Anna Ritz

Cells coordinate diverse biological processes through interactions among thousands of molecules, but mapping these interactions comprehensively and systematically remains an open problem. Pathway reconstruction algorithms address this problem by linking molecules of interest, identified from high-throughput omics experiments, using prior knowledge encoded as background interaction networks. This process recovers intermediate molecules and interactions that were not directly measured in the experimental data but plausibly connect the observed molecules. It generates testable hypotheses about interactions that drive cell behavior and informs the choice of follow-up experiments. Many algorithms have been created over decades, each optimizing different computational objectives and relying on different assumptions. The resulting heterogeneity has made benchmarking challenging, limiting systematic comparisons. Therefore, selecting an algorithm for a given biological context remains a non-trivial and poorly informed task. This registered report presents a large-scale benchmark of pathway reconstruction algorithms, evaluating 14 algorithms across 822 datasets from four biological settings. To enable this benchmark, we introduce Signaling Pathway Reconstruction Analysis Streamliner (SPRAS), which standardizes algorithm inputs, outputs, and execution into a formal framework, enabling systematic comparison that was previously infeasible. We will assess each algorithm on reconstruction performance against gold standard pathways, algorithm similarity, and computational performance across different biological contexts. Together, these evaluations will provide quantitative evidence for understanding pathway reconstruction algorithm behavior and guiding algorithm selection.

Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a protein’s position in a network in the context of a specific biological process or pathway. We developed ProteinWeaver, a web-based tool designed to visualize and analyze non-human protein interaction networks by integrating known biological functions. ProteinWeaver provides users with an intuitive interface to situate a user-specified protein in a user-provided biological context (as a Gene Ontology term) in seven model organisms. ProteinWeaver also reports the presence of physical and regulatory network motifs within the queried subnetwork and statistics about the protein’s distance to the biological process or pathway within the network. These insights can help researchers generate testable hypotheses about the protein’s potential role in the process or pathway under study. Two cell biology case studies demonstrate ProteinWeaver’s potential to generate hypotheses from the queried subnetworks. ProteinWeaver is available at https://proteinweaver.reedcompbio.org/.

Graphs are powerful tools for modeling and analyzing molecular interaction networks. Graphs typically represent either undirected physical interactions or directed regulatory relationships, which can obscure a particular protein's functional context. Graphlets can describe local topologies and patterns within graphs, and combining physical and regulatory interactions offer new graphlet configurations that can provide biological insights. We present GRPhIN, a tool for characterizing graphlets and protein roles within graphlets in mixed physical and regulatory interaction networks. We describe the graphlets of mixed networks in B. subtilis , C. elegans , D. melanogaster , D. rerio , and S. cerevisiae and examine local topologies of proteins and subnetworks related to the oxidative stress response pathway. We found a number of graphlets that were abundant in all species, specific node positions (orbits) within graphlets that were over-represented in stress-associated proteins, and rarely-occurring graphlets that were enriched in stress subnetworks. These results showcase the potential for using graphlets in mixed physical and regulatory interaction networks to identify new patterns beyond a single interaction type.

Motivation: Graphs are powerful tools for modeling and analyzing molecular interaction networks. Graphs typically represent either undirected physical interactions or directed regulatory relationships, which can obscure a particular protein’s functional context. Graphlets can describe local topologies and patterns within graphs, and combining physical and regulatory interactions offer new graphlet configurations that can provide biological insights. Results: We present GRPhIN, a tool for characterizing graphlets and protein roles within graphlets in mixed physical and regulatory interaction networks. We describe the graphlets of mixed networks in Bacillus subtilis, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, and Saccharomyces cerevisiae and examine local topologies of proteins and subnetworks related to the oxidative stress response pathway. We found a number of graphlets that were abundant in all species, specific node positions (orbits) within graphlets that were overrepresented in stress-associated proteins, and rarely-occurring graphlets that were overrepresented in oxidative stress subnetworks. These results showcase the potential for using graphlets in mixed physical and regulatory interaction networks to identify new patterns beyond a single interaction type. Availability and implementation: GRPhIN is available at https://github.com/Reed-CompBio/grphin.

Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a protein's position in a network in the context of a specific biological process or pathway. We developed ProteinWeaver, a web-based tool designed to visualize and analyze non-human protein interaction networks by integrating known biological functions. ProteinWeaver provides users with an intuitive interface to situate a user-specified protein in a user-provided biological context (as a Gene Ontology term) in five model organisms. ProteinWeaver also reports the presence of physical and regulatory network motifs within the queried subnetwork and statistics about the protein's distance to the biological process or pathway within the network. These insights can help researchers generate testable hypotheses about the protein's potential role in the process or pathway under study. Two cell biology case studies demonstrate ProteinWeaver's potential to generate hypotheses from the queried subnetworks. ProteinWeaver is available at https://proteinweaver.reedcompbio.org/.

Protein interactions and cellular responses are fundamental pillars of molecular systems biology. Decoding these complex signaling pathways requires advanced computational methods. One promising direction of algorithm development is using graph algorithms to identify proteins involved in signaling pathways. Despite the availability of tools, many researchers grapple with software and user experience constraints. In response, we have developed the Signaling Pathway Reconstruction Analysis Streamliner (SPRAS), a robust containerized framework that enables users to easily reconstruct signaling pathways by connecting proteins of interest within molecular interaction networks. It seamlessly integrates graph algorithms designed for pathway reconstruction with downstream visualization and clustering analysis. We contribute and integrate three random-walk-based algorithms to SPRAS, including one algorithm we developed for large networks and two other algorithms that appear in the literature. Random walk approaches have been highly successful in predicting candidate proteins involved in a signaling pathway, and integrating them into SPRAS will greatly expand the framework's ability for pathway reconstruction. We illustrate their importance by using the random walk algorithms now available in SPRAS to explore potential proteins involved in cell-cell fusion in flies. In our computational experiments, five fly proteins appeared in multiple reconstructed pathways, suggesting a potential role for them in cell-cell fusion. With the addition of these new algorithms, SPRAS will become an essential tool for unraveling the mysteries of biological interactions.

Networks provide a meaningful way to represent and analyze complex biological information, but the methodological details of network-based tools are often described for a technical audience. Graphery is a hands-on tutorial webserver designed to help biological researchers understand the fundamental concepts behind commonly-used graph algorithms. Each tutorial describes a graph concept along with executable Python code that visualizes the concept in a code view and a graph view. Graphery tutorials help researchers understand graph statistics (such as degree distribution and network modularity) and classic graph algorithms (such as shortest paths and random walks). Users navigate each tutorial using their choice of real-world biological networks, ranging in scale from molecular interaction graphs to ecological networks. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Discipline-focused tutorials will be essential to help researchers interpret their biological data. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Availability: Graphery is available at https://graphery.reedcompbio.org/.

Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation: Not applicable.

The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)-induced apical constriction that occurs in Drosophila. The binding of Fog to its cognizant receptors Mist/Smog results in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered role in its regulation. We developed a computational method to predict unidentified candidate NMII regulators using a network of pairwise protein–protein interactions called an interactome. We first constructed a Drosophila interactome of over 500,000 protein–protein interactions from several databases that curate high-throughput experiments. Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signaling. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay in Drosophila S2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using this assay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog pathway.