NPJ Systems Biology and Applications最新文献

High Wnt and low Notch activities characterize epidermal stem cells (SCs), while low Wnt and high Notch activities characterize the terminally differentiated epidermal cells (TDCs). However, the mechanism by which transit amplifying cells (TACs) are induced to become terminally differentiated remains unclear. Our analysis suggests that oscillations in Wnt, Notch, and YAP/TAZ activities lead to the production of TDCs from TACs. Furthermore, the role of stem cell markers in epidermal differentiation, regeneration, and the functional aspects of the epidermis remains unclear. Here, based on the ability of the epidermal SCs to induce the differentiation of TACs, we characterize the SCs as having the expression of Notch ligand, Delta, higher than a critical value. Further, we have functionally defined the critical value of the Delta expression by SCs. Our paper may have general implications for the stemness and differentiation of other tissues.

Boolean networks provide robust, explainable, and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes. Yet, the construction of such models is extremely challenging, as it requires integrating prior knowledge with experimental observation of the transcriptome, potentially relating thousands of genes. We present a general methodology for integrating transcriptome data and prior knowledge on the underlying gene regulatory network in order to generate automatically ensembles of Boolean networks able to reproduce the modeled qualitative behavior. Our methodology builds on the software BoNesis, which implements the automatic construction of Boolean networks from a specification of their expected structural and dynamical properties. We show how to transform transcriptome data into such a qualitative specification, and then how to exploit the generated ensembles of Boolean networks for identifying families of candidate models, and for predicting robust cellular reprogramming targets. We illustrate the scalability and versatility of our overall approach with two applications: the modeling of hematopoiesis from single-cell RNA-Seq data, and modeling the differentiation of bone marrow stromal cells into adipocytes and osteoblasts from bulk RNA-seq time series data. For this latter case, we took advantage of ensemble modeling to predict combinations of reprogramming factors for trans-differentiation that are robust to model uncertainties due to variations in experimental replicates and choice of binarization method. Moreover, we performed an in silico assessment of the fidelity and efficiency of the reprogramming and conducted preliminary experimental validation.

Transthyretin amyloidosis (ATTR) is a genetically diverse disorder caused by destabilising mutations in the transthyretin (TTR) protein, leading to pathological aggregation. While stabilisers like tafamidis and acoramidis are approved, their efficacy across TTR variants remains unclear. This study presents an in silico pipeline combining AlphaFold3 for structure prediction, ESM2 for sequence embeddings, DiffDock-L and AutoDock Vina for molecular docking, and DiffSBDD for ligand generation. Simulations show that binding affinities of approved ligands vary significantly among TTR variants, with some mutations (e.g., W61L, Y98F) reducing binding despite being distant from the binding site. Embedding-based clustering highlights potential benign mutations and supports scalable variant classification. Additionally, customised ligand optimisation can recover binding affinity in specific cases, though effects are mutation-dependent. These findings emphasise the need for variant-aware therapeutic strategies. This integrative approach offers a foundation for precision drug design in ATTR, enabling the development of personalised stabilisers tailored to individual mutational profiles.

Physical inactivity and low fitness have become global health concerns. Metabolomics, as an integrative approach, may link fitness to molecular changes. In this study, we analyzed blood metabolomes from elderly individuals under different treatments. By defining two fitness groups and their corresponding metabolite profiles, we applied several machine learning classifiers to identify key metabolite biomarkers. Aspartate consistently emerged as a dominant fitness marker. We further defined a body activity index (BAI) and analyzed two cohorts with high and low BAI using COVRECON, a novel method for metabolic network interaction analysis. COVRECON identifies causal molecular dynamics in multiomics data. Aspartate-amino-transferase (AST) was among the dominant processes distinguishing the groups. Routine blood tests confirmed significant differences in AST and ALT. Aspartate is also a known biomarker in dementia, related to physical fitness. In summary, we combine machine learning and COVRECON to identify metabolic biomarkers and molecular dynamics supporting active aging.

Elranatamab, an approved bispecific antibody (BsAb) for relapsed/refractory multiple myeloma, forms an immune synapse between the T-cell CD3 marker and B-cell maturation antigen (BCMA) on myeloma cells. Circulating soluble BCMA (sBCMA) is associated with disease burden and may reduce drug exposure, impacting efficacy. A quantitative systems pharmacology model that captures elranatamab's mechanism of action and disease dynamics was developed and calibrated to clinical datasets. Simulations explored model uncertainty and inter-patient variability with respect to biological, pharmacologic, and tumor-related components to inform clinical dose-response relationships and evaluate the effect of baseline sBCMA levels on dose and regimen. Model simulations supported 76 mg weekly as the optimal regimen, including in patients with high sBCMA. A left shift in the dose-response curve among virtual responders supported maintenance of efficacy with less frequent dosing. This work exemplifies how mechanistic models may support BsAb dose and regimen justification within the framework of model-informed drug development.

Universal Differential Equations (UDEs) combine mechanistic differential equations with data-driven artificial neural networks, forming a flexible framework for modelling complex biological systems. This hybrid approach leverages prior knowledge and data to uncover unknown processes and deliver accurate predictions. However, UDEs face challenges in efficient and reliable training due to stiff dynamics and noisy, sparse data common in biology, and in ensuring the interpretability of the parameters of the mechanistic model. We investigate these challenges and evaluate UDE performance on realistic biological scenarios, providing a systematic training pipeline. Our results demonstrate the versatility of UDEs in systems biology and reveal that noise and limited data significantly degrade performance, but regularisation can improve accuracy and interpretability. By addressing key challenges and offering practical solutions, this work advances UDE methodology and underscores its potential in tackling complex problems in systems biology.

Understanding diseases as the result of continuous transitions from a healthy system is more realistic than understanding them as discrete states. Here, we designed the spectrum formation approach (SFA), a machine learning-based method that extracts key features contributing to disease state continuity. We applied the SFA to transcriptomic data from patients with progressive liver disease and neurodegenerative movement disorders to examine its effectiveness in identifying biologically relevant gene sets. The SFA identified transcription factors that potentially regulate liver inflammation and voluntary movement. In neurodegenerative disorders, the SFA also identified genes regulated by ETS-1, with unclear effects on movement. In functional assessment using human iPSC-derived neurons, ETS-1 overexpression disrupted dopamine receptor balance, reduced GABA-producing enzyme levels, and promoted cell death. These findings suggest that the SFA enables the discovery of regulatory factors capable of modifying disease states and provides a framework for the continuity-based interpretation of biological systems.

We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this approach, the model captures the transduction of environmental cues into biological responses directly from experimental observations, without explicitly predefining cell behavior. This enables the prediction of dynamic, environment-dependent cell behavior and offers a scalable and flexible alternative to traditional rule-based ABM. To illustrate its potential, we present an application to barotactic cell migration data from microfluidic device experiments, where cells adapt their migration behavior based on pressure gradients, demonstrating the model's ability to generalize across varying geometries and pressure configurations. Thus, this approach introduces a novel direction for modeling how cells sense and transduce environmental cues into biological behaviors.

While cancer has traditionally been considered a genetic disease, mounting evidence indicates an important role for non-genetic (epigenetic) mechanisms. Common anti-cancer drugs have recently been observed to induce the adoption of non-genetic drug-tolerant cell states, thereby accelerating the evolution of drug resistance. This confounds conventional high-dose treatment strategies aimed at maximal tumor reduction, since high doses can simultaneously promote non-genetic resistance. In this work, we study optimal dosing of anti-cancer treatment under drug-induced cell plasticity. We show that the optimal dosing strategy steers the tumor to a fixed equilibrium composition between sensitive and tolerant cells, while precisely balancing the trade-off between cell kill and tolerance induction. The optimal equilibrium strategy ranges from applying a low dose continuously to applying the maximum dose intermittently, depending on the dynamics of tolerance induction. We finally discuss how our approach can be integrated with in vitro data to derive patient-specific treatment insights.

The first embryonic division of Caenorhabditis elegans is a model for asymmetric cell division, and identifying the stages of cell division across related species could improve our understanding of the divergence of cellular events and mechanisms. Comparative microscopy of evolutionarily divergent species continues to rely on label-free differential interference contrast (DIC) microscopy due to technical challenges in molecular tagging, with the identification of cell division stages still relying on label-free microscopy. Here, we compare multiple deep convolutional neural networks (CNNs) trained to automate cell stage classification in DIC microscopy movies and interpret the results, with code and classification weights released as OpenSource. The networks are trained to identify if a single frame of a time-series belongs to one of the four morphologically distinct stages: (i) pro-nuclear migration, (ii) centration and rotation, (iii) spindle displacement and (iv) cytokinesis, that had been manually labeled. Three previously described networks, ResNet, VggNet, and EfficientNet, and a customized shallow network, which we refer to as EvoCellNet, achieved 91% or greater accuracy in test data from 23 different nematode species. We find activation vectors of the CNNs of the sparse EvoCellNet correlate with spatial localization of intracellular features of multiple species, such as pro-nuclei, spindle, and spindle-poles. While the pipeline is robust when applied to comparable DIC time-series of C. elegans and C. briggsae embryos, distinct from those on which it was trained and tested, successful classification is limited to images with conserved morphological features. Thus, deep learning networks can be used to generalize the morphological changes across species of nematode embryos, capturing chronology based on low-level intracellular features with biological relevance.