MOTIVATION: Signalling entropy measures the uncertainty or randomness in the signalling pathways of a biological system. It reflects the complexity and variability of protein interactions and can indicate how information is processed within cells. Higher signalling entropy often indicates a more dynamic and adaptive state, whereas lower entropy may imply a more stable and less responsive condition. Estimating signalling entropy has become a valuable method for studying and understanding the complexity of biological processes. This measure has the potential to shed valuable insights into various phenomena, including the mechanisms behind cell fate decisions, drug resistance, and disease progression. To examine the molecular changes within a system, signalling entropy is quantified through the integration of expression measurements and protein interaction networks. Experimental and computational issues, such as false positives and additional noise, can all compromise the accuracy of protein interaction networks. Correction methods can be used to mitigate spurious results, correct for experimental bias, and integrate data from multiple sources. However, to date, the effect of such approaches on entropy calculations, together with the impact of different underlying networks, has yet to be evaluated. RESULTS: Here, we investigate how the topology of distinct protein interaction networks can alter the entropy calculation. We examine the entropy derived from different protein interaction networks. Additionally, we systematically evaluate different correction strategies, outlining their benefits and drawbacks along with identifying the most effective approaches for specific types of data and biological scenarios. This protocol outlines how to optimize the reliability of entropy calculations and ultimately leads to a deeper comprehension of biological processes and disease mechanisms.

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.