A central part of our digital twins, which underlies all apps in the STRATIF-AI project, are the models we have for the brain. So far these models have mostly focused on the neurovascular coupling (NVC). i.e. on the vascular response to neuronal activity. In our latest Ph.D. thesis “Mathematical Modelling of Cerebral Metabolism: From Ion Channels to Metabolic Fluxes”, which was defended by Dr Nicolas Sundqvist last Friday, we have taken these models to a higher level: to also include more detailed cerebral metabolism.
In the first paper, we use MRS data from humans to develop a small model for central metabolism, which is linked to our NVC models. In the second paper, we focus on cell-specific contributions, and show that inter-neurons might be the most dominant cell type in the NVC response; this puts much of the existing fMRI data into question. In the third paper, we present a detailed model for the most metabolically expensive process in neurons: the ion channels. Finally, in the fourth paper, we have developed a method for how to develop even more detailed intracellular flux models, using 13C-labelled media and a new approach to validation of intracellular flux models.
Opponent was Dr João Duarte from Lund, and the examination committee consisted of Jeanette Hellgren Kotaleski (KTH), Arvind Kumar (KTH), and Katarina Nöh (Jülich Forschungscentrum).
During Feb 14 and 15, 2024, we at ISBgroup at Linköping University (LIU) had a few visitors, who joined us for an interactive 2-day workshop on UX-design for our stroke Prevention App development. The Prevention app is developed jointly between Linköping University (LiU) and Z2, as part of the STRATIF-AI Horizon-Europe project (stratif-ai.eu), and it is meant for both primary and secondary stroke prevention. Primary prevention will be done in Region Västerbotten and Region Östergötland, and it involves a so-called Health Dialogue. A Health Dialogue is preceded by measurements of traditional risk factors (blood pressure, blood lipids, fasting glucose, etc), which are discussed together with e.g. a specialized nurse or a physiotherapist. Our app will use digital twins to help explain why a person should care about these risk factors, and how they can change their lifestyle (e.g. exercise more, eat less, take certain medications, etc), to improve the risk factors. Based on this information, the patient then sets some goals, to improve their lifestyle, to reduce one or several of the risk factors. These goals are then worked further on together with a coach and dedicated support-group. Also this subsequent coaching-period will be supported by the app, which thus provides a bridge between traditional healthcare actors, and those not normally integrated in healthcare: coaches, personal trainers, support groups. etc.
During the 2 days workshop, we did various role playing exercises, whiteboard and post-it note sketches, which then will be converted to Figma implementations, which then lead to new user tests, etc. From Z2, we have Jesper Fellenius, who has developed the backend for the personal data vault, where the data are stored, and who also has developed a prior prototype for a similar app, which we re-use. From LIU, we have Dr Gunnar Cedersund, Dr Dirk de Weerd, and M.Sc. student Greta Nilsson, who work with the app development. From Region Östergötland (RÖ), we have MD student Johanna Levander and MD specialist Valentin Kindesjö, who represent clinical needs. Apart from these, we also have Johan Holmsäter and Mats Janson, who represent Lev Skönare and coaching and wellbeing partners, who also are connected to STRATIF-AI.
This year, we got a double jackpot from the Swedish Research Council – who gave us glowing reviews for the 3R project, scaling from microphysiological in vitro systems to humans using scalable digital twins.
In Sweden, the Swedish Research Council (Swe: Vetenskapsrådet, VR), is the most central research grant, and it is often considered a key quality stamp of a top researcher to have at least one grant from VR. Therefore, competition is usually fierce (acceptance rate usually is 5-15%), and it is not at all guaranteed that you get money, even if you have a competitive application. Therefore, I am proud to say that this year, I got not only one grant, but two – and that the evaluation from the reviewers was unusually high and glowing.
The project I have gotten the review responses for so far is a special call on 3R, i.e. Replacement, Reduction and Refinement of animal experiments. This is a topic, I have been very active in ever since 2015, when I was awarded the first edition of the prize “Nytänkaren” (the thinker of new ideas), by the Swedish Fund for Research without Animal Experiments. The project extends on our experimental work, both within the group, doing cell biology cultures using 13C-labelled metabolites on liver and adipose tissue taken from surgery), and that in collaboration with AstraZeneca, centered around organs-on-a-chip, i.e. small microphysiological systems (MPS), with organoids and spheroids consisting of human cells (Fig A, recent paper). In the project, we will i) analyse these in vitro data using mechanistic modelling to get more information out of the data (e.g. metabolic fluxes), ii) plan new experiments, by first doing the experiments in the computer, and iii) translate the results to humans, by e.g. scaling the volumes of the spheroids to human sizes, and by adding the missing organs, which allows us to re-assemble the digital twin in the computer (Fig B-C, Step 1 and 2). The project will evaluate and quantify the benefits of this for e.g. drug development, and we will disseminate the results to pharma, scientists, and regulatory agencies (Fig C, Step 3).
In the evaluation, we only got 6s and 7s, which means that we were among the highest rated of all applicants, even among the few who got money (6 out of 56). The ranking is from 1-7, where a “normal, decent” scientist usually get a 3 (meaning “good”), and where you need at least a mixture of 5s and 6s to have any chance of getting money. If you get all 6s, you are usually getting the money for sure, and 7 is only very rarely given out (I was a reviewer for ~60 applicants two years ago, and then I think only one or possibly two got a 7 on any criterion). Therefore, I am very grateful that this year, I got only 6s and higher, and that two(!) categories got a 7: “merits of applicant” and “relevance for 3R” (Fig D). If the rating levels were the same as when I was a reviewer, I would – I think – have been number one of all applicants that year, and in any case, I must have been among the very top of all the 56 applicants also this year.
The life of a scientist is filled with many many rejected applications, so when you get a jackpot once in a while, it is important to stop a bit – and celebrate! Because tomorrow, it is time to get started working on the new exciting research projects! 🙂
Last week, I had the honor of giving the concluding keynote lecture at the event “Data-driven mechanistic modelling in life sciences”. This follows a trend of being invited to give more and more keynote and plenary lectures at events, for which I am very grateful. Such longer lectures also give me the chance to expand a little bit more on my point-of-view. The focus of this particular workshop is also something I am very keen to promote, since I think that this particular overlap (mechanistic and data-driven modelling) is under-represented in many communities and conferences.
The centrality of this overlap is actually seen even in our group logo (Fig A). a) The fact that it has an open non-black box, represents the fact that we do mechanistic modelling. b) The data-driven aspect of our models is represent by the purple core in the middle, which represents the fact that we always look for core predictions. Core predictions are predictions that are well-determined from the current prior knowledge and data, even when taking all uncertainties in data and prior knowledge into account.
While I personally think that this is the way to work, and while we have a very well-established workflow for how to develop models in this fashion, mechanistic modelling and data-driven modelling are unfortunately often done in two disjoint communities, with too little overlap (Fig B). Mechanistic modelling often results in mere simulation-based results, which have not been validated using independent data, i.e. data that has not been used to train the model. This is often the case for e.g. PDE and agent-based models, but also common in e.g. theoretical ecology, theoretical biology, etc. It was therefore encouraging too see that one of the presentations at the workshop (by Joshua Bull) looked at spatial models, and on how to quantify the comparison between simulations and data also for spatial models. Data-driven modelling is too often interpreted to mean only machine-learning, narrow AI, and other black-box modelling techniques. While these are big and very hyped communities and approaches at the moment, they are not the only techniques that can be used to do data-driven modelling. In other words, while these black-box models include important techniques, which are useful if one has standardized large-scale data, they also have critical short-comings. Black-box models e.g. have big problems incorporating the type of data that is present in most biological papers, including the prior that is knowledge available. For these reasons, explainability and trustworthiness are challenges. I therefore think that hybrid modelling is the way forward (see e.g. this review, and this example). At the conference, there was also an excellent opening keynote of day 2, by Alvaro Köhn-Luque, which showed some additional and interesting examples of hybrid models.
Titel: Mathematical modelling of diffuse liver disease
Opponents: Jan Boren and Matthias König
Abstract: Christian has worked on constructing mathematical models in the context of NAFLD treatment and disease progression. One project has been focusing on creating a minimal mathematical model capable of simulating different dietary interventions, such low carb high fat diet (LCHF), and high fructose (HC) diets. To better understand treatment connection to changes in lipid fluxes. The second project has focused on determining liver function before hepatectomy, which is the only treatment option for HCC and liver metastasis. The two different projects have focused on each end of the NAFLD disease spectra, and the coming project will focus on modelling of non-alcoholic steatohepatitis (NASH)
Titel: Hybrid modelling for diabetes and stroke prevention
Opponents: Maria Kjellsson, Anna Nordström
Abstract: The long-term goal that the thesis will be a part of is to have a tool for prevention of metabolic and cardiovascular diseases, particularly stroke and its common precursor diabetes. The plan is to use a digital twin and hybrid modelling. A digital twin can be used to make personalized predictions for different scenarios – lifestyle changes, diets, drugs, etc. – that can then be evaluated and compared. These predictions can include the long-term dynamics of different risk factors relevant for cardiovascular disease, such as BMI or fasting plasma glucose, or short term dynamics such as meal response glucose. These predictions can for example be used to understand the dynamics behind disease progression and to get continuous updated on progress of the changes made in real life, which can hopefully increase medical pedagogics and motivation. The long-term predictions of risk factors can be used in a hybrid modelling scheme as input in a machine learning model to get your risk score for having a stroke within a certain time span. The thesis covers the first steps towards, and a first example of the idea described above, as well as the development of models describing some of the different mechanisms relevant to diabetes and stroke progressions that can later be used in a comprehensive model of the entire progression.
Titel: Systems modelling of human metabolism: Methods for 13C metabolic flux analysis and applications to thebrain
Opponents: Elizabeth Klint, Dr. Katharina Nöh, Dr. Joao Duart
Abstract: Nicolas has worked on constructing a mathematical model that can explain the mechanism that regulate the metabolism in the brain. This mathematical model explains what the cerebral metabolic response looks like in response neuronal activity induced by a visual stimulus. Furthermore, the work also includes how these mechanisms for the metabolic response connect to the mechanisms that of the neurovascular coupling. This work has resulted in a manuscript recently accepted for publication and will formed a base for future modelling endeavours. Additionally, Nicolas has worked with modelling intracellular metabolic fluxes using measurements of 13C isotope labels. A lot of this work have gone into method development for modelling stoichiometric models for 13C metabolic flux analysis (MFA) and has resulted in a publication where we present a method for validating such models when there is an uncertainty with respect to experimental measurement uncertainty. The plan for the continued work of Nicolas’ doctoral studies is to apply the methodologies for 13C MFA to a system for studying neurodegenerative diseases in neurological cell lines. As well as to investigate the mechanisms of the regulation of oxygen metabolism with respect to activity in different types of neuronal cells.
CompBioMed Conference 2023 (CBMC23) will address all aspects of computational biomedicine, from genome through organ to whole human and population levels, embracing data driven, mechanistic modelling and simulation, machine learning and combinations thereof. This year Gunnar will is one of the Plenary Speakers and he will discuss physiologically based digital twins: a digital and interactive copy of yourself that follows you throughout your health journey.
Join the VPH (Virtual Physiologocal Human) summer school in Barcelona, June 5-9, 2023. This summer school includes 15 morning lectures, one honorary VPH lecture, and a lot of networking opportunities. Gunnar will talk about about the digital twins, so don’t miss it!
Last day to register is 21 of May, and you find more information HERE
M4-health and digital twins: bring a digital copy of yourself with you throughout your health journey
Keynote Lecture, Wednesday June 7th, 2023, 09:30-11:00
Abstract: For the last 20 years, Cedersund has developed mechanistic mathematical models for all of the main organs in the human body: heart, liver, fat, brain, etc. Lately, these models have combined into an interconnected model for the body as a whole. This interconnected model can be made specifically for each individual, and is then called a digital twin. This digital twin technology employs a hybrid approach, which combines the mechanistic simulation models with machine learning and bioinformatics models. This allows a patient, doctor, or other end-user to look inside the body of a patient, as it is now, ranging from the whole-body to the intracellular level. This also allows for simulations of different future scenarios, ranging from ms to years, and can simulate e.g. the risk of a stroke, depending on choice of diets, exercise, and certain medications. The models are thus of an M4-nature: multi-level, multi-timescale, mechanistic, and multi-organ. The focus on this talk will be on how we systematically test mechanistically hypothesis on the intracellular and organ levels, using mechanistic modelling, optimization, and predictions with uncertainty – followed by corresponding model-designed experiments. I will also to some extent go through how we assemble the different organ sub-models together into the integrated digital twin, which in itself is a modelling problem, and how we then put all of this into a series of different eHealth apps.
Biosketche: Gunnar Cedersund (https://liu.se/en/employee/gunce57) heads a cross-disciplinary research group at the Department of Biomedical Engineering (IMT) at Linköping University. The heart of this group (15+ people) does hybrid mathematical modelling, combining machine learning with mechanistic small- and large-scale models. These models are developed using both pre-clinical and experimental data of various types, which are collected both by experimentalists and clininicians within the same group, and by numerous collaborators. These models are made available for preventive and patient-centric care, as well as for drug development and medical pedagogics, using innovative eHealth technologies. This is made possible, e.g., via the fact that Cedersund heads the 6MEuro EU project STRATIF-AI, which brings his digital twins into healthcare for all phases of stroke (prevention, acute treatment, and rehabilitation), using a series of different apps, which all are connected to the same backend.
Digital twins will now be tested in real health care environments, in an EU-financed project. Gunnar is the project manager of STRATIF-AI, which has been granted SEK 65 million over a four-year period by Europe Horizon (the European Commission). The project is linked to stroke research, but the digital twins method is expected to have many different applications.