Mechanistic interactions as the origin of modularity in biological networks

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements - such as the species in an ecosystem - as finite-state machines (FSMs) and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity likely emerges for free. We further find that the more modular an interact..., , , # Mechanistic interactions as the origin of modularity in biological networks - Code & Data

The repository contains the Python code (model and analysis) and data for:

Wechsler, D., and J. Bascompte. Mechanistic interactions as the origin of modularity in biological networks.
*unpublished*.

```
modularity_in_biological_networks.zip
├── core/ Model code and measures
│ ├── basic_measures.py Functions to compute e.g. connectance
│ ├── finite_state_machine.py Finite-state machine class
│ ├── fsm_minimization.py Implementation of the procedure to minimize a set of FSM (used to compute trait complexity)
│ ├── modularity.py Functions to compute modularity (invokes R package BiRewire)
│ ├── modularity_maximization.py Algorithm to find the maximal modularity of a network with a given connectance or degree sequence (see sections S3 and S4 in supplementary material).
│ ├── network_evolution.py Evolves networks ...