Mike's Notes
Introduction to Volume 16, Issue 3 (28 August 2026) of Interface Focus.
Resources
- https://royalsocietypublishing.org/rsfs/issue/16/3
- https://royalsocietypublishing.org/rsfs/article/16/3/20250077/483124/Data-driven-modelling-for-living-systemsData
- https://royalsocietypublishing.org/rsfs/article-pdf/doi/10.1098/rsfs.2025.0077/6162777/rsfs.2025.0077.pdf
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Last Updated
30/09/2026
Data-driven modelling for living systems
Maia Angelov: Maia Angelova has a PhD, MSc, BSc in Physics; Professor at Aston University.
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Data-driven modelling in the living system has increasing significance with the abundance of complex data of different modalities. Data are being collected at different scales, from molecular to genetic, cellular, organ, organism and vital signs, to electronic health records. In addition, we produce individual health data, sleep and mobility data collected with wearable devices, as well as data collected from social media, professional networks, workplace and the environment in general. Modelling these data is now possible with the advances of several methods and technologies, all part of the artificial intelligence framework. Modern data analysis is a powerful lens with which we can zoom in and out of the living system, similar to what we can observe with a microscope. This theme issue presents data-driven models which reflect several different angles and lenses to zoom in and out of the human body, to observe and analyse the role and functions of its genes, cells, organs and the interactions between them, as well as the role of the human in the society and environment.
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Accepted: 01 Apr 2026
Published online: 28 Aug 2026
Online ISSN: 2042-8901
Print ISSN: 2042-8898
Interface Focus. (2026) 16 (3): 20250077 .
https://doi.org/10.1098/rsfs.2025.0077
Keywords:
data-driven modelling, living system, complexity, artificial intelligence, machine learning, multi-scale modelling, multi-modal data
Subjects:
biocomplexity, biomathematics, biophysics
Introduction
Data-driven modelling for living systems is a field of increasing significance with the abundance of complex data of different modalities. With the advances of new technologies, the quality and quantity of multi-modal data are increasing. Besides, data are being collected at many scales, from molecular to genetic, cellular, organ, organism and vital signs, and electronic health records. In addition, we produce individual health data, sleep and mobility data collected with wearable devices, as well as data collected from social media, professional networks, workplace and the environment in general. Modelling these data is now possible with the advances of several new and not so new methods and technologies, all part of the artificial intelligence framework, such as machine learning, data mining, mathematical modelling, physics-inspired modelling and various statistical approaches.
We can think of modern data analysis as a powerful lens, with which we can zoom in and out of living systems, similar to what we can observe with a microscope or a telescope, respectively. This theme issue presents data-driven models, which reflect several different angles and lenses to zoom in and out of the human body and observe and analyse the role and functions of its genes, cells, organs and interactions between them, as well as the human in society and the environment. With data-driven modelling, we can explore what our genes, organs, personal habits, workplace, environment and surroundings (including nature and pollution) can tell us about our health, behaviours and well-being.
The systems modelled here are living systems, and as such, they are changing with time and space. The approaches to model such systems are multi-focal to reflect the reach and real-time dynamics and variability of these systems. As living systems are immensely different and variable, one approach does not suit all. Here, we model physiological elements as nodes and events at the organ level, which also includes the interactions between our organs, such as brain, heart and blood system, positioned in a network that reflects the geometry (position of the node) and dynamics (interactions between the nodes, as well as between nodes and external factors). This network is known as the physiological organ network. It includes the physical positions of the organs in a network generated from our anatomy, and the dynamics generated from the physiological and physical functions. These network models can account for the interactions and the communications between the organs, reaction to external factors, relations and associations between the nodes, which can happen at different scales, from genetic to environment and reflect the multi-scale aspect of the models. The description and analysis of such a physiological network at the organ level can be applied at the molecular, genetic, and cellular level, as well as a network of individuals in a social, financial or manufacturing environment, to mention a few. This approach reflects the multi-scale nature of our organism and the ecosystem in which we exist.
The level of complexity changes with changes at the individual, small or larger group of elements, or at a level of society. In addition, when describing a more complex system, we often prefer to drop some of the details. In this way, we can analyse and visualize at a higher level and obtain an overview of the system and its behaviour. We can consider this as adding or dropping layers of complexity as the lens, mentioned above, and the process as zooming in and out to observe better the details or see a bigger horizon. For the time being, there is no single best model; different models may describe specific scales better. Furthermore, we may not need all details but a fast answer to a specific question, which may require choosing to model specific elements of our system.
The modelling approaches at each scale may be different, but the main components, such as the structure of the network (described by geometry and graph theory) and the dynamics of the network (described by various mathematical and physical approaches), as well as the probabilistic aspects of the events and changes in the elements (described by stochastics and statistical approaches), remain the same.
Such variety requires a multi-disciplinary approach, involving the work of data and computer scientists, mathematicians, physicists, statisticians and engineers, as well as physiologists, neuroscientists, medical doctors, sociologists and economists, to name a few.
The idea of this theme issue came at the conference with the same name held in Sofia, from 8 to 9 June 2023, celebrating Professor Maia Angelova's 65th birthday (figure 1). It seemed an appropriate venue for this topic as she has modelled multi-scale and complex systems for most of her professional life. Furthermore, she has expanded her expertise in modelling crystals and materials, complex and ordered systems in general, to model living systems, which could be disordered, but rarely chaotic. This has happened to many physicists and applied mathematicians in the last 20 years, who now work in biology, physiology, and medicine, and apply knowledge and methodologies from one world to another.
Figure 1.
The idea of this theme issue was inspired by the conference ‘Data-driven modelling for living systems’, held to honour Professor Maia Angelova's 65th birthday. The conference was hosted by the Institute of Biophysics and Biomedical Engineering at the Bulgarian Academy of Sciences, Sofia, Bulgaria, on 8 and 9 June 2023. The conference participants after the poster session with the birthday cake in the conference hall. Credit: Ms Yoana Dobreva.
The conference was organized by the Institute of Biophysics and Bioengineering at the Bulgarian Academy of Sciences. It was in a hybrid mode and included invited and plenary talks, short oral presentations and posters. The participants were established experts in their fields, as well as early and mid-career researchers. This theme issue presents a selection of papers given at the conference. We have included the work of established scientists and mathematicians, as well as early-career researchers (postdoctoral researchers and PhDs).
Ethics
This work did not require ethical approval from a human subject or animal welfare committee.
Data accessibility
This article has no additional data.
Declaration of AI use
I have not used AI-assisted technologies in creating this article.
Author contributions
M.A.: conceptualization, investigation, methodology, project administration, writing—original draft, writing—review and editing.
Conflict of interest declaration
I declare I have no competing interests.
Funding
No funding has been received for this article.
Theme
One contribution of 9 to a theme issue ‘Data driven modelling for living systems’.
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