A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

Positive and negative feedback regulation of the TGF-β1 explains two equilibrium states in skin aging

During aging, skin homeostasis is essential for maintaining appearance, as well as biological defense of the human body. In this study, we identified thrombospondin-1 (THBS1) and fibromodulin (FMOD) as positive and negative regulators, respectively, of the TGF-β1-SMAD4 axis in human skin aging, based on in vitro and in vivo omics analyses and mathematical modeling. Using transcriptomic and epigenetic analyses of senescent dermal fibroblasts, TGF-β1 was identified as the key upstream regulator. Bifurcation analysis revealed a binary high-/low-TGF-β1 switch, with THBS1 as the main controller. Computational simulation of the TGF-β1 signaling pathway indicated that THBS1 expression was sensitively regulated, whereas FMOD was regulated robustly. Results of sensitivity analysis and validation showed that inhibition of SMAD4 complex formation was a promising method to control THBS1 production and senescence. Therefore, this study demonstrated the potential of combining data-driven target discovery with mathematical approaches to determine the mechanisms underlying skin aging.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Extending BioMASS to construct mathematical models from external knowledge

Motivation

Mechanistic modeling based on ordinary differential equations has led to numerous findings in systems biology by integrating prior knowledge and experimental data. However, the manual curation of knowledge necessary when constructing models poses a bottleneck. As the speed of knowledge accumulation continues to grow, there is a demand for a scalable means of constructing executable models.

Results

We previously introduced BioMASS-an open-source, Python-based framework-to construct, simulate, and analyze mechanistic models of signaling networks. With one of its features, Text2Model, BioMASS allows users to define models in a natural language-like format, thereby facilitating the construction of large-scale models. We demonstrate that Text2Model can serve as a tool for integrating external knowledge for mathematical modeling by generating Text2Model files from a pathway database or through the use of a large language model, and simulating its dynamics through BioMASS. Our findings reveal the tool's capabilities to encourage exploration from prior knowledge and pave the way for a fully data-driven approach to constructing mathematical models.

Availability and implementation

The code and documentation for BioMASS are available at https://github.com/biomass-dev/biomass and https://biomass-core.readthedocs.io, respectively. The code used in this article are available at https://github.com/okadalabipr/text2model-from-knowledge.

Htr2b Promotes M1 Microglia Polarization and Neuroinflammation after Spinal Cord Injury via Inhibition of Neuregulin-1/ErbB Signaling

The secondary injury of spinal cord injury (SCI) is dominated by neuroinflammation, which was caused by microglia M1 polarization. This study aimed to investigate the role and mechanism of Htr2b on neuroinflammation of SCI. The BV2 and HMC3 microglia were treated with lipopolysaccharide (LPS) or interferon (IFN)-γ to simulate in vitro models of SCI. Sprague-Dawley rats were subjected to the T10 laminectomy to induce animal model of SCI. Htr2b mRNA expression was measured by qRT-PCR. The expression of Htr2b and Iba-1 was detected by western blot and immunofluorescence. The expression of inflammatory cytokines in vitro and in vivo was also measured. Kyoto Encyclopedia of Genes and Genomes (KEGG) was employed to analyze Htr2b-regulated signaling pathways. Rat behavior was analyzed by the Basso, Beattie, and Bresnahan (BBB) and inclined plane test. Rat dorsal horn tissues were stained by hematoxylin-eosin (H&E) and Nissl to measure neuron loss. Htr2b was highly expressed in LPS- and IFN-γ-treated microglia and SCI rats. SCI modeling promoted M1 microglia polarization and increased levels of inflammatory cytokines. Inhibition of Htr2b by Htr2b shRNA or RS-127445 reduced the expression of Htr2b, Iba-1, and iNOS and suppressed cytokine levels. KEGG showed that Htr2b inhibited ErbB signaling pathway. Inhibition of Htr2b increased protein expression of neuregulin-1 (Nrg-1) and p-ErbB4. Inhibition of the ErbB signaling pathway markedly reversed the effect of Htr2b shRNA on M1 microglia polarization and inflammatory cytokines. Htr2b promotes M1 microglia polarization and neuroinflammation after SCI by inhibiting Nrg-1/ErbB signaling pathway.

Omics-Based Mathematical Modeling Unveils Pathogenesis of Periodontitis in an Experimental Murine Model

Periodontitis is a multifactorial disease that progresses via dynamic interaction between bacterial and host-derived genetic factors. The recent trend of omics analyses has discovered many periodontitis-related risk factors. However, how much the individual factor affects the pathogenesis of periodontitis is still unknown. This article aims to identify multiple key factors related to the pathogenesis of periodontitis and quantitatively predict the influence of each factor on alveolar bone resorption by omics analysis and mathematical modeling. First, we induced periodontitis in mice (n = 3 or 4 at each time point) by tooth ligation. Next, we assessed alveolar bone resorption by micro-computed tomography, alterations in the gene expression by RNA sequencing, and the microbiome of the gingivae by 16S ribosomal RNA sequencing during disease pathogenesis. Omics data analysis identified key players (bacteria and molecules) involved in the pathogenesis of periodontitis. We then constructed a mathematical model of the pathogenesis of periodontitis by employing ordinary differential equations that described the dynamic regulatory interplay between the key players and predicted the alveolar bone integrity as output. Finally, we estimated the model parameters using our dynamic experimental data and validated the model prediction of influence on alveolar bone resorption by in vivo experiments. The model predictions and experimental results revealed that monocyte recruitment induced by bacteria-mediated Toll-like receptor activation was the principal reaction regulating alveolar bone resorption in a periodontitis condition. On the other hand, osteoblast-mediated osteoclast differentiation had less impact on bone integrity in a periodontitis condition.

Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics

Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling.

For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022).

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A computational framework for patient-specific modeling

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Integration of clinical data and cell line data for model calibration

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Building a mechanistic dynamic model from .txt file

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Stratification of patients with breast cancer based on in silico signaling dynamics

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand-drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.

A text-based computational framework for patient -specific modeling for classification of cancers

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.

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A text file describing biochemical systems is converted into an executable model

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Patient-specific models incorporate individual gene expression profiles

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In silico signaling dynamics can be utilized as prognostic biomarkers

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Personalized kinetic models are capable of predicting potential drug targets

Systems approaches to investigate the role of NF-κB signaling in aging

The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.

A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms

Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation and another showing high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.

Building patient-specific models for receptor tyrosine kinase signaling networks

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.