Editorial: Systems biology, women in science 2021/22: Data and model integration

Abstract

Despite recent progress in encouraging and retaining talented women in science, technology, engineering, and mathematics (STEM) careers, women still face stiff penalties in the academic world. Research shows that women receive less funding, awards, teaching scores, invitations to speak at conferences, and citations than male colleagues (Berggren et al., 2022; Ainslie, 2022). To facilitate the success of our female colleagues and trainees in academia, this Research Topic aimed to highlight the work of women in Systems Biology, with a special focus on showcasing research on Data and Model Integration. It spans advances in theory, methodology, and experimental work with applications to biologically compelling problems. This Research Topic includes six original research articles, one perspective article and one technology and code article, with the participation of 41 authors from 10 countries: Colombia, France, Germany, Greece, Ireland, Mexico, Netherlands, Philippines, Switzerland, and the United Kingdom. We have a total of 7,493 views as of 9 January 2023. Overall, we were very pleased by the quality of the submissions we received in response to the call. In the Model Integration area, Connolly and colleagues presented a methodology for pandemic modelling motivated by the current COVID-19 outbreak with the title “From Epidemic to Pandemic Modelling” (Connolly et al.) Pandemicmodels are important to design effective controlmeasures, such as travel or quarantine restrictions. Here, the authors proposed a methodology for systematically extending epidemic models to multilevel and multiscale spatiotemporal pandemic models that integrate information about geography and travel connections. PetriNuts, a publicly available webbased platform, supports model construction, simulation, and output visualization. It also enables deterministic, stochastic and hybrid simulation, as well as structural and behavioural analysis. Flores-Garza and co-authors published “Mathematical Model of the Immunopathological Progression of Tuberculosis,” an elegant model to understand tuberculosis, a worldwide persistent infectious disease caused by the bacteriaMycobacterium tuberculosis (Flores-Garza et al.). Amechanistic mathematical model integrates multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays. Ordinary differential equations (ODEs) were used to describe the regulatory interplay between the cell phenotypic variation and the inflammatory microenvironment. The model can predict disease outcomes for different mouse genotypes and simulate the interaction between host and pathogen genotypes. In doing so, it provides a powerful tool to test the effect of host-pathogen interaction alterations on infection outcomes. These in silico experiments can lead to future experimentation and help reduce the number of in vivo experiments. OPEN ACCESS