DOI: 10.5061/dryad.sqv9s4n9f
title: Data from: Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks

description: Many genes and signaling pathways within plant and animal taxa drive the
 expression of multiple organismal traits. This form of genetic pleiotropy
 instigates trade-offs among life-history traits if a mutation in the
 pleiotropic gene improves the fitness contribution of one trait at the
 expense of another. Whether or not pleiotropy gives rise to conflict among
 traits, however, likely depends on the resource costs and timing of trait
 deployment during organismal development. To investigate factors that
 could influence the evolutionary maintenance of pleiotropy in gene
 networks, we developed an agent-based model of co-evolution between
 parasites and hosts. Hosts comprise signaling networks that must
 faithfully complete a developmental program while also defending against
 parasites, and trait signaling networks could be independent or share a
 pleiotropic component as they evolved to improve host fitness. We found
 that hosts with independent developmental and immune networks were
 significantly more fit than hosts with pleiotropic networks when traits
 were deployed asynchronously during development. When host genotypes
 directly competed against each other, however, pleiotropic hosts were
 victorious regardless of trait synchrony because the pleiotropic networks
 were more robust to parasite manipulation, potentially explaining the
 abundance of pleiotropy in immune systems despite its contribution to life
 history trade-offs.
description: # Data from: Pleiotropy alleviates the fitness costs associated with
 resource allocation trade-offs in immune signaling networks
 [https://doi.org/10.5061/dryad.sqv9s4n9f](https://doi.org/10.5061/dryad.sqv9s4n9f) The file submitted here contains the datasets used to generate the figures in the paper "Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signaling networks". Code used to generate figures using this data is available at [https://github.com/Reese-Martin/Pleiotropy_And_Resource_Allocation](https://github.com/Reese-Martin/Pleiotropy_And_Resource_Allocation) ## Description of the data and file structure The data is currently in a zip file, expanding this file will show several individual data files that contain population fitness information, host signaling networks, and other associated data. All data are stored as .jld2 files which can be saved and loaded using the FileIO package in the Juila programming language. each data file is a multidimensional array of float64 values. 3D arrays have simulations on axis 1, individuals on axis 2 and generations on axis 3, so to see data of the first individual in the population throughout simulation one you would index it as data[1,1,:]. 2D arrays have population mean values from each simulation on axis 1, and the generation on axis 2. To see data for the 3rd simulations mean fitness at generation 50 you would index it as data[3,50]. Note: julia indexing starts at 1 not 0. Data files in the zip'd archive are named with the following general naming convention 50_PercentInf_EC_Limit_100_FitEff_Balanced_TNTE_Synch_HostFit.jld2 which can be read as follows **50_PercentInf** indicates that 50 percent of hosts are infected in each generation **EC_Limit_100** indicates that each time step of signaling network protein activity was limited to a total increase of 100 percentage points of active protein. (i.e. protein 1 goes from .5-&gt;.75 and protein 2 goes from .25-&gt;1.00 but no more activity is allowed) **EC_Limit_10** indicates the limit was 10 percentage points, and **Variable** indicates that the simulations altered between the 100 and 10 limits on 50 timestep intervals. **FitEff_Balanced** indicates that there were balanced fitness effects between the immune, parasitic, and developmental fitness components **TNTE** is read as "Two Networks Two Effectors", showing that these simulations were for the non-pleiotropic hosts. **TNOE** is "Two Networks One Effector", the label for the pleiotropic hosts **TNOE_vs_TNTE** indicates that the simulations were competitions between the pleiotropic and non-pleiotropic hosts **EQGens_500** indicates that there were 500 equilibration (or burn in) generations before competition began. **Synch** indicates that developmental and immune signaling was synchronous. **Asynch** indicates that developmental and immune signaling was asynchronous. **HostFit** indicates that the data stored was mean Host fitness for each generation in a simulation on the columns, with rows storing separate simulations. **Nets** store the most populous host network from the final generation of a simulation. **NetSizes** store the immune and developmental host network sizes, necessary for network based analysis. **PerPopTE** stores the Percentage of the Population that had Two Effectors with each simulation on a row and each generation in columns. 0s indicate One effector host wins, 1s indicate Two Effector host wins. Look for the first 1 or the first 0 to determine a win, since competitions did not continue after one species was driven to extinction.  **HostFitOE** one effector host mean population fitness with each simulation on a row and each generation in columns. **HostFitTE** two effector host mean population fitness with each simulation on a row and each generation in columns. ## Sharing/Access information Data was generated using code from the following repository * [https://github.com/Reese-Martin/Pleiotropy_And_Resource_Allocation](https://github.com/Reese-Martin/Pleiotropy_And_Resource_Allocation) ## Code/Software All code used in this project was written in the Julia programming language. Please see the linked github repo for a more detailed description of the code.