cellnition 0.2.0

Open-source cross-platform biology simulator analyzing gene regulatory networks (GRNs) with Network Finite State Machines (NFSMs).

Cellnition is an open source simulator to create and analyze Network Finite State Machines (NFSMs) from gene regulatory network (GRN) models.

Regulatory networks such as GRNs preside over complex phenomena in biological systems, yet given a specific regulatory network, how do we know what it’s capable of doing?

Cellnition treats regulatory networks as analogue computers, where NFSMs map the sequential logic inherent in the network as a dissipative dynamic system. As an extension and improvement upon attractor landscape analysis, NFSMs reveal the analogue computing operations inherent in GRNs, allowing for identification of associated “intelligent behaviors”. NFSMs capture the “analog programming” of GRNs, providing clear identification of:

• Interventions that can induce transitions between stable states (e.g. from “diseased” to “healthy”)

• Identification of path-dependencies, representing stable changes occurring after a transient intervention is applied (e.g. evaluating if a transient treatment with pharmacological agent can permanently heal a condition)

• Identification of inducible cycles of behavior that take the system through a complex multi-phase process (e.g. wound healing).

NFSMs have a range of applications, including the identification of strategies to renormalize cancer (see Tutorial 2).

Read more about Cellnition’s NFSMs in our pre-print publication: Harnessing the Analogue Computing Power of Regulatory Networks with the Regulatory Network Machine.

Cellnition is portably implemented in Python, continuously stress-tested via GitHub Actions × tox × pytest × Codecov, and licensed under a non-commercial use, open source APACHE license with Tufts Open Source License Rider v.1. For maintainability, cellnition officially supports only the most recently released version of CPython.

Contents

• Install

• Features

• Tutorials

• License

Install

Cellnition is easily installable with pip, the standard package installer officially bundled with Python:

pip3 install cellnition

Features

Cellnition embodies a range of functionality, including:

• Work with regulatory networks imported from Cellnition’s network-library, use Cellnition to procedurally generate regulatory networks with random or scale-free degree distributions, or import your own user-defined regulatory networks as directed graphs with activating or inhibiting edge characteristics (see Tutorial 1 and Tutorial 2 for some examples).

• Analyze and characterize regulatory network graphs with a variety of metrics (see the characterize_graph method and Tutorial 1 and Tutorial 2).

• Use directed graph representations of regulatory networks to build fully-continuous, differential equation based simulators of network dynamics (see ProbabilityNet class and Tutorial 1).

• Use directed graph representations of regulatory networks to build logic equation based Boolean simulators of network dynamics (see BooleanNet class and Tutorial 2).

• Explore regulatory network dynamics with comprehensive equillibrium state search and characterization capabilities, along with temporal simulators (see Tutorial 1 and Tutorial 2 for some examples).

• Create simulated datasets, including simulation of automated gene-knockout experiments for a continuous regulatory network model (see GeneKnockout class).

• Generate Network Finite State Machines (NFSMs) for continuous models (see Tutorial 1) or for Boolean models (see Tutorial 2).

• Create and export a variety of plots and visualizations, including of the regulatory network analytic equations, regulatory network directed graphs, heatmaps of gene expressions in equilibrium states, gene expressions in temporal simulations, and depictions of the general and event-driven NFSMs (see Tutorial 1 and Tutorial 2 for some examples).

Tutorials

Cellnition tutorials are available as Jupyter Notebooks:

• Tutorial 1 : Create NFSMs from a continuous, differential-equation based GRN model.

• Tutorial 2 : Create NFSMs from a Boolean, logic-equation based GRN model.

License

Cellnition is non-commerical use, open source software licensed under an Apache 2.0 license with Tufts Open Source License Rider v.1, restricting use to academic purposes only.