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MotivationBiological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks.ResultsInitial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies.Availability and implementationSource code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata.Supplementary informationSupplementary data are available at Bioinformatics online.

As the Corona Disease 2019 (COVID-19) caused by SARS-CoV-2 persists, vaccination is one of the key measures to contain the spread. Side effects (SE) from vaccination are one of the reasons for reluctance to vaccinate. We systematically investigated self-reported SE after the first, second, and booster vaccinations. The data were collected during the TüSeRe: exact study (Tübinger Monitoring Studie zur exakten Analyse der Immunantwort nach Vakzinierung). Employees of health and research institutions were invited to participate. Study participants were asked to fill out an online questionnaire and report their SE after each dose of SARS-CoV-2 vaccination. A total of 1046 participants (mean age: 44 ± 12.9 years; female, n = 815 (78%); male, n = 231 (22%)) were included in the analysis. Local and systemic SE were more frequent after receiving the vector-based vaccine ChAdOx1 nCoV-19 in the first vaccination. However, local and systemic SE were more common after receiving mRNA vaccines (BNT162b2, mRNA-1273) in the second vaccination. Compared to the BNT162b2 vaccine, more SE have been observed after receiving the mRNA-1273 vaccine in the booster vaccination. In multivariate analysis, local and systemic side effects were associated with vaccine type, age and gender. Local and systemic SE are common after SARS-CoV-2 vaccines. The frequency of self-reported local and systemic SE differ significantly between mRNA and vector-based vaccines.

Motivation Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology. Results Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible. Availability and implementation Code is freely available at https://github.com/sysbio-bioinf/BNStatic. Supplementary information Supplementary data are available at Bioinformatics online.

Mobile applications have increasingly entered the healthcare sector. Besides being daily companions, so-called mHealth applications have the potential to enable individuals to collect data, document issues, and share them with healthcare professionals to better adjust medical treatment, side effects, or quality of life. While patient empowerment should be a paramount goal, the setup of these applications in a reliable and communication-effective way is under discussion. In particular, including mHealth applications in the clinical practice routine is crucial to boost their development. Security concerns are of utmost importance as such applications deal with personal data. Considering the sensitive nature of many of the involved data, a trustworthy transfer protocol to the respective health care providers is essential to convince potential users. On the same grounds, healthcare providers, which represent another major stakeholder, might be skeptical of utilizing mHealth applications. This issue is often not prioritized by app developers, and there is a multitude of apps lacking clear and transparent data transfer concepts with a focus on both security and usability. In the following, we present and discuss two different approaches for managing and reporting sensitive clinical information and their secure inter-sectoral transfer. Both use cases are currently implemented into clinical practice, and their applicability is under constant evaluation. Besides, to empower inter-sectoral communication, both approaches have been developed in close collaboration with healthcare providers to maximize both communication and effectiveness of the mHealth applications. Based on our work, we conclude that while mHealth applications can be important in many aspects of improving health care, there are often significant limitations of mHealth-based communication, which can hamper its integration in clinical settings. To overcome these limitations, we show how to apply and re-elaborate on existing security and communication strategies. Finally, we highlight how these approaches can strengthen both patient and healthcare professionals’ empowerment.

Mobile applications have increasingly entered the healthcare sector. Besides being daily companions, so-called mHealth applications have the potential to enable individuals to collect data, document issues, and share them with healthcare professionals to better adjust medical treatment, side effects, or quality of life. While patient empowerment should be a paramount goal, the setup of these applications in a reliable and communication-effective way is under discussion. In particular, including mHealth applications in the clinical practice routine is crucial to boost their development. Security concerns are of utmost importance as such applications deal with personal data. Considering the sensitive nature of many of the involved data, a trustworthy transfer protocol to the respective health care providers is essential to convince potential users. On the same grounds, healthcare providers, which represent another major stakeholder, might be skeptical of utilizing mHealth applications. This issue is often not prioritized by app developers, and there is a multitude of apps lacking clear and transparent data transfer concepts with a focus on both security and usability. In the following, we present and discuss two different approaches for managing and reporting sensitive clinical information and their secure inter-sectoral transfer. Both use cases are currently implemented into clinical practice, and their applicability is under constant evaluation. Besides, to empower inter-sectoral communication, both approaches have been developed in close collaboration with healthcare providers to maximize both communication and effectiveness of the mHealth applications. Based on our work, we conclude that while mHealth applications can be important in many aspects of improving health care, there are often significant limitations of mHealth-based communication, which can hamper its integration in clinical settings. To overcome these limitations, we show how to apply and re-elaborate on existing security and communication strategies. Finally, we highlight how these approaches can strengthen both patient and healthcare professionals’ empowerment.

Cell signaling pathways often crosstalk generating complex biological behaviors observed in different cellular contexts. Frequently, laboratory experiments focus on a few putative regulators, alone unable to predict the molecular mechanisms behind the observed phenotypes. Here, systems biology complements these approaches by giving a holistic picture to complex signaling crosstalk. In particular, Boolean network models are a meaningful tool to study large network behaviors and can cope with incomplete kinetic information. By introducing a model describing pathways involved in hematopoietic stem cell maintenance, we present a general approach on how to model cell signaling pathways with Boolean network models.

In its most trending interpretation, empowerment in health care is implemented as a patient-centered approach. In the same sense, many mobile health (mHealth) apps are being developed with a primary focus on the individual user. The integration of mHealth apps into the health care system has the potential to counteract existing challenges, including incomplete or nonstandardized medical data and lack of communication, especially in the intersectional context (eg, patients, medical forces). However, concerns about data security and privacy, regional differences in regulations, lack of accessibility, and nontransparent apps hinder the successful integration of mHealth into the health care system. One approach to address this is to rethink the interpretation of empowerment. On that basis, we here examine existing approaches of individual empowerment and subsequently analyze a different view of empowerment in digital health, namely interaction empowerment. Such a change of perspective could positively influence intersectoral communication and facilitate secure data and knowledge sharing. We discuss this novel viewpoint on empowerment, focusing on more efficient integration and development of mHealth approaches. A renewed interpretation of empowerment could thus buffer current limitations of individual empowerment while also advancing digitization of the health system.

UNSTRUCTURED In its most trending interpretation, empowerment in health care is implemented as a patient-centered approach. In the same sense, many mobile health (mHealth) apps are being developed with a primary focus on the individual user. The integration of mHealth apps into the health care system has the potential to counteract existing challenges, including incomplete or nonstandardized medical data and lack of communication, especially in the intersectional context (eg, patients, medical forces). However, concerns about data security and privacy, regional differences in regulations, lack of accessibility, and nontransparent apps hinder the successful integration of mHealth into the health care system. One approach to address this is to rethink the interpretation of empowerment. On that basis, we here examine existing approaches of individual empowerment and subsequently analyze a different view of empowerment in digital health, namely interaction empowerment. Such a change of perspective could positively influence intersectoral communication and facilitate secure data and knowledge sharing. We discuss this novel viewpoint on empowerment, focusing on more efficient integration and development of mHealth approaches. A renewed interpretation of empowerment could thus buffer current limitations of individual empowerment while also advancing digitization of the health system.

Overcoming the COVID-19 crisis requires new ideas and strategies for online communication of personal medical information and patient empowerment. Rapid testing of a large number of subjects is essential for monitoring and delaying the spread of SARS-CoV-2 in order to mitigate the pandemic's consequences. People who do not know that they are infected may not stay in quarantine and, thus, risk infecting others. Unfortunately, the massive number of COVID-19 tests performed is challenging for both laboratories and the units that conduct throat swabs and communicate the results.The goal of this study was to reduce the communication burden for health care professionals. We developed a secure and easy-to-use tracking system to report COVID-19 test results online that is simple to understand for the tested subjects as soon as these results become available. Instead of personal calls, the system updates the status and the results of the tests automatically. This aims to reduce the delay when informing testees about their results and, consequently, to slow down the virus spread.The application in this study draws on an existing tracking tool. With this open-source and browser-based online tracking system, we aim to minimize the time required to inform the tested person and the testing units (eg, hospitals or the public health care system). The system can be integrated into the clinical workflow with very modest effort and avoids excessive load to telephone hotlines.The test statuses and results are published on a secured webpage, enabling regular status checks by patients; status checks are performed without the use of smartphones, which has some importance, as smartphone usage diminishes with age. Stress tests and statistics show the performance of our software. CTest is currently running at two university hospitals in Germany-University Hospital Ulm and University Hospital Tübingen-with thousands of tests being performed each week. Results show a mean number of 10 (SD 2.8) views per testee.CTest runs independently of existing infrastructures, aims at straightforward integration, and aims for the safe transmission of information. The system is easy to use for testees. QR (Quick Response) code links allow for quick access to the test results. The mean number of views per entry indicates a reduced amount of time for both health care professionals and testees. The system is quite generic and can be extended and adapted to other communication tasks.

Graphical abstract, Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-κB signaling pathway-based scRNA-seq data of human hematopoietic stem cells (HSCs), we were able to reconstruct eight ensembles, and evaluate their dynamic behavior. Moreover, we propose a strategy to evaluate the resulting attractor patterns. Interaction graph-based features and dynamic investigations of our model ensembles provide a new perspective on the heterogeneity and mechanisms related to human HSCs aging.

Cancer is a very distressing disease, not only for the patients themselves, but also for their family members and relatives. Therefore, patients are regularly monitored to decide whether psychological treatment is necessary and applicable. However, such monitoring processes are costly in terms of required staff and time. Mobile data collection is an emerging trend in various domains. The medical and psychological field benefits from such an approach, which enables experts to quickly collect a large amount of individual health data. Mobile data collection applications enable a more holistic view of patients and assist psychologists in taking proper actions. We developed a mobile application, FeelBack, which is designed to support data collection that is based on well-known and approved psychological instruments. A controlled pilot evaluation with 60 participants provides insights into the feasibility of the developed platform and it shows the initial results. 31 of these participants received paper-based questionnaire and 29 followed the digital approach. The results reveal an increase of the overall acceptance by 58.5% in the mean when using a digital screening as compared to the paper-based. We believe that such a platform may significantly improve cancer patients’ and relatives’ psychological treatment, as available data can be used to optimize treatment., publishedVersion

Cofilin-1 (CFL1) overexpression in pancreatic cancer correlates with high invasiveness and shorter survival. Besides a well-documented role in actin remodeling, additional cellular functions of CFL1 remain poorly understood. Here, we unraveled molecular tumor-promoting functions of CFL1 in pancreatic cancer. For this purpose, we first show that a knockdown of CFL1 results in reduced growth and proliferation rates in vitro and in vivo, while apoptosis is not induced. By mechanistic modeling we were able to predict the underlying regulation. Model simulations indicate that an imbalance in actin remodeling induces overexpression and activation of CFL1 by acting on transcription factor 7-like 2 (TCF7L2) and aurora kinase A (AURKA). Moreover, we could predict that CFL1 impacts proliferation and apoptosis via the signal transducer and activator of transcription 3 (STAT3). These initial model-based regulations could be substantiated by studying protein levels in pancreatic cancer cell lines and human datasets. Finally, we identified the surface protein CD44 as a promising therapeutic target for pancreatic cancer patients with high CFL1 expression., publishedVersion

BACKGROUND Overcoming the COVID-19 crisis requires new ideas and strategies for online communication of personal medical information and patient empowerment. Rapid testing of a large number of subjects is essential for monitoring and delaying the spread of SARS-CoV-2 in order to mitigate the pandemic’s consequences. People who do not know that they are infected may not stay in quarantine and, thus, risk infecting others. Unfortunately, the massive number of COVID-19 tests performed is challenging for both laboratories and the units that conduct throat swabs and communicate the results. OBJECTIVE The goal of this study was to reduce the communication burden for health care professionals. We developed a secure and easy-to-use tracking system to report COVID-19 test results online that is simple to understand for the tested subjects as soon as these results become available. Instead of personal calls, the system updates the status and the results of the tests automatically. This aims to reduce the delay when informing testees about their results and, consequently, to slow down the virus spread. METHODS The application in this study draws on an existing tracking tool. With this open-source and browser-based online tracking system, we aim to minimize the time required to inform the tested person and the testing units (eg, hospitals or the public health care system). The system can be integrated into the clinical workflow with very modest effort and avoids excessive load to telephone hotlines. RESULTS The test statuses and results are published on a secured webpage, enabling regular status checks by patients; status checks are performed without the use of smartphones, which has some importance, as smartphone usage diminishes with age. Stress tests and statistics show the performance of our software. CTest is currently running at two university hospitals in Germany—University Hospital Ulm and University Hospital Tübingen—with thousands of tests being performed each week. Results show a mean number of 10 (SD 2.8) views per testee. CONCLUSIONS CTest runs independently of existing infrastructures, aims at straightforward integration, and aims for the safe transmission of information. The system is easy to use for testees. QR (Quick Response) code links allow for quick access to the test results. The mean number of views per entry indicates a reduced amount of time for both health care professionals and testees. The system is quite generic and can be extended and adapted to other communication tasks.

Overcoming the COVID-19 crisis requires new ideas and strategies. Rapid testing of a large number of subjects is essential to monitor, and delay, the spread of SARS-CoV-2 to mitigate the consequences of the pandemic. People not knowing that they are infected may not stay in quarantine and, thus, are a risk for infecting others. Unfortunately, the massive number of COVID-19 tests performed is challenging for both laboratories and the units that take the throat swab and have to communicate test results. Here, we present a secure tracking system (CTest) to report COVID-19 test results online as soon as they become available. The system can be integrated into the clinical workflow with very modest effort and avoids excessive load to telephone hotlines. With this open-source and browser-based online tracking system, we aim to minimize the time required to inform the tested person but also the test units, e.g. hospitals or the public healthcare system. Instead of personal calls, CTest updates the status of the test automatically when the test results are available. Test reports are published on a secured web-page enabling regular status checks also by patients not using smartphones with dedicated mobile apps which has some importance as smartphone usage diminishes with age.The source code, as well as further information to integrate CTest into the IT environment of other clinics or test-centres, are freely available from https://github.com/sysbio-bioinf/CTest under the Eclipse Public License v2.0 (EPL2).

Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models. 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology., publishedVersion

Background Overcoming the COVID-19 crisis requires new ideas and strategies for online communication of personal medical information and patient empowerment. Rapid testing of a large number of subjects is essential for monitoring and delaying the spread of SARS-CoV-2 in order to mitigate the pandemic’s consequences. People who do not know that they are infected may not stay in quarantine and, thus, risk infecting others. Unfortunately, the massive number of COVID-19 tests performed is challenging for both laboratories and the units that conduct throat swabs and communicate the results. Objective The goal of this study was to reduce the communication burden for health care professionals. We developed a secure and easy-to-use tracking system to report COVID-19 test results online that is simple to understand for the tested subjects as soon as these results become available. Instead of personal calls, the system updates the status and the results of the tests automatically. This aims to reduce the delay when informing testees about their results and, consequently, to slow down the virus spread. Methods The application in this study draws on an existing tracking tool. With this open-source and browser-based online tracking system, we aim to minimize the time required to inform the tested person and the testing units (eg, hospitals or the public health care system). The system can be integrated into the clinical workflow with very modest effort and avoids excessive load to telephone hotlines. Results The test statuses and results are published on a secured webpage, enabling regular status checks by patients; status checks are performed without the use of smartphones, which has some importance, as smartphone usage diminishes with age. Stress tests and statistics show the performance of our software. CTest is currently running at two university hospitals in Germany—University Hospital Ulm and University Hospital Tübingen—with thousands of tests being performed each week. Results show a mean number of 10 (SD 2.8) views per testee. Conclusions CTest runs independently of existing infrastructures, aims at straightforward integration, and aims for the safe transmission of information. The system is easy to use for testees. QR (Quick Response) code links allow for quick access to the test results. The mean number of views per entry indicates a reduced amount of time for both health care professionals and testees. The system is quite generic and can be extended and adapted to other communication tasks.

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior—so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.

Cells and tissues are exposed to stress from numerous sources. Senescence is a protective mechanism that prevents malignant tissue changes and constitutes a fundamental mechanism of aging. It can be accompanied by a senescence associated secretory phenotype (SASP) that causes chronic inflammation. We present a Boolean network model-based gene regulatory network of the SASP, incorporating published gene interaction data. The simulation results describe current biological knowledge. The model predicts different in-silico knockouts that prevent key SASP-mediators, IL-6 and IL-8, from getting activated upon DNA damage. The NF-κB Essential Modulator (NEMO) was the most promising in-silico knockout candidate and we were able to show its importance in the inhibition of IL-6 and IL-8 following DNA-damage in murine dermal fibroblasts in-vitro. We strengthen the speculated regulator function of the NF-κB signaling pathway in the onset and maintenance of the SASP using in-silico and in-vitro approaches. We were able to mechanistically show, that DNA damage mediated SASP triggering of IL-6 and IL-8 is mainly relayed through NF-κB, giving access to possible therapy targets for SASP-accompanied diseases., Author summary The senescence associated secretory phenotype is developed by cells undergoing permanent cell cycle arrest. This phenotype is characterized by the secretion of a variety of factors that facilitate tissue breakdown and inflammation and is therefore theorized to, in part, be causal for aging and age-related diseases. In recent years the SASP has been implicated in a variety of chronic inflammatory diseases. Due to these advances, it is imperative to better understand the dynamics of this cellular phenotype and to find ways to disrupt it. We have developed a Boolean network incorporating the major signaling pathways of the SASP that allows us to specifically investigate interactions of the pathways and genes involved. We validated our model by reliably reproducing published data on the SASP. We utilized our model to uncover components that directly control the detrimental effects of the senescence associated secretory phenotype that are largely caused by IL-6 and IL-8, two major factors of the SASP in establishing and spreading senescence as well as causing local inflammation. In subsequent in-vitro experiments, we were able to verify our computational results and could suggest NEMO as one potential target for therapy of SASP-related diseases.

Differential diagnosis and therapy of heterogeneous breast tumors poses a major clinical challenge. To address the need for a comprehensive, noninvasive strategy to define the molecular and functional profiles of tumors in vivo, we investigated a novel combination of metabolic PET and diffusion-weighted (DW)-MRI in the polyoma virus middle T antigen transgenic mouse model of breast cancer. The implementation of a voxelwise analysis for the clustering of intra- and intertumoral heterogeneity in this model resulted in a multiparametric profile based on [18F]Fluorodeoxyglucose ([18F]FDG)-PET and DW-MRI, which identified three distinct tumor phenotypes in vivo, including solid acinar, and solid nodular malignancies as well as cystic hyperplasia. To evaluate the feasibility of this approach for clinical use, we examined estrogen receptor-positive and progesterone receptor-positive breast tumors from five patient cases using DW-MRI and [18F]FDG-PET in a simultaneous PET/MRI system. The postsurgical in vivo PET/MRI data were correlated to whole-slide histology using the latter traditional diagnostic standard to define phenotype. By this approach, we showed how molecular, structural (microscopic, anatomic), and functional information could be simultaneously obtained noninvasively to identify precancerous and malignant subtypes within heterogeneous tumors. Combined with an automatized analysis, our results suggest that multiparametric molecular and functional imaging may be capable of providing comprehensive tumor profiling for noninvasive cancer diagnostics. Cancer Res; 76(18); 5512–22. ©2016 AACR.

Summary Mathematical models and their simulation are increasingly used to gain insights into cellular pathways and regulatory networks. Dynamics of regulatory factors can be modeled using Boolean networks (BNs), among others. Text-based representations of models are precise descriptions, but hard to understand and interpret. ViSiBooL aims at providing a graphical way of modeling and simulating networks. By providing visualizations of static and dynamic network properties simultaneously, it is possible to directly observe the effects of changes in the network structure on the behavior. In order to address the challenges of clear design and a user-friendly graphical user interface (GUI), ViSiBooL implements visual representations of BNs. Additionally temporal extensions of the BNs for the modeling of regulatory time delays are incorporated. The GUI of ViSiBooL allows to model, organize, simulate and visualize BNs as well as corresponding simulation results such as attractors. Attractor searches are performed in parallel to the modeling process. Hence, changes in the network behavior are visualized at the same time. Availability and Implementation ViSiBooL (Java 8) is freely available at http://sysbio.uni-ulm.de/?Software:ViSiBooL.

Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF- κ B signaling is a crucial pathway in the process of aging. The NF- κ B system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes—for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF- κ B signaling in aged organisms in comparison to young ones.