents a paradigm shift in how models are constructed and refined. By automatically determining the most appropriate level of detail for modeling reaction networks, our proposed approach significantly enhances the efficiency and accuracy of simulations. This is particularly crucial for systems like A{beta} aggregation, where the precise characterization of nucleation and growth kinetics can provide insights into potential therapeutic targets. The potential generalizability of this automatic model reduction technique to other network models is a key highlight of this study. The methodology developed here has far-reaching implications, offering a scalable and adaptable tool for a wide range of applications beyond biomedical research. The ability to dynamically adjust model complexity in response to the specific demands of the system under study is a powerful asset. This flexibility ensures that the models remain both computationally feasible and scientifically relevant, capable of accommodating new data and evolving understandings of complex phenomena.

biochemistry↗