Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as “proof of concept” regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

Summary: According to previous reports, Lactococcus lactis imports glucose via two distinct phosphoenolpyruvate:phosphotransferase systems (mannose‐PTS and cellobiose‐PTS) and one or more unknown non‐PTS permease(s). GlcU was identified as the sole non‐PTS permease involved in the transport of glucose. Additionally, the biochemical properties of PTS^Man, PTS^Cel and GlcU were characterized in double knockout mutants with glucose uptake restricted to a single system. Transport susceptibility to protonophores indicated that glucose uptake via GlcU is proton‐motive force dependent. Competition assays revealed a high specificity of GlcU for glucose. Furthermore, the permease has low affinity for glucose and displays strong preference for the β‐anomer as shown by the profiles of consumption of the two glucose anomers studied by ¹³C‐NMR. Similar kinetic properties were found for PTS^Cel, while PTS^Man is a high‐affinity system recognizing equally well the two anomeric forms of glucose. Transcripts of the genes encoding the three transporters are present simultaneously in the parent strain NZ9000 as shown by reverse transcription‐PCR. Investigation of the distribution of GlcU homologues among bacteria showed that these proteins are restricted to the low‐GC Gram‐positive Firmicutes. This work completes the identification of the glucose transport systems in L. lactis MG1363.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific—and practical–medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

Like many other environments, Lake Mendota, WI, USA, is populated by many thousand microbial species. Only about 1,000 of these constitute between 80 and 99% of the total microbial community, depending on the season, whereas the remaining species are rare. The functioning and resilience of the lake ecosystem depend on these microorganisms, and it is therefore important to understand their dynamics throughout the year. We propose a two-layered set of dynamic mathematical models that capture and interpret the yearly abundance patterns of the species within the metapopulation. The first layer analyzes the interactions between 14 subcommunities (SCs) that peak at different times of the year and together contain all species whereas the second layer focuses on interactions between individual species and SCs. Each SC contains species from numerous families, genera, and phyla in strikingly different abundances. The dynamic models quantify the importance of environmental factors in shaping the dynamics of the lake’s metapopulation and reveal positive or negative interactions between species and SCs. Three environmental factors, namely temperature, ammonia/phosphorus, and nitrate+nitrite, positively affect almost all SCs, whereas by far the most interactions between SCs are inhibitory. As far as the interactions can be independently validated, they are supported by literature information. The models are quite robust and permit predictions of species abundances over many years both, under the assumption that conditions do not change drastically, or in response to environmental perturbations.

The effect of pH on the glucose metabolism of non-growing cells of L. lactis MG1363 was studied by in vivo NMR in the range 4.8 to 6.5. Immediate pH effects on glucose transporters and/or enzyme activities were distinguished from transcriptional/translational effects by using cells grown at the optimal pH of 6.5 or pre-adjusted to low pH by growth at 5.1. In cells grown at pH 5.1, glucose metabolism proceeds at a rate 35% higher than in non-adjusted cells at the same pH. Besides the upregulation of stress-related genes (such as dnaK and groEL), cells adjusted to low pH overexpressed H⁺-ATPase subunits as well as glycolytic genes. At sub-optimal pHs, the total intracellular pool of lactic acid reached approximately 500 mM in cells grown at optimal pH and about 700 mM in cells grown at pH 5.1. These high levels, together with good pH homeostasis (internal pH always above 6), imply intracellular accumulation of the ionized form of lactic acid (lactate anion), and the concomitant export of the equivalent protons. The average number, n, of protons exported with each lactate anion was determined directly from the kinetics of accumulation of intra- and extracellular lactic acid as monitored online by ¹³C-NMR. In cells non-adjusted to low pH, n varies between 2 and 1 during glucose consumption, suggesting an inhibitory effect of intracellular lactate on proton export. We confirmed that extracellular lactate did not affect the lactate: proton stoichiometry. In adjusted cells, n was lower and varied less, indicating a different mix of lactic acid exporters less affected by the high level of intracellular lactate. A qualitative model for pH effects and acid stress adaptation is proposed on the basis of these results.