Theory predicts that (i) vertical transmission of parasites (i.e. when they are passed directly from a host to its offspring) selects for benign association with the host and that (ii) vertically transmitted parasites that lower their hosts' fitness cannot persist if they are not able to infect horizontally (i.e. contagiously) other host individuals in the population. In this paper, we develop a mathematical model to examine whether mutualism is a prerequisite for persistence of exclusively vertically transmitted (from maternal plant to offspring via seeds) fungal endophytes in structured grass metapopulations. Interestingly, endophyte survival does not require plant mutualism, even in a metapopulation consisting of qualitatively identical patches, if vertical transmission of the fungus is perfect, i.e. if all established seedlings in offspring of the endophyte-infected plant are infected. In more realistic situations, when the metapopulation consists of qualitatively different patches, endophyte-infected plants may persist at the metapopulation level even if the vertical transmission is imperfect (due to hyphae inviability or failure to grow into all seeds) and the endophyte decreases the host grass fitness in certain environments. These results have biological importance because they (i) question the requirement of a mutualistic nature in exclusively vertically transmitted symbionts and (ii) emphasize the importance of habitat diversity in relation to symbiont success in vertical transmission.

Motivation There is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g., drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets. Results We introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein-protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI, and InnateDB, directed interaction data from InnateDB, Omnipath, and SIGNOR, cell-line data from COLT and DepMap, and drug-target data from DrugBank. Availability The application and data are available online at https://netcontrol.combio.org/. The source code is available at https://github.com/Vilksar/NetControl4BioMed under an MIT license.

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