Below is a reverse-chronological order of computational resources that we have developed over the years. Note that we also keep an active GitHub repository you might want to check. Most material (if not all) available there is cross-listed here.
Micodymora is a python package allowing the simulation of Ordinary Differential Equations (ODE) models of microbial population dynamics, as presented in the pre-print "Delattre et al, 2019" (see publications/pre print section). For further details and to access the package, please visit our GitHub page.
14. Source code for MetQy.
This is the R implementation of MetQy, a KEGG query tool, presented in the pre-print "Martinez-Vernon et al, 2017" (see pre-print and articles sections). The following link is for the zipped file, which one can install within the R environment. See separate GitHub page for MetQy for further details on installation and usage.
MetQy ZIP file
13. ViraNet. The host-virus modelling tool and associated files.
This is the Phyton implementation of the host-virus modelling presented in the pre-print "Aller et al, 2017" (see pre-print and articles sections). The links are for the actual model creation tool ViraNet and few files associated with the said pre-print: (i) a MATLAB implementation of a host-virus model, and (ii) the actual NCBI files for the viruses, that were used in "Aller et. al. 2017".
ViraNet ZIP file | Assoc. Files
12. EvoFBA - Source Code for Simulating Evolution of Constraint Based Models of Cellular Metabolism
This is a MATLAB implementation of the evolutionary and ecological dynamics simulations of growing E.coli populations as presented in "Grosskopf T, et al, BMC Evol Bio, 2016". These simulations involve mimicking growth dynamics of E.coli cells using dynamical constraint-based modeling of cellular metabolism and mimicking ecological dynamics through differential equations describing concentrations of media components. See the associated paper for further details.
11. Source Code for a Minimal Signaling Network Motif Capable of Ultrasensitive and Adaptive Dynamics
This is a MATLAB implementation of a minimalist signaling network motif presented in "Song F, Ollivier J, and Soyer OS, PLCB, 2016". The motif is displayed on Figure 4A of this paper and consists of a signaling cycle and a sequestering protein that can act on the kinase and the phosphatase. Depending on the kinetic parameters, this system can display either ultrasensitivity (in terms of its dose-response curve) or adaptive dynamics (in terms of its time response to a perturbation). See the associated paper for further details.
10. Source Code for Enumeration of Microbial Growth Supporting Reactions from Glucose
This code is used for one of the analyses presented in "Grokopf T. and Soyer O.S., ISME Journal, 2016". It is written as a MATLAB script and makes use of an enumeration algorithm to compose biochemical reactions starting with Glucose. The associated Gibbs free energy change associated with these reactions under standard conditions is calculated and the reactions are presented and discussed in the context of supporting microbial growth and resulting diversity. See corresponding paper for further details.
9. Source Code for BioJazz
BioJazz is an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with theoretically unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use.
8. Source Code for Metabolic Tinker
Tinker is a metabolic pathway design/search tool. The current Tinker server is under maintainance, please download the source code and run it on personal desktop. Tinker source code in Java can be downloaded and run on a personal computer or adapted for specific applications. The program uses indexed data to reduce computational burden; however, the computer should have at least 1 GB of memory for the best performance.
7. Source code for evolutionary simulations of a single feedback circuit under fluctuating selection.
This code is used for the analysis presented in "Kuwahara H. and Soyer O.S., Molecular Systems Biology, 8:564 (2012)". It is written in C language and makes use of the Gillespie algorithm to simulate stochastic models of a single feedback circuit in virtual bacteria. These bacteria are evolved in silico under fluctuating selection. In this version of the simulations, all parameters of the system are evolvable. See corresponding paper for details.
6. Source code for analysing the effects of gene duplication on the dynamics of a signaling network.
This code is used for the analysis presented in "Creevey J. and Soyer O.S Journal of Evolutionary Biology, 23, 11 (2010)". It is written in Java language and makes use of a publicly available library for solving ODEs with Runga-Kutta method.
5. A generic model for two-component signaling relays.
The provided file includes (along with additional material) an human-readable and executable description of a generic model for two-component relays as seen in prokaryotes and lower eukaryotes. The model given here has 4-layers but the model structure allows easy extendability in the number of relay layers. For more information and analysis, consult the related publication: "Csikasz-Nagy A., Cardelli L., Soyer O. S. Journal of Royal Society Interface. Epub 11 Aug 2010".
Model and Supplementary Analyses
4. Sample movie showing evolution of taxis responses in bacteria.
This movie shows distribution of virtual bacteria over generations as their signaling network underlying taxis responses evolve. It is generated from simulations used for the analysis presented in "Goldstein R.A., Soyer O.S. PLoS Computational Biology, 4, 5:e1000084 (2008)".
3. Source code for simulating the evolution of signaling networks underlying taxis responses in bacteria.
This code is used for the analysis presented in "Goldstein R.A., Soyer O.S. PLoS Computational Biology, 4, 5:e1000084 (2008)". It is written in Java language and makes use of a publicly available library for solving ODEs with Runga-Kutta method.
2. Source code for simulating the evolution of signaling networks under parasite interference.
This code is used for the analysis presented in "Salath M., Soyer O.S. Molecular Systems Biology, 4, 202 (2008)". It is written in Java language and makes use of a publicly available library for solving ODEs with Runga-Kutta method.
1. Response dynamics in all possible signalling networks of size three.
The HTML links takes you to data presented in "Soyer O.S., Salath M., Bonhoeffer S. Journal of Theoretical Biology, 238(2) (2006)". For each network topology we analyzed response dynamics under 1000 different realizations of the network. Each realization had randomly chosen kinetic rates for the reactions. The response dynamics of these realizations are classified into 6 different categories (columns on the table). Different rows correspond to different models, which employ different assumptions on protein relaxation reactions.