Maintenance of alternative chromatin states through cell divisions pose some fundamental constraints on the dynamics of histone modifications. In this paper, we study the systems biology of epigenetic inheritance by defining and analyzing general classes of mathematical models. We discuss how the number of modification states involved plays an essential role in the stability of epigenetic states. In addition, DNA duplication and the consequent dilution of marked histones act as a large perturbation for a stable state of histone modifications. The requirement that this large perturbation falls into the basin of attraction of the original state sometimes leads to additional constraints on effective models. Two such models, inspired by two different biological systems, are compared in their fulfilling the requirements of multistability and of recovery after DNA duplication. We conclude that in the presence of multiple histone modifications that characterize alternative epigenetic stable states, these requirements are more easily fulfilled.

Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory.

The application of liposuctioned white adipose tissue (L-WAT) and adipose-derived stem cells (ADSCs) as a novel immunomodulatory treatment option is the currently subject of various clinical trials. Because it is crucial to understand the underlying therapeutic mechanisms, the latest studies focused on the immunomodulatory functions of L-WAT or ADSCs. However, studies that examine the specific transcriptional adaptation of these treatment options to an extrinsic inflammatory stimulus in an unbiased manner are scarce. The aim of this study was to compare the gene expression profile of L-WAT and ADSCs, when subjected to tumor necrosis factor alpha (TNFα), and to identify key factors that might be therapeutically relevant when using L-WAT or ADSCs as an immuno-modulator. Fat tissue was harvested by liposuction from five human donors. ADSCs were isolated from the same donors and shortly subjected to expansion culture. L-WAT and ADSCs were treated with human recombinant TNFα, to trigger a strong inflammatory response. Subsequently, an mRNA deep next-generation sequencing was performed to evaluate the different inflammatory responses of L-WAT and ADSCs. We found significant gene expression changes in both experimental groups after TNFα incubation. However, ADSCs showed a more homogenous gene expression profile by predominantly expressing genes involved in immunomodulatory processes such as CCL19, CCL5, TNFSF15 and IL1b when compared to L-WAT, which reacted rather heterogeneously. As RNA sequencing between L-WAT and ADSCS treated with TNFα revealed that L-WAT responded very heterogeneously to TNFα treatment, we therefore conclude that ADSCs are more reliable and predictable when used therapeutically. Our study furthermore yields insight into potential biological processes regarding immune system response, inflammatory response, and cell activation. Our results can help to better understand the different immunomodulatory effects of L-WAT and ADSCs.

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In aiming to explain the establishment, maintenance and stability of methylation pattern in gene body of Arabidopsis we propose here a theoretical framework for understanding how the methylated and unmethylated states of cytosine residues are maintained and transmitted during DNA replication. Routed in statistical mechanics, the framework built herein is used to explore minimal models of epigenetic inheritance and identify the necessary conditions for stability of methylated/unmethylated states of cytosine over rounds of DNA replication. 1The models are flexible enough to allow adding new biological concepts and information.

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Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation). HIC data has the potential to inform relevant aspects of the healthcare systems. Although HIC data inherit limitations, large sample sizes and long-term follow-up provides enormous predictive power. Herein, we highlight the benefits and limitations of HIC data and provide examples from the cardiovascular field, i.e. how HIC data is supporting healthcare, focusing on the demographical and epidemiological differences, pharmacotherapy, healthcare utilization, cost-effectiveness and outcomes of different treatments. As an outlook we discuss the potential of using HIC-based big data and modern artificial intelligence (AI) algorithms to guide patient education and care, which could lead to the development of a learning healthcare system and support a medically relevant legislation in the future.