Resources
Quick links: Computational Resources, Experimental Resources, Education and Outreach, Review Excerpts
Computational Resources - see also our GitHub page
18. Co Substrate Dynamics Models and Data
In a recent pre-print available at https://www.biorxiv.org/content/10.1101/2022.09.05.506656v1 we have developed and analysed metabolic reaction motifs featuring ‘cycling’ of co-substrates. Our analytical results shown that there is a flux limitation arising from such co-substrate dynamics, which we tried to gather evidence for by analysing measuring metabolic fluxes from Escherichia coli. The models, data analysis and simulation code associated with this study are available as a Zenodo repository at https://doi.org/10.5281/zenodo.7565439 while the active code is available on our Github page at https://github.com/OSS-Lab/CoSubstrateDynamics
17. ChemChaste
ChemChaste is a computational tool for the spatial simulation of multicellular and bulk biochemistry. It can simulate an arbitrary number of diffusing chemicals with spatially heterogeneous diffusion coefficients. It can simulate cells within a spatial domain and having their own intracellular reactions that can be linked to the environmental domain. ChemChaste uses a file-based interface to define the simulation / model parameters. Description of ChemChaste is currently available in a pre-print at https://doi.org/10.1101/2021.10.21.465304Link opens in a new window.
The ChemChaste1.0 code is available at our lab GithubLink opens in a new window page and on ZenodoLink opens in a new window.
16. FBAhv
This is a Phyton project containing scripts allowing to add a "virus biomass function" to a cell metabolic model (a SBML model), and then to perform an analysis of this "Host-Virus Model" (HVM). This code is used in the implementation of the mammalian lung cell-COVID19 modelling presented in "Delattre et al, 2021Link opens in a new window" and available on our GitHubLink opens in a new window page.
15. Micodymora
Micodymora is a python package allowing the simulation of Ordinary Differential Equations (ODE) models of microbial population dynamics, as presented in "Delattre et al, 2020". For further details and to access the package, please visit our GitHubLink opens in a new window page.
14. Source code for MetQy.
This is the R implementation of MetQy, a KEGG query tool, presented in "Martinez-Vernon et al, 2018Link opens in a new window". The following link is for the zipped file, which one can install within the R environment. See separate GitHub pageLink opens in a new window 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 "Aller et al, 2018Link opens in a new window". The links are for the actual model creation tool ViraNet and a few associated files: (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. 2018".
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, 2016Link opens in a new window". 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.
ZIP file
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, 2016Link opens in a new window". 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.
ZIP file
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, 2016Link opens in a new window". 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.
ZIP file
9. Source Code for BioJazz
BioJazzLink opens in a new window 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 source code for Tinker is in Java and 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.
ZIP fileLink opens in a new window
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 paperLink opens in a new window 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.
ZIP fileLink opens in a new window
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 AnalysesLink opens in a new window
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)".
Evo MovieLink opens in a new window
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.
ZIP fileLink opens in a new window
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.
ZIP fileLink opens in a new window
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.
HTML Links (ZIP file)
Experimental Resources - see also our protocols.io page
Below are a list of microorganisms we work routinely with (as of Jan 2018) and some experimental protocols and recipes we have developed over the years. The latter are coded using an internal coding system (that is irrelevant for wider users). As for the listed microorganisms, we are happy to provide samples to other research groups.
1. Microorganisms
Desulfovibrio vulgaris strain DSM644 (sourced from DSMZ)
Methanococcus maripaludis strain DSM2067 (sourced from DSMZ)
Methanosarcina barkeri strain DSM800 (sourced from DSMZ)
Escherichia coli K-12 strain DSM18039 (sourced from DSMZ)
Shewanella oneidensis strain MR1 (sourced from Rosser group, University of Edinburgh)
Roseobacter sp. AzwK-3b (sourced from Hansel group, Woods Hole Oceanographic Institution, Falmouth, USA)
2. Experimental Protocols (and Culture Media)
OSP01_01:
General protocol for preparing anaerobic mediaLink opens in a new window (to be used with any specific media composition)
OSM01_01:
D.vulgaris and M. maripaludis co-culture mediaLink opens in a new window (this media can also be used for tri-cultures of D.vulgaris, M. maripaludis, and M. barkeri)
OSM03_01:
Artificial sea water mediaLink opens in a new window (used for culturing AzwK-3b and S. oneidensis MR1)
OSP14_00:
General introductionLink opens in a new window to bio-electrochemical analysis and cell setup
Education & Outreach
3. Simple simulation of microbial chemotaxis
This is a simple simulation of microbial chemotaxis, coded in the visually oriented Processing language. The simulation includes four different 'types' of bacteria that all display tumble-and-run type swimming behaviour. Each type implements a different 'strategy' to control their tumbling behaviour. The simulation code introduces the effectiveness of two sense-response strategies, namely 'adaptive' and 'ultra sensitive'. The simulation can be readily expanded upon with more strategies, or in silico evolution of strategies! The current code is developed by Alex Darlington and Orkun S Soyer. Download the code and experiment with it! Note: For the code to work, one needs to place it in a folder with the same name as the code file.
Source code (PDE file)
2. Microbial Communities Research Explained
This is a nice, 3-minute video explaining our research on microbial communities in an accessible way. The video is directed by Conal Reid and acted/implemented by Henry, Tobias and Kalesh. Enjoy!
VideoLink opens in a new window
1. Gassing for Anaerobic Cultivation
In this video, Simone Zenobi and Henry Porter, explain the use of a gassing manifold and its use for anaerobic media preparation. The provided video consists of four parts describing the gassing manifold, the flushing of an anaerobic tube, establishing gas exchange in the head space, and finally pressurizing an anaerobic tube.
VideoLink opens in a new window
Review Excerpts
The peer-review process is not perfect and this is not always realised, especially by upcoming scientists and PhD students. Reviews can be horrible, great, unreasonable, off-the-point, encouraging, and this list goes on...the trick is not to dismiss reviews by saying "these guys clearly did not understand my paper" and not to be de-moralised thinking "if I'm getting such reviews, I would never succeed in science".
This is an experimental attempt to provide insight into the reviewing process, mainly as a training-experience for upcoming scientists. If you find this useful or have any other comments please get in touch. Do also get in touch if you would like to share excerpts from reviews you received.
In the following, some excerpts are slightly edited to make them more anonymous. Excerpts relating to the same paper are listed under the same number. Notes from me are given in italic. Enjoy...
4a. Overall, I think this study is a rare, first-principle contribution to microbial ecology and I am very enthusiastic about it.
4b. While the model does produce some interesting results, the results are not sufficient to warrant the author's conclusion. In particular;
4c. The ... model has been developed by others. NOTE: the work uses a generic model to apply it to a new question.
4d. Theory does nothing to explain the ...., so it has limited scope. NOTE: the work does not attempt to explain .....
3a. While the origin of ... is interesting and important, the results of this study are not novel, particularly in light of ...'s very similar findings.
3b. These findings add an important piece to the puzzle of ...
3c. I think the authors have satisfactorily addressed my comments.
3d. I am generally not convinced by the response.
2a. The paper is well written, but some parts are dense and the fact that the information is spread between the main text, the "Method" section and the supplementary information makes the understanding not always straightforward
2b. This is a modeling paper. The authors should provide the deterministic equations in a clear, human reading form in the main paper.
1a. The first paragraph of the Methods is somewhat redundant with the last paragraph of the Introduction.
1b. There are a limitless number of ways a ... can be modeled. The authors present one possible choice, which has some biological justification.
1c. The ... conclusions are relatively non-sensical
1d. I am very impressed by this manuscript and learned a lot from it...I was initially rather skeptical when I read the Title and Abstract. But the authors have convinced me. Their model seems realistic and it produces a solid prediction.