{"doi":"10.3389/fcell.2024.1475005","title":"Editorial: Network-based mathematical modeling in cell and developmental biology","abstract":"The Research Topic on Network-based Mathematical Modeling in Cell and Developmental Biology raised various subjects of interest for mathematical modelers that also provide insight for future studies by bench scientists. In essence, three themes emerged and will be discussed below. First, the crucial and essential topic of reproducibility of published models was addressed. Secondly, novel mathematical and computational approaches enabled a deeper understanding of biological systems. A third theme arose through the significant efforts aimed at creating whole cell models and downstream applications. Enhancing reproducibility through rigor and transparency is a long-term goal of NIH (Collins & Tabak, 2014) and other agencies such as NSF. Wet-lab experiments can be difficult to reproduce because of variations in the conditions. Are models, essentially computational experiments, easily reproducible? Guided by FAIR principles (Findable, Accessible, Interoperable, and Reusable), Pedro Mendes examined a highly cited mathematical model that described segment polarity in Drosophilia published by Von Dassov et al. in 2000. The unavailability of the original software forced the author to recode the model, which was a labor-intensive process that required de novo model implementation. The major take-home message from the report is that publication of mathematical models in a widely used standard format is essential, as only this will ensure the model is reproducible in the future. Several novel mathematical approaches were taken by investigators to better understand cellular reaction networks. Marrone and colleagues described the use of nullclines, curves on a plane that are solutions to the differential equations, for analysis of systems with more than two variables. The authors followed Zhang et al. (2011) in considering pseudo-nullclines (an analog of nullclines for a system that can be decomposed into 2 modules) and used them to reproduce the dynamics of several well-known systems such as the embryonic cell cycle and MAPK cascade. Glazer and colleagues developed a new Monte Carlo Boolean Modeler (MC-Boomer) method to generate large (hundreds of thousands) collections of Boolean models whose simulations agree with observed data. A pipeline for analyzing these models and discovering novel regulatory interactions was developed and applied to a well-known model of the Drosophila segment polarity network (Albert & Othmer, 2003). Analysis of the models generated by MC-Boomer can be used to identify alternate hypotheses for the gene regulatory mechanism that could be then experimentally validated. Eidi and colleagues used stochastic modeling to investigate the spatial arrangement of cell types during stem cell division, governed by two Turing signaling patterns. Their model predicts the pattern of the differentiated cells and identifies the signaling patterns that influenced the formation of the cellular structure.Biological networks are usually described as graphs with nodes representing biological entities (e.g., genes, proteins, functional complexes) and connecting edges representing influences on their behavior. Thus, graph-theoretical methods are under constant development, as illustrated by the two manuscripts in this issue. Nam and Gunawardena introduced the linear framework - a graph-theoretic approach to analyzing biomolecular systems described by continuous time Markov processes, such as post-translational modification and gene regulation. The nodes represented individual molecular states and edges represented the probabilities of transitions between molecular states. This report described the application of linear frameworks before the steady state was reached. Specifically, the authors showed that the properties of the First Passage Time (FPT) were functions of the edge labels. The FPT defined a timescale of single-molecule kinetics, such as the enzyme’s completion time, and the approach described by the authors can be used for the","journal":"Frontiers in Cell and Developmental Biology","year":2024,"id":502009,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1174660,"name":"Susan D. Mertins","orcid":"0000-0001-6517-0471","position":1,"is_corresponding":false},{"id":60882,"name":"Michael L Blinov","orcid":"0000-0002-9363-9705","position":0,"is_corresponding":true}],"reference_count":4,"raw_metadata":null,"created_at":"2026-07-19T02:10:19.647285Z","pmid":"39188526","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}