MOTIVATION: Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community. RESULTS: Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer. AVAILABILITY AND IMPLEMENTATION: https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/.

MOTIVATION: Biological 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. RESULTS: Initial 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 IMPLEMENTATION: Source code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In his letter to the editor, Luis Rocha addresses the concern that other researchers might be discouraged from further investigation of dynamic analyses in Boolean networks based on a statement from our recently published manuscript (Weidner et al., 2021). In particular, the author refers to a phrase in our abstract on the feasibility of dynamic investigations in large Boolean models. We value the discussion on this crucial topic in the field of Boolean networks. However, we kindly disagree on the interpretation of the respective parts of our manuscript. First, we want to refer to the point addressed in our abstract. Here, we state: ‘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’. In addition, in our introduction, we report that: ‘Nevertheless, also for BNs, it holds that dynamic complexity scales exponentially with network size, again limiting the possibility of complete dynamic investigations’. This sentence comes with a reference explaining the feasibility of exhaustive attractor computation in Boolean network models. Here, we are entirely in line with the view given, and this is also what is elaborated throughout our manuscript. Foremost, when it comes to screening potential interventions targets using Boolean networks, dynamic analyses in the sense of exhaustive screening become very complex with a growing number of compounds in the model and potential targets or even combinations of targets. Our method does not aim to be a replacement of dynamic analysis but a step in the screening for intervention targets, scaling down the number of interventions to screen. Subsequently, the identified targets can be evaluated by different perturbation analyses based on network dynamics, such as more detailed studies of the attractor landscape (Müssel et al., 2010), or an automated screening (Schwab and Kestler, 2018). Especially when adding another layer of complexity, such as with large reconstructed networks or even populations of those (Schwab et al., 2021), detailed intervention screening on top of attractor evaluation becomes complex, and interaction graph-based pre-screening methods become helpful. Financial Support: none declared. Conflict of Interest: none declared.

MOTIVATION: Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients' samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches. RESULTS: Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar ('analysis and visualization of alteration data') that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection. AVAILABILITY: AVAtar is available at: https://github.com/sysbio-bioinf/avatar. CONTACT: [email protected], phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502.

In the originally published version of this manuscript, there was an error in co-author Thomas K. Hoffmann's name. The full name should read: “Thomas K. Hoffmann” instead of “Thomas K. Hoffman”. This error has now been corrected online.

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.

Abstract Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.

BACKGROUND: The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. RESULTS: Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. CONCLUSION: Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.