Tools and Software

Software

Please properly cite the following software and papers when using or referencing them in any published work.

• Analyzing causal emergence in networks
• Annette
• Baccountant
• BETSE
• BETSEE
• CAIM
• CalculIon
• Cellular
• Cellular competency evolution model
• CompetitionAsCoordination
• EDeN
• EDeN interface
• FieldSHIFT
• GABEE
• Growing Neural Cellular Automata
• Inform
• Limbform
• MorphoBayes
• MorphoPsy
• Neato
• Neoblast competition simulation
• Planarian regeneration simulation software
• Planform
• PLIMBO
• PyInform
• SBMLtoODEjax
• Scale-free Cognition
• Somatic cells protect stem cells from a lethal environment
• Sorting Algorithms as Basal Cognition

Analyzing causal emergence in networks

• By Erik Hoel (Tufts University) and Brennan Klein (Northeastern University), 2019
• URL
• Klein,B., and Hoel, E. (2020), The Emergence of Informative Higher Scales in Complex Networks, Complexity, 2020: 8932526, doi: 10.1155/2020/8932526 
• Hoel, E., and Levin, M. (2020), Emergence of Informative Higher Scales in Biological Systems: a computational toolkit for optimal prediction and control, Communicative & Integrative Biology, 13(1): 108-118  

Annette

• A recurrent artificial neural network library
• By Douglas Moore (Tufts University/ASU), Michael Levin (Tufts University), and Sara Walker (Arizona State University), 2016
  
• URL

Baccountant

• A software package for quantifying bacterial colony growth
• By Cuong Nguyen (Allen Discovery Center at Tufts University) and Michael Levin (Tufts University), 2017
• Zip file for Windows or Mac and Video tutorials

BETSE

• BioElectric Tissue Simulation Engine
• By Alexis Pietak (Allen Discovery Center at Tufts University) and Michael Levin (Tufts University), 2016
  
• URL
• Pietak, A., and Levin, M. (2016), Exploring Instructive Physiological Signaling with the Bioelectric Tissue Simulation Engine, Frontiers in Bioengineering and Biotechnology, 4: 55 

BETSEE

• A front-end GUI for the BETSE package
• By Cecil Curry
  
• URL

CAIM

• Information analysis of imaging data
• By Douglas Moore (Tufts University/ASU) and Patrick McMillen, 2019
  
• URL
• McMillen, P., and Levin, M. (2022), Information Theory as an experimental tool for integrating disparate biophysical signaling modules, International Journal of Molecular Sciences, 23(17): 9580 

CalculIon

• Software utility for calculating cellular bioelectric properties
• By Alexis Pietak (Allen Discovery Center at Tufts University), 2023
  
• URL and explanatory video
• A paper supplying the details of our model emphasizing the slow changes that shape the electrochemical ion gradients fundamental to bioelectricity will be available for download soon.

Cellular

• A simple, extensible cellular automaton library
• By Douglas Moore (Tufts University/ASU), Michael Levin (Tufts University), and Sara Walker (Arizona State University), 2016
  
• URL

Cellular competency evolution model

• By Lakshwin Sreesha (Université Paris Cité/Tufts University), 2023
  
• URL
• Shreesha, L., and Levin, M. (2023), Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model, Entropy, 25(1): 131

CompetitionAsCoordination

• By Peter Smiley (Tufts University)
• URL
• Smiley, P., and Levin, M. (2022), Competition for Finite Resources as Coordination Mechanism for Morphogenesis: an evolutionary algorithm study of digital embryogeny, BioSystems,221: 104762

EDeN

• Electroceutical Design Environment
• By Cassandra D. M. Churchill (University of Alberta), Philip Winter (University of Alberta), Jack A. Tuszynski (University of Alberta), and Michael Levin (Tufts University), 2021
• URL
• Churchill, C. D. M., Winter, P., Tuszynski, J. A., and Levin, M. (2019), EDEn - Electroceutical Design Environment: Ion Channel Tissue Expression Database with Small Molecule Modulators, iScience,11: 42-56 

EDeN interface

• By Stefano Rosa
• URL

FieldSHIFT

• Translate science from one domain to another
• By Thomas O'Brien (Streams) and Joel Stremmel (Streams), 2023
• URL
• O’Brien, T., Stremmel, J., Pio-Lopez, L., McMillen, P., Rasmussen-Ivey, C., and Levin, M. (2023), Machine Learning for Hypothesis Generation in Biology and Medicine: Exploring the latent space of neuroscience and developmental bioelectricity, OSF Preprints, doi:10.31219/osf.io/269e5

GABEE

• Genetic Algorithm for Bio-Electric Exploration
• By Micah Brodsky (Tufts University/MIT), Alexis Pietak (Allen Discovery Center at Tufts University), and Michael Levin (Tufts University), 2017
  
• URL
• Brodsky, M., and Levin, M. (2018), From Physics to Pattern: Uncovering Pattern Formation in Tissue Electrophysiology, in T. Ikegami, N. Virgo, O. Witkowski, M. Oka, R. Suzuki and H. Iizuka (Eds.), ALIFE 2018: The 2018 Conference on Artificial Life. MIT Press: Tokyo, p. 351-358

Growing Neural Cellular Automata

• Differentiable Model of Morphogenesis
• By Alexander Mordvintsev (Google), Eyvind Niklasson (Google), Ettore Randazzo (Google) and Michael Levin (Tufts University), 2020
  
• URL

Inform

• A Cross Platform C library for Information Analysis of Dynamical Systems
• By Douglas Moore (Tufts University/ASU), Michael Levin (Tufts University), and Sara Walker (Arizona State University), 2016
  
• URL
• Moore, D. G., Valentini, G., Walker, S. I., and Levin, M. (2018), Inform: Efficient Information-Theoretic Analysis of Collective Behaviors, Frontiers in Robotics and AI, 5: 60
• Moore, D. G., Valentini, G., Walker, S. I., and Levin, M. (2018), Inform: a toolkit for information-theoretic analysis of complex systems, Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), p. 3258-3265

Limbform

• Limb regeneration expert system
• By Daniel Lobo (Tufts University/University of Maryland)
  
• URL
• Lobo, D., Feldman, E. B., Shah, M., Malone, T. J., and Levin, M. (2014), Limbform: a functional ontology-based database of limb regeneration experiments, Bioinformatics, 30(24): 3598-3600
• Lobo, D., Feldman, E. B., Shah, M., Malone, T., and Levin, M. (2014), A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration, Regeneration, 1(2): 37-56

MorphoBayes

• Bayesian modeling of morphogenesis
• By Franz Kuchling (Tufts University), 2019
  
• URL
• Kuchling, F., Friston, K., Georgiev, G., and Levin, M. (2020), Morphogenesis as Bayesian Inference: a Variational Approach to Pattern Formation and Control in Complex Biological Systems, Physics of Life Reviews, 33: 88-108
• Kuchling, F., Friston, K., Georgiev, G., and Levin, M. (2020), Integrating Variational Approaches to Pattern Formation into a Deeper Physics: Reply to comments on "Morphogenesis as Bayesian Inference: A Variational Approach to Pattern Formation and Manipulation in Complex Biological Systems", Physics of Life Reviews, 33: 125-128

MorphoPsy

• By Léo Pio-Lopez (Tufts University), Franz Kuchling (Tufts University), and Giovanni Pezzulo (National Research Council, Rome), 2022
  
• URL
• Pio-Lopez, L., Kuchling, F., Tung, A., Pezzulo, G., and Levin, M. (2022), Active Inference, Morphogenesis, and Computational Psychiatry, Frontiers in Computational Neuroscience, 16: 988977

Neato

• An implementation of Ken Stanley’s NeuroEvolution of Augmenting Topologies
• By Douglas Moore (Tufts University/ASU), Michael Levin (Tufts University), and Sara Walker (Arizona State University), 2016
  
• URL

Neoblast competition simulation

• By Chris Fields and Michael Levin (Tufts University), 2018
  
• URL
• Fields, C., and Levin, M. (2018), Are planaria individuals? What regenerative biology is telling us about the nature of multicellularity, Evolutionary Biology, 45(3): 237-247

Planarian regeneration simulation software

• By Daniel Lobo (Tufts University/University of Maryland)
  
• URL
• Lobo, D., and Levin, M. (2015), Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration, PLoS Computational Biology, 11(6): e1004295

Planform

• Planarian regeneration expert system
• By Daniel Lobo (Tufts University/University of Maryland)
  
• URL
• Lobo, D., Malone, T. J., and Levin, M. (2013), Towards a bioinformatics of patterning: a computational approach to understanding regulative morphogenesis, Biology Open, 2(2): 156-169
• Lobo, D., Malone, T. J., and Levin, M. (2013), Planform: an application and database of graph-encoded planarian regenerative experiments, Bioinformatics, 29(8): 1098-1100
• Lobo, D., Beane, W., and Levin, M. (2012), Modeling planarian regeneration: a primer for reverse-engineering the worm, PLoS Computational Biology, 8(4): e1002481

PLIMBO

• Planarian Interface for Modelling Body Organization
• Alexis Pietak (Allen Discovery Center at Tufts University) and Michael Levin (Tufts University), 2019
  
• URL

PyInform

• A Python Wrapper for the Inform Information Analysis Library
• By Douglas Moore (Tufts University/ASU), Michael Levin (Tufts University), and Sara Walker (Arizona State University), 2016
  
• URL

SBMLtoODEjax

• Automatically parse and convert SBML models into python models written end-to-end in JAX
• Mayalen Etcheverry (INRIA, University of Bordeaux, Talence, France), 2023
  
• URL
• Etcheverry, M., Levin, M., Moulin-Frier, C., and Oudeyer, P.-Y. (2023), SBMLtoODEjax: efficient simulation and optimization of ODE SBML models in JAX, arXiv, doi:10.48550/arXiv.2307.08452

Scale-free Cognition

• A recurrent artificial neural network library
• Léo Pio-Lopez (Tufts University), 2022
  
• URL
• Pio-Lopez, L., Bischof, J., LaPalme, J. V., and Levin, M. (2023), The scaling of goals via homeostasis: an evolutionary simulation, experiment and analysis, Interface Focus, 13(3): 20220072 

Somatic cells protect stem cells from a lethal environment

• By Chris Fields and Michael Levin (Tufts University), 2018
  
• URL
• Fields, C., and Levin, M. (2019), Somatic Multicellularity as a Satisficing Solution to the Prediction-Error Minimization Problem, Communicative & Integrative Biology, 12(1): 119-132

Sorting Algorithms as Basal Cognition

• By Taining Zhang (Tufts University), 2023
  
• URL
• Zhang, T., Goldstein, A., and Levin, M. (2023), Classical Sorting Algorithms as a Model of Morphogenesis: self-sorting arrays reveal unexpected competencies in a minimal model of basal intelligence, OSF Preprints, doi:10.31219/osf.io/e5d4u